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US20120323594A1 - Prognostic and predictive gene signature for non-small cell lung cancer and adjuvant chemotherapy - Google Patents

Prognostic and predictive gene signature for non-small cell lung cancer and adjuvant chemotherapy Download PDF

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US20120323594A1
US20120323594A1 US13/471,915 US201213471915A US2012323594A1 US 20120323594 A1 US20120323594 A1 US 20120323594A1 US 201213471915 A US201213471915 A US 201213471915A US 2012323594 A1 US2012323594 A1 US 2012323594A1
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biomarker
gene
relative expression
subject
biomarkers
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Ming-Sound Tsao
Frances A. Shepherd
Igor Jurisica
Sandy D. Der
Chang-Qi Zhu
Dan Strumpf
Lesley Seymour
Keyue Ding
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University Health Network
Helomics Corp
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Publication of US20120323594A1 publication Critical patent/US20120323594A1/en
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    • C12Q1/00—Measuring or testing processes involving enzymes, nucleic acids or microorganisms; Compositions therefor; Processes of preparing such compositions
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    • G01N33/53—Immunoassay; Biospecific binding assay; Materials therefor
    • G01N33/574—Immunoassay; Biospecific binding assay; Materials therefor for cancer
    • G01N33/57407—Specifically defined cancers
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    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16B—BIOINFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR GENETIC OR PROTEIN-RELATED DATA PROCESSING IN COMPUTATIONAL MOLECULAR BIOLOGY
    • G16B25/00—ICT specially adapted for hybridisation; ICT specially adapted for gene or protein expression
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    • G16B25/00—ICT specially adapted for hybridisation; ICT specially adapted for gene or protein expression
    • G16B25/10—Gene or protein expression profiling; Expression-ratio estimation or normalisation
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16B—BIOINFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR GENETIC OR PROTEIN-RELATED DATA PROCESSING IN COMPUTATIONAL MOLECULAR BIOLOGY
    • G16B40/00—ICT specially adapted for biostatistics; ICT specially adapted for bioinformatics-related machine learning or data mining, e.g. knowledge discovery or pattern finding
    • G—PHYSICS
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    • G16B—BIOINFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR GENETIC OR PROTEIN-RELATED DATA PROCESSING IN COMPUTATIONAL MOLECULAR BIOLOGY
    • G16B40/00—ICT specially adapted for biostatistics; ICT specially adapted for bioinformatics-related machine learning or data mining, e.g. knowledge discovery or pattern finding
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    • G—PHYSICS
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    • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H50/00—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics
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    • C12Q2600/00—Oligonucleotides characterized by their use
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    • C12Q2600/00—Oligonucleotides characterized by their use
    • C12Q2600/112—Disease subtyping, staging or classification
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    • G01N2800/52—Predicting or monitoring the response to treatment, e.g. for selection of therapy based on assay results in personalised medicine; Prognosis

Definitions

  • the application relates to compositions and methods for prognosing and classifying non-small cell lung cancer and for determining the benefit of adjuvant chemotherapy.
  • NSCLC Non-small cell lung cancer
  • Tumor stage is the primary determinant for treatment selection for NSCLC patients.
  • Recent clinical trials have led to the adoption of adjuvant cisplatin-based chemotherapy in early stage NSCLC patients (Stages IB-IIIA).
  • the 5-year survival advantage conferred by adjuvant chemotherapy in recent trials are 4% in the International Adjuvant Lung Trial (IALT) involving 1,867 stage I-III patients 2 , 15% in the National Cancer Institute of Canada Clinical Trials Group (NCIC CTG) BR.10 Trial involving 483 stage IB-II patients 3 , and 9% in the Adjuvant Navelbine International Trialist Association (ANITA) trial involving 840 stage IB-IIIA patients 4 .
  • ANITA Adjuvant Navelbine International Trialist Association
  • Pre-planned stratification analysis in the later two trials showed no significant survival benefit for stage IB patients 3,4 .
  • LACE Lung Adjuvant Cisplatin Evaluation
  • Applicants have identified from historical patient data a minimal set of fifteen genes whose expression levels, either alone or in combination with that of one to 3 additional genes, is prognostic of survival outcome and diagnostic of adjuvant therapy benefit.
  • the fifteen genes are provided in Table 4.
  • Optional additional genes may be selected from those provided in Table 3.
  • the prognostic and diagnostic value of the gene sets identified by Applicants was verified by validation against independent data sets, as set forth in the Examples below.
  • the present disclosure provides methods and kits useful for obtaining and utilizing expression information for the fifteen, and optionally one to 3 additional genes, to obtain prognostic and diagnostic information for patient with NSCLC.
  • the methods of the present disclosure generally involve obtaining from a patient relative expression data, at the DNA, mRNA, or protein level, for each of the fifteen, and optional additional, genes, processing the data and comparing the resulting information to one or more reference values.
  • Relative expression levels are expression data normalized according to techniques known to those skilled in the art. Expression data may be normalized with respect to one or more genes with invariant expression, such as “housekeeping” genes. In some embodiments, expression data may be processed using standard techniques, such as transformation to a z-score, and/or software tools, such as RMAexpress v0.3.
  • a multi-gene signature for prognosing or classifying patients with lung cancer.
  • a fifteen-gene signature is provided, comprising reference values for each of fifteen different genes based on relative expression data for each gene from a historical data set with a known outcome, such as good or poor survival, and/or known treatment, such as adjuvant chemotherapy.
  • four reference values are provided for each of the fifteen genes listed in Table 4.
  • the reference values for each of the fifteen genes are principal component values set forth in Table 10.
  • a sixteen-, seventeen-, or eighteen-gene signature comprises reference values for each of sixteen, seventeen, or eighteen different genes based on relative expression data for each gene from a historical data set with a known outcome and/or known treatment.
  • reference values are provided for one, two, three genes in addition to those listed in Table 4, and the genes are selected from those listed in Table 3.
  • a single reference value for each gene is provided.
  • relative expression data from a patient are combined with the gene-specific reference values on a gene-by-gene basis for each of the fifteen, and optional additional, genes, to generate a test value which allows prognosis or therapy recommendation.
  • relative expression data are subjected to an algorithm that yields a single test value, or combined score, which is then compared to a control value obtained from the historical expression data for a patient or pool of patients.
  • the control value is a numerical threshold for predicting outcomes, for example good and poor outcome, or making therapy recommendations, for example adjuvant therapy in addition to surgical resection or surgical resection alone.
  • a test value or combined score greater than the control value is predictive, for example, of high risk (poor outcome) or benefit from adjuvant therapy, whereas a combined score falling below the control value is predictive, for example, of low risk (good outcome) or lack of benefit from adjuvant therapy.
  • the combined score is calculated from relative expression data multiplied by reference values, determined from historical data, for each gene. Accordingly, the combined score may be calculated using the algorithm of Formula I below:
  • PC1 is the sum of the relative expression level for each gene in a multi-gene signature multiplied by a first principal component for each gene in the multi-gene signature
  • PC2 is the sum of the relative expression level for each gene multiplied by a second principal component for each gene
  • PC3 is the sum of the relative expression level for each gene multiplied by a third principal component for each gene
  • PC4 is the sum of the relative expression level for each gene multiplied by a fourth principal component for each gene.
  • the combined score is referred to as a risk score.
  • a risk score for a subject can be calculated by applying Formula I to relative expression data from a test sample obtained from the subject.
  • PC1 is the sum of the relative expression level for each gene provided in Table 4 multiplied by a first principal component for each gene, respectively, as set forth in Table 10;
  • PC2 is the sum of the relative expression level for each gene provided in Table 4 multiplied by a second principal component for each gene, respectively, as set forth in Table 10;
  • PC3 is the sum of the relative expression level for each gene provided in Table 4 multiplied by a third principal component for each gene, respectively, as set forth in Table 10;
  • PC4 is the sum of the relative expression level for each gene provided in Table 4 multiplied by a fourth principal component for each gene, respectively, as set forth in Table 10.
  • the present inventors have identified a gene signature that is prognostic for survival as well as predictive for benefit from adjuvant chemotherapy.
  • the application provides a method of prognosing or classifying a subject with non-small cell lung cancer comprising the steps:
  • the application provides a method of predicting prognosis in a subject with non-small cell lung cancer comprising the steps:
  • the prognoses and classifying methods of the application can be used to select treatment.
  • the methods can be used to select or identify subjects who might benefit from adjuvant chemotherapy.
  • the application provides a method of selecting a therapy for a subject with NSCLC, comprising the steps:
  • the application provides a method of selecting a therapy for a subject with NSCLC, comprising the steps:
  • compositions useful for use with the methods described herein are also provided.
  • kits used to prognose or classify a subject with NSCLC into a good survival group or a poor survival group or for selecting therapy for a subject with NSCLC that includes detection agents that can detect the expression products of the biomarkers.
  • kits useful for carrying out the diagnostic and prognostic tests described herein generally comprise reagents and compositions for obtaining relative expression data for the fifteen, and optional additional, genes described in Tables 3 and 4. As will be recognized by the skilled artisans, the contents of the kits will depend upon the means used to obtain the relative expression information.
  • Kits may comprise a labeled compound or agent capable of detecting protein product(s) or nucleic acid sequence(s) in a sample and means for determining the amount of the protein or mRNA in the sample (e.g., an antibody which binds the protein or a fragment thereof, or an oligonucleotide probe which binds to DNA or mRNA encoding the protein). Kits can also include instructions for interpreting the results obtained using the kit.
  • a labeled compound or agent capable of detecting protein product(s) or nucleic acid sequence(s) in a sample and means for determining the amount of the protein or mRNA in the sample (e.g., an antibody which binds the protein or a fragment thereof, or an oligonucleotide probe which binds to DNA or mRNA encoding the protein).
  • Kits can also include instructions for interpreting the results obtained using the kit.
  • kits are oligonucleotide-based kits, which may comprise, for example: (1) an oligonucleotide, e.g., a detectably labeled oligonucleotide, which hybridizes to a nucleic acid sequence encoding a marker protein or (2) a pair of primers useful for amplifying a marker nucleic acid molecule.
  • Kits may also comprise, e.g., a buffering agent, a preservative, or a protein stabilizing agent.
  • the kits can further comprise components necessary for detecting the detectable label (e.g., an enzyme or a substrate).
  • the kits can also contain a control sample or a series of control samples which can be assayed and compared to the test sample.
  • Each component of a kit can be enclosed within an individual container and all of the various containers can be within a single package, along with instructions for interpreting the results of the assays performed using the kit.
  • kits are antibody-based kits, which may comprise, for example: (1) a first antibody (e.g., attached to a solid support) which binds to a marker protein; and, optionally, (2) a second, different antibody which binds to either the protein or the first antibody and is conjugated to a detectable label.
  • a first antibody e.g., attached to a solid support
  • a second, different antibody which binds to either the protein or the first antibody and is conjugated to a detectable label.
  • a further aspect provides computer implemented products, computer readable mediums and computer systems that are useful for the methods described herein.
  • FIG. 1 shows the derivation and testing of the prognostic signature.
  • FIG. 2 shows the survival outcome based on the 15-gene signature in training and test sets.
  • FIG. 3 shows a comparison of chemotherapy vs. observation in low and high risk patients with microarray data.
  • FIG. 4 shows a consort diagram for microarray study of BR. 10 patients.
  • FIG. 5 shows the effect of adjuvant chemotherapy in microarray profiled patients.
  • FIG. 6 shows the effect of microarray batch processing at 2 different times.
  • the samples were profiled in 2 batches at 2 times (January 2004 and June 2005).
  • Unsupervised clustering shows that the expression patterns of these two batches differed significantly with samples arrayed on January 2004 aggregated in cluster 1 (93%) and samples arrayed on June 2005 in cluster 2 (73%).
  • the application relates to 15 biomarkers that form a 15-gene signature, and provides methods, compositions, computer implemented products, detection agents and kits for prognosing or classifying a subject with non-small cell lung cancer (NSCLC) and for determining the benefit of adjuvant chemotherapy.
  • NSCLC non-small cell lung cancer
  • biomarker refers to a gene that is differentially expressed in individuals with non-small cell lung cancer (NSCLC) according to prognosis and is predictive of different survival outcomes and of the benefit of adjuvant chemotherapy.
  • NSCLC non-small cell lung cancer
  • a 15-gene signature comprises 15 biomarker genes listed in Table 4.
  • additional biomarkers for a 16-, 17-, or 18-gene signature may be selected from the genes listed in Table 3.
  • one aspect of the invention is a method of prognosing or classifying a subject with non-small cell lung cancer, comprising the steps:
  • the application provides a method of predicting prognosis in a subject with non-small cell lung cancer (NSCLC) comprising the steps:
  • the term “reference expression profile” as used herein refers to the expression of the 15 biomarkers or genes listed in Table 4 associated with a clinical outcome in a NSCLC patient.
  • the reference expression profile comprises 15 values, each value representing the expression level of a biomarker, wherein each biomarker corresponds to one gene in Table 4.
  • the reference expression profile is identified using one or more samples comprising tumor wherein the expression is similar between related samples defining an outcome class or group such as poor survival or good survival and is different to unrelated samples defining a different outcome class such that the reference expression profile is associated with a particular clinical outcome.
  • the reference expression profile is accordingly a reference profile of the expression of the 15 genes in Table 4, to which the subject expression levels of the corresponding genes in a patient sample are compared in methods for determining or predicting clinical outcome.
  • control refers to a specific value or dataset that can be used to prognose or classify the value e.g expression level or reference expression profile obtained from the test sample associated with an outcome class.
  • a dataset may be obtained from samples from a group of subjects known to have NSCLC and good survival outcome or known to have NSCLC and have poor survival outcome or known to have NSCLC and have benefited from adjuvant chemotherapy or known to have NSCLC and not have benefited from adjuvant chemotherapy.
  • the expression data of the biomarkers in the dataset can be used to create a “control value” that is used in testing samples from new patients.
  • a control value is obtained from the historical expression data for a patient or pool of patients with a known outcome.
  • the control value is a numerical threshold for predicting outcomes, for example good and poor outcome, or making therapy recommendations, for example adjuvant therapy in addition to surgical resection or surgical resection alone.
  • the “control” is a predetermined value for the set of 15 biomarkers obtained from NSCLC patients whose biomarker expression values and survival times are known.
  • the “control” is a predetermined reference profile for the set of fifteen biomarkers obtained from NSCLC patients whose survival times are known. Using values from known samples allows one to develop an algorithm for classifying new patient samples into good and poor survival groups as described in the Example.
  • control is a sample from a subject known to have NSCLC and good survival outcome.
  • control is a sample from a subject known to have NSCLC and poor survival outcome.
  • the comparison between the expression of the biomarkers in the test sample and the expression of the biomarkers in the control will depend on the control used. For example, if the control is from a subject known to have NSCLC and poor survival, and there is a difference in expression of the biomarkers between the control and test sample, then the subject can be prognosed or classified in a good survival group. If the control is from a subject known to have NSCLC and good survival, and there is a difference in expression of the biomarkers between the control and test sample, then the subject can be prognosed or classified in a poor survival group.
  • control is from a subject known to have NSCLC and good survival, and there is a similarity in expression of the biomarkers between the control and test sample, then the subject can be prognosed or classified in a good survival group.
  • control is from a subject known to have NSCLC and poor survival, and there is a similarity in expression of the biomarkers between the control and test sample, then the subject can be prognosed or classified in a poor survival group.
  • a “reference value” refers to a gene-specific coefficient derived from historical expression data.
  • the multi-gene signatures of the present disclosure comprise gene-specific reference values.
  • the multi-gene signature comprises one reference value for each gene in the signature.
  • the multi-gene signature comprises four reference values for each gene in the signature.
  • the reference values are the first four components derived from principal component analysis for each gene in the signature.
  • the term “differentially expressed” or “differential expression” as used herein refers to a difference in the level of expression of the biomarkers that can be assayed by measuring the level of expression of the products of the biomarkers, such as the difference in level of messenger RNA transcript expressed or proteins expressed of the biomarkers. In a preferred embodiment, the difference is statistically significant.
  • the term “difference in the level of expression” refers to an increase or decrease in the measurable expression level of a given biomarker as measured by the amount of messenger RNA transcript and/or the amount of protein in a sample as compared with the measurable expression level of a given biomarker in a control.
  • the differential expression can be compared using the ratio of the level of expression of a given biomarker or biomarkers as compared with the expression level of the given biomarker or biomarkers of a control, wherein the ratio is not equal to 1.0.
  • an RNA or protein is differentially expressed if the ratio of the level of expression in a first sample as compared with a second sample is greater than or less than 1.0.
  • the differential expression is measured using p-value.
  • a biomarker when using p-value, is identified as being differentially expressed as between a first sample and a second sample when the p-value is less than 0.1, preferably less than 0.05, more preferably less than 0.01, even more preferably less than 0.005, the most preferably less than 0.001.
  • similarity in expression means that there is no or little difference in the level of expression of the biomarkers between the test sample and the control or reference profile. For example, similarity can refer to a fold difference compared to a control. In a preferred embodiment, there is no statistically significant difference in the level of expression of the biomarkers.
  • most similar in the context of a reference profile refers to a reference profile that is associated with a clinical outcome that shows the greatest number of identities and/or degree of changes with the subject profile.
  • prognosis refers to a clinical outcome group such as a poor survival group or a good survival group associated with a disease subtype which is reflected by a reference profile such as a biomarker reference expression profile or reflected by an expression level of the fifteen biomarkers disclosed herein.
  • the prognosis provides an indication of disease progression and includes an indication of likelihood of death due to lung cancer.
  • the clinical outcome class includes a good survival group and a poor survival group.
  • prognosing or classifying means predicting or identifying the clinical outcome group that a subject belongs to according to the subject's similarity to a reference profile or biomarker expression level associated with the prognosis.
  • prognosing or classifying comprises a method or process of determining whether an individual with NSCLC has a good or poor survival outcome, or grouping an individual with NSCLC into a good survival group or a poor survival group.
  • good survival refers to an increased chance of survival as compared to patients in the “poor survival” group.
  • biomarkers of the application can prognose or classify patients into a “good survival group”. These patients are at a lower risk of death after surgery.
  • pool survival refers to an increased risk of death as compared to patients in the “good survival” group.
  • biomarkers or genes of the application can prognose or classify patients into a “poor survival group”. These patients are at greater risk of death from surgery.
  • the biomarker reference expression profile comprises a poor survival group. In another embodiment, the biomarker reference expression profile comprises a good survival group.
  • subject refers to any member of the animal kingdom, preferably a human being that has NSCLC or that is suspected of having NSCLC.
  • stage I includes cancer in the lung, but has not spread to adjacent lymph nodes or outside the chest.
  • Stage I is divided into two categories based on the size of the tumor (IA and IB).
  • Stage II includes cancer located in the lung and proximal lymph nodes.
  • Stage II is divided into 2 categories based on the size of tumor and nodal status (IIA and IIB).
  • Stage III includes cancer located in the lung and the lymph nodes.
  • Stage III is divided into 2 categories based on the size of tumor and nodal status (IIIA and IIIB).
  • Stage IV includes cancer that has metastasized to distant locations.
  • the term “early stage NSCLC” includes patients with Stage I to IIIA NSCLC. These patients are treated primarily by complete surgical resection.
  • a multi-gene signature is prognostic of patient outcome and/or response to adjuvant chemotherapy.
  • a minimal signature for 15 genes is provided.
  • the signature comprises reference values for each of the 15 genes listed in Table 4.
  • the 15-gene signature is associated with the early stages of NSCLC. Accordingly, in one embodiment, the subject has stage I NSCLC. In another embodiment, the subject has stage II NSCLC.
  • a 16-, 17-, 18-gene signature is prognostic of patient outcome and/or response to adjuvant chemotherapy.
  • the signature comprises reference values for one, two or three genes selected from those listed in Table 3, in addition to reference values for each of the genes listed in Table 4.
  • the additional one, two, or three genes are selected from RGS4, UGT2B4, and MCF2 listed in Table 3.
  • the multi-gene signature comprises four coefficients, or reference values, for each gene in the signature.
  • the four coefficients are the first four principal components derived from principal component analysis described in Example 1 below.
  • the 15-gene signature comprises the principal component values listed in Table 10 below.
  • a 16-, 17-, 18-gene signature comprises coefficients for a sixteenth, seventeenth, and eighteenth gene, respectively, derived from principal component analysis as described in Example 1 below.
  • the coefficients for a sixteenth, seventeenth, and eighteenth gene, respectively are the first four principal components derived according to Example 1.
  • the additional one, two, or three genes are selected from RGS4, UGT2B4, and MCF2 listed in Table 3.
  • test sample refers to any cancer-affected fluid, cell or tissue sample from a subject which can be assayed for biomarker expression products and/or a reference expression profile, e.g. genes differentially expressed in subjects with NSCLC according to survival outcome.
  • RNA includes mRNA transcripts, and/or specific spliced variants of mRNA.
  • RNA product of the biomarker refers to RNA transcripts transcribed from the biomarkers and/or specific spliced variants.
  • protein it refers to proteins translated from the RNA transcripts transcribed from the biomarkers.
  • protein product of the biomarker or “biomarker protein” refers to proteins translated from RNA products of the biomarkers.
  • RNA products of the biomarkers within a sample, including arrays, such as microarrays, RT-PCR (including quantitative PCR), nuclease protection assays and Northern blot analyses.
  • arrays such as microarrays, RT-PCR (including quantitative PCR), nuclease protection assays and Northern blot analyses.
  • Any analytical procedure capable of permitting specific and quantifiable (or semi-quantifiable) detection of the and, optionally, additional biomarkers may be used in the methods herein presented, such as the microarray methods set forth herein, and methods known to those skilled in the art.
  • the biomarker expression levels are determined using arrays, optionally microarrays, RT-PCR, optionally quantitative RT-PCR, nuclease protection assays or Northern blot analyses.
  • RNA either total RNA or poly A RNA
  • Labeling is usually performed during reverse transcription by incorporating a labeled nucleotide in the reaction mixture.
  • a microarray is then hybridized with labeled RNA, and relative expression levels calculated based on the relative concentrations of cDNA molecules that hybridized to the cDNAs represented on the microarray.
  • Microarray analysis can be performed by commercially available equipment, following manufactuer's protocols, such as by using Affymetrix GeneChip technology, Agilent Technologies cDNA microarrays, Illumina Whole-Genome DASL array assays, or any other comparable microarray technology.
  • probes capable of hybridizing to one or more biomarker RNAs or cDNAs are attached to the substrate at a defined location (“addressable array”). Probes can be attached to the substrate in a wide variety of ways, as will be appreciated by those in the art. In some embodiments, the probes are synthesized first and subsequently attached to the substrate. In other embodiments, the probes are synthesized on the substrate. In some embodiments, probes are synthesized on the substrate surface using techniques such as photopolymerization and photolithography.
  • microarrays are utilized in a RNA-primed, Array-based Klenow Enzyme (“RAKE”) assay.
  • RAKE RNA-primed, Array-based Klenow Enzyme
  • the DNA probes comprise a base sequence that is complementary to a target RNA of interest, such as one or more biomarker RNAs capable of specifically hybridizing to a nucleic acid comprising a sequence that is identically present in one of the genes listed in Table 4 under standard hybridization conditions.
  • a target RNA of interest such as one or more biomarker RNAs capable of specifically hybridizing to a nucleic acid comprising a sequence that is identically present in one of the genes listed in Table 4 under standard hybridization conditions.
  • the addressable array comprises DNA probes for no more than the 15 genes listed in Table 4. In some embodiments, the addressable array comprises DNA probes for each of the 15 genes listed in Table 4 and optionally, no more than one, two, or three additional genes selected from those listed in Table 3. In one embodiment, the addressable array comprises DNA probes for each of the 15 genes listed in Table 4 and DNA probes for one, two, or all three of RGS4, UGT2B4, and MCF2 listed in Table 3.
  • the addressable array comprises DNA probes for each of the 15 genes listed in Table 4 and, optionally, one, two, three, or four housekeeping genes. In one embodiment, the addressable array comprises DNA probes for each of the 15 genes listed in Table 4, one, two, three, or four housekeeping genes, and, additionally, no more than one, two, three or four additional genes selected from those listed in Table 3.
  • expression data are pre-processed to correct for variations in sample preparation or other non-experimental variables affecting expression measurements.
  • background adjustment, quantile adjustment, and summarization may be performed on microarray data, using standard software programs such as RMAexpress v0.3, followed by centering of the data to the mean and scaling to the standard deviation.
  • the sample After the sample is hybridized to the array, it is exposed to exonuclease I to digest any unhybridized probes.
  • the Klenow fragment of DNA polymerase I is then applied along with biotinylated dATP, allowing the hybridized biomarker RNAs to act as primers for the enzyme with the DNA probe as template.
  • the slide is then washed and a streptavidin-conjugated fluorophore is applied to detect and quantitate the spots on the array containing hybridized and Klenow-extended biomarker RNAs from the sample.
  • the RNA sample is reverse transcribed using a biotin/poly-dA random octamer primer.
  • the RNA template is digested and the biotin-containing cDNA is hybridized to an addressable microarray with bound probes that permit specific detection of biomarker RNAs.
  • the microarray includes at least one probe comprising at least 8, at least 9, at least 10, at least 11, at least 12, at least 13, at least 14, at least 15, at least 16, at least 17, at least 18, at least 19, even at least 20, 21, 22, 23, or 24 contiguous nucleotides identically present in each of the genes listed in Table 4.
  • the microarray After hybridization of the cDNA to the microarray, the microarray is exposed to a streptavidin-bound detectable marker, such as a fluorescent dye, and the bound cDNA is detected.
  • a streptavidin-bound detectable marker such as a fluorescent dye
  • the array is a U133A chip from Affymetrix.
  • a plurality of nucleic acid probes that are complementary or hybridizable to an expression product of the genes listed in Table 4 are used on the array.
  • the probe target sequences are listed in Table 9.
  • the probe target sequences are selected from SEQ ID NO: 3, 11-15, 22, 26, 35, 49, 78, 85, 130, 133, and 169.
  • fifteen probes are used, each probe hybridizable to a different target sequence selected from SEQ ID NO: 3, 11-15, 22, 26, 35, 49, 78, 85, 130, 133, and 169.
  • a plurality of nucleic acid probes that are complementary or hybridizable to an expression product of some or all the genes listed in Table 3 are used on the array.
  • the probe target sequences are selected from those listed in Table 11.
  • the probe target sequences are selected from SEQ ID NO:1-172.
  • nucleic acid includes DNA and RNA and can be either double stranded or single stranded.
  • hybridize or “hybridizable” refers to the sequence specific non-covalent binding interaction with a complementary nucleic acid.
  • the hybridization is under high stringency conditions. Appropriate stringency conditions which promote hybridization are known to those skilled in the art, or can be found in Current Protocols in Molecular Biology, John Wiley & Sons, N.Y. (1989), 6.3.1 6.3.6. For example, 6.0 ⁇ sodium chloride/sodium citrate (SSC) at about 45° C., followed by a wash of 2.0 ⁇ SSC at 50° C. may be employed.
  • SSC sodium chloride/sodium citrate
  • probe refers to a nucleic acid sequence that will hybridize to a nucleic acid target sequence.
  • the probe hybridizes to an RNA product of the biomarker or a nucleic acid sequence complementary thereof.
  • the length of probe depends on the hybridization conditions and the sequences of the probe and nucleic acid target sequence. In one embodiment, the probe is at least 8, 10, 15, 20, 25, 50, 75, 100, 150, 200, 250, 400, 500 or more nucleotides in length.
  • compositions that comprise at least one biomarker or target RNA-specific probe.
  • target RNA-specific probe encompasses probes that have a region of contiguous nucleotides having a sequence that is either (i) identically present in one of the genes listed in Tables 3 or 4, or (ii) complementary to the sequence of a region of contiguous nucleotides found in one of the genes listed in Tables 3 or 4, where “region” can comprise the full length sequence of any one of the genes listed in Tables 3 or 4, a complementary sequence of the full length sequence of any one of the genes listed in Tables 3 or 4, or a subsequence thereof.
  • target RNA-specific probes consist of deoxyribonucleotides. In other embodiments, target RNA-specific probes consist of both deoxyribonucleotides and nucleotide analogs. In some embodiments, biomarker RNA-specific probes comprise at least one nucleotide analog which increases the hybridization binding energy. In some embodiments, a target RNA-specific probe in the compositions described herein binds to one biomarker RNA in the sample.
  • more than one probe specific for a single biomarker RNA is present in the compositions, the probes capable of binding to overlapping or spatially separated regions of the biomarker RNA.
  • the compositions described herein are designed to hybridize to cDNAs reverse transcribed from biomarker RNAs
  • the composition comprises at least one target RNA-specific probe comprising a sequence that is identically present in a biomarker RNA (or a subsequence thereof).
  • a biomarker RNA is capable of specifically hybridizing to at least one probe comprising a base sequence that is identically present in one of the genes listed in Table 4. In some embodiments, a biomarker RNA is capable of specifically hybridizing to at least one nucleic acid probe comprising a sequence that is identically present in one of the genes listed in Table 3. In some embodiments, a target RNA is capable of specifically hybridizing to at least one nucleic acid probe, and comprises a sequence that is identical to a sequence selected from SEQ ID NO:1-172, or a sequence listed in Table 11. In some embodiments, a target RNA is capable of specifically hybridizing to at least one nucleic acid probe, and comprises a sequence that is identical to a sequence listed in Table 9.
  • a target RNA is capable of specifically hybridizing to at least one nucleic acid probe, and comprises a sequence that is identical to a sequence selected from SEQ ID NO: 3, 11-15, 22, 26, 35, 49, 78, 85, 130, 133, and 169.
  • a biomarker RNA is capable of specifically hybridizing to at least one probe comprising a base sequence that is identically present in one of the genes listed in Table 4.
  • the composition comprises a plurality of target or biomarker RNA-specific probes each comprising a region of contiguous nucleotides comprising a base sequence that is identically present in one or more of the genes listed in Table 4, or in a subsequence thereof. In some embodiments, the composition comprises a plurality of target or biomarker RNA-specific probes each comprising a region of contiguous nucleotides comprising a base sequence that is complementary to a sequence listed in Table 9.
  • the composition comprises a plurality of target RNA-specific probes each comprising a region of contiguous nucleotides comprising a base sequence that is complementary to a sequence selected from SEQ ID NO: 3, 11-15, 22, 26, 35, 49, 78, 85, 130, 133, and 169.
  • the terms “complementary” or “partially complementary” to a biomarker or target RNA (or target region thereof), and the percentage of “complementarity” of the probe sequence to that of the biomarker RNA sequence is the percentage “identity” to the reverse complement of the sequence of the biomarker RNA.
  • the degree of “complementarity” is expressed as the percentage identity between the sequence of the probe (or region thereof) and the reverse complement of the sequence of the biomarker RNA that best aligns therewith. The percentage is calculated by counting the number of aligned bases that are identical as between the 2 sequences, dividing by the total number of contiguous nucleotides in the probe, and multiplying by 100.
  • the microarray comprises probes comprising a region with a base sequence that is fully complementary to a target region of a biomarker RNA. In other embodiments, the microarray comprises probes comprising a region with a base sequence that comprises one or more base mismatches when compared to the sequence of the best-aligned target region of a biomarker RNA.
  • a “region” of a probe or biomarker RNA may comprise or consist of 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29 or more contiguous nucleotides from a particular gene or a complementary sequence thereof.
  • the region is of the same length as the probe or the biomarker RNA. In other embodiments, the region is shorter than the length of the probe or the biomarker RNA.
  • the microarray comprises fifteen probes each comprising a region of at least 10 contiguous nucleotides, such as at least 11 contiguous nucleotides, such as at least 13 contiguous nucleotides, such as at least 14 contiguous nucleotides, such as at least 15 contiguous nucleotides, such as at least 16 contiguous nucleotides, such as at least 17 contiguous nucleotides, such as at least 18 contiguous nucleotides, such as at least 19 contiguous nucleotides, such as at least 20 contiguous nucleotides, such as at least 21 contiguous nucleotides, such as at least 22 contiguous nucleotides, such as at least 23 contiguous nucleotides, such as at least 24 contiguous nucleotides, such as at least 25 contiguous nucleotides with a base sequence that is identically present in one of the genes listed in Table 4.
  • the microarray component comprises fifteen probes each comprising a region with a base sequence that is identically present in each of the genes listed in Table 4.
  • the microarray comprises sixteen, seventeen, eighteen probes, each of which comprises a region with a base sequence that is identically present in each of the genes listed in Table 4 and, optionally, one, two, or three of the genes listed in Table 3.
  • the one, two, or three genes from Table 3 are selected from RGS4, UGT2B4, and MCF2.
  • the biomarker expression levels are determined by using quantitative RT-PCR.
  • RT-PCR is one of the most sensitive, flexible, and quantitative methods for measuring expression levels.
  • the first step is the isolation of mRNA from a target sample.
  • the starting material is typically total RNA isolated from human tumors or tumor cell lines.
  • General methods for mRNA extraction are well known in the art and are disclosed in standard textbooks of molecular biology, including Ausubel et al., Current Protocols of Molecular Biology, John Wiley and Sons (1997). Methods for RNA extraction from paraffin embedded tissues are disclosed, for example, in Rupp and Locker, Lab Invest. 56:A67 (1987), and De Andres et al., BioTechniques 18:42044 (1995).
  • RNA isolation can be performed using purification kit, buffer set and protease from commercial manufacturers, such as Qiagen, according to the manufacturer's instructions.
  • Qiagen a commercial manufacturer
  • total RNA from cells in culture can be isolated using Qiagen RNeasy mini-columns. Numerous RNA isolation kits are commercially available.
  • the primers used for quantitative RT-PCR comprise a forward and reverse primer for each gene listed in Table 4.
  • the primers used for quantitative RT-PCR are listed in Table 7.
  • primers comprising sequences identical to the sequences of SEQ ID NO: 173-202 are used for quantitative RT-PCR, wherein primers with sequences identifical to SEQ ID NO:173-187 are forward primers and primers with sequences identifical to SEQ ID NO:188-202 are reverse primers.
  • the analytical method used for detecting at least one biomarker RNA in the methods set forth herein includes real-time quantitative RT-PCR. See Chen, C. et al. (2005) Nucl. Acids Res. 33:e179, which is incorporated herein by reference in its entirety.
  • PCR can use a variety of thermostable DNA-dependent DNA polymerases, it typically employs the Taq DNA polymerase, which has a 5′-3′ nuclease activity but lacks a 3′-5′ proofreading endonuclease activity.
  • RT-PCR is done using a TaqMan® assay sold by Applied Biosystems, Inc. In a first step, total RNA is isolated from the sample.
  • the assay can be used to analyze about 10 ng of total RNA input sample, such as about 9 ng of input sample, such as about 8 ng of input sample, such as about 7 ng of input sample, such as about 6 ng of input sample, such as about 5 ng of input sample, such as about 4 ng of input sample, such as about 3 ng of input sample, such as about 2 ng of input sample, and even as little as about 1 ng of input sample containing RNA.
  • the TaqMan® assay utilizes a stem-loop primer that is specifically complementary to the 3′-end of a biomarker RNA.
  • the step of hybridizing the stem-loop primer to the biomarker RNA is followed by reverse transcription of the biomarker RNA template, resulting in extension of the 3′ end of the primer.
  • the result of the reverse transcription step is a chimeric (DNA) amplicon with the step-loop primer sequence at the 5′ end of the amplicon and the cDNA of the biomarker RNA at the 3′ end.
  • Quantitation of the biomarker RNA is achieved by RT-PCR using a universal reverse primer comprising a sequence that is complementary to a sequence at the 5′ end of all stem-loop biomarker RNA primers, a biomarker RNA-specific forward primer, and a biomarker RNA sequence-specific TaqMan® probe.
  • the assay uses fluorescence resonance energy transfer (“FRET”) to detect and quantitate the synthesized PCR product.
  • the TaqMan® probe comprises a fluorescent dye molecule coupled to the 5′-end and a quencher molecule coupled to the 3′-end, such that the dye and the quencher are in close proximity, allowing the quencher to suppress the fluorescence signal of the dye via FRET.
  • FRET fluorescence resonance energy transfer
  • quantitation of the results of RT-PCR assays is done by constructing a standard curve from a nucleic acid of known concentration and then extrapolating quantitative information for biomarker RNAs of unknown concentration.
  • the nucleic acid used for generating a standard curve is an RNA of known concentration.
  • the nucleic acid used for generating a standard curve is a purified double-stranded plasmid DNA or a single-stranded DNA generated in vitro.
  • C t cycle threshold, e.g., the number of PCR cycles required for the fluorescence signal to rise above background
  • C t values are inversely proportional to the amount of nucleic acid target in a sample.
  • C t values of the target RNA of interest can be compared with a control or calibrator, such as RNA from normal tissue.
  • the C t values of the calibrator and the target RNA samples of interest are normalized to an appropriate endogenous housekeeping gene (see above).
  • RT-PCR chemistries useful for detecting and quantitating PCR products in the methods presented herein include, but are not limited to, Molecular Beacons, Scorpion probes and SYBR Green detection.
  • Molecular Beacons can be used to detect and quantitate PCR products. Like TaqMan® probes, Molecular Beacons use FRET to detect and quantitate a PCR product via a probe comprising a fluorescent dye and a quencher attached at the ends of the probe. Unlike TaqMan® probes, Molecular Beacons remain intact during the PCR cycles. Molecular Beacon probes form a stem-loop structure when free in solution, thereby allowing the dye and quencher to be in close enough proximity to cause fluorescence quenching. When the Molecular Beacon hybridizes to a target, the stem-loop structure is abolished so that the dye and the quencher become separated in space and the dye fluoresces. Molecular Beacons are available, e.g., from Gene LinkTM (see http://www.genelink.com/newsite/products/mbintro.asp).
  • Scorpion probes can be used as both sequence-specific primers and for PCR product detection and quantitation. Like Molecular Beacons, Scorpion probes form a stem-loop structure when not hybridized to a target nucleic acid. However, unlike Molecular Beacons, a Scorpion probe achieves both sequence-specific priming and PCR product detection. A fluorescent dye molecule is attached to the 5′-end of the Scorpion probe, and a quencher is attached to the 3′-end. The 3′ portion of the probe is complementary to the extension product of the PCR primer, and this complementary portion is linked to the 5′-end of the probe by a non-amplifiable moiety.
  • Scorpion probes are available from, e.g, Premier Biosoft International (see http://www.premierbiosoft.com/tech_notes/Scorpion.html).
  • RT-PCR detection is performed specifically to detect and quantify the expression of a single biomarker RNA.
  • the biomarker RNA in typical embodiments, is selected from a biomarker RNA capable of specifically hybridizing to a nucleic acid comprising a sequence that is identically present in one of the genes set forth in Table 4. In some embodiments, the biomarker RNA specifically hybridizes to a nucleic acid comprising a sequence that is identically present in at least one of the genes in Table 3.
  • RT-PCR detection is utilized to detect, in a single multiplex reaction, each of 15, each of 16, each of 17, even each of 18 biomarker RNAs.
  • the biomarker RNAs in some embodiments, are capable of specifically hybridizing to a nucleic acid comprising a sequence that is identically present in one of the fifteen genes listed in Table 4 and optionally one, two, or three additional genes listed in Table 3.
  • a plurality of probes such as TaqMan probes, each specific for a different RNA target, is used.
  • each target RNA-specific probe is spectrally distinguishable from the other probes used in the same multiplex reaction.
  • quantitation of RT-PCR products is accomplished using a dye that binds to double-stranded DNA products, such as SYBR Green.
  • the assay is the QuantiTect SYBR Green PCR assay from Qiagen.
  • total RNA is first isolated from a sample.
  • Total RNA is subsequently poly-adenylated at the 3′-end and reverse transcribed using a universal primer with poly-dT at the 5′-end.
  • a single reverse transcription reaction is sufficient to assay multiple biomarker RNAs.
  • RT-PCR is then accomplished using biomarker RNA-specific primers and an miScript Universal Primer, which comprises a poly-dT sequence at the 5′-end.
  • SYBR Green dye binds non-specifically to double-stranded DNA and upon excitation, emits light.
  • buffer conditions that promote highly-specific annealing of primers to the PCR template e.g., available in the QuantiTect SYBR Green PCR Kit from Qiagen
  • the signal from SYBR green increases, allowing quantitation of specific products.
  • RT-PCR is performed using any RT-PCR instrumentation available in the art.
  • instrumentation used in real-time RT-PCR data collection and analysis comprises a thermal cycler, optics for fluorescence excitation and emission collection, and optionally a computer and data acquisition and analysis software.
  • the method of detectably quantifying one or more biomarker RNAs includes the steps of: (a) isolating total RNA; (b) reverse transcribing a biomarker RNA to produce a cDNA that is complementary to the biomarker RNA; (c) amplifying the cDNA from step (b); and (d) detecting the amount of a biomarker RNA with RT-PCR.
  • the RT-PCR detection is performed using a FRET probe, which includes, but is not limited to, a TaqMan® probe, a Molecular beacon probe and a Scorpion probe.
  • a FRET probe which includes, but is not limited to, a TaqMan® probe, a Molecular beacon probe and a Scorpion probe.
  • the RT-PCR detection and quantification is performed with a TaqMan® probe, i.e., a linear probe that typically has a fluorescent dye covalently bound at one end of the DNA and a quencher molecule covalently bound at the other end of the DNA.
  • the FRET probe comprises a base sequence that is complementary to a region of the cDNA such that, when the FRET probe is hybridized to the cDNA, the dye fluorescence is quenched, and when the probe is digested during amplification of the cDNA, the dye is released from the probe and produces a fluorescence signal.
  • the amount of biomarker RNA in the sample is proportional to the amount of fluorescence measured during cDNA amplification.
  • the TaqMan® probe typically comprises a region of contiguous nucleotides comprising a base sequence that is complementary to a region of a biomarker RNA or its complementary cDNA that is reverse transcribed from the biomarker RNA template (i.e., the sequence of the probe region is complementary to or identically present in the biomarker RNA to be detected) such that the probe is specifically hybridizable to the resulting PCR amplicon.
  • the probe comprises a region of at least 6 contiguous nucleotides having a base sequence that is fully complementary to or identically present in a region of a cDNA that has been reverse transcribed from a biomarker RNA template, such as comprising a region of at least 8 contiguous nucleotides, or comprising a region of at least 10 contiguous nucleotides, or comprising a region of at least 12 contiguous nucleotides, or comprising a region of at least 14 contiguous nucleotides, or even comprising a region of at least 16 contiguous nucleotides having a base sequence that is complementary to or identically present in a region of a cDNA reverse transcribed from a biomarker RNA to be detected.
  • the region of the cDNA that has a sequence that is complementary to the TaqMan® probe sequence is at or near the center of the cDNA molecule.
  • all biomarker RNAs are detected in a single multiplex reaction.
  • each TaqMan® probe that is targeted to a unique cDNA is spectrally distinguishable when released from the probe.
  • each biomarker RNA is detected by a unique fluorescence signal.
  • expression levels may be represented by gene transcript numbers per nanogram of cDNA.
  • RT-PCR data can be subjected to standardization and normalization against one or more housekeeping genes as has been previously described. See e.g., Rubie et al., Mol. Cell. Probes 19(2):101-9 (2005).
  • Appropriate genes for normalization in the methods described herein include those as to which the quantity of the product does not vary between between different cell types, cell lines or under different growth and sample preparation conditions.
  • endogenous housekeeping genes useful as normalization controls in the methods described herein include, but are not limited to, ACTB, BAT1, B2M, TBP, U6 snRNA, RNU44, RNU 48, and U47.
  • the at least one endogenous housekeeping gene for use in normalizing the measured quantity of RNA is selected from ACTB, BAT1, B2M, TBP, U6 snRNA, U6 snRNA, RNU44, RNU 48, and U47.
  • normalization to the geometric mean of two, three, four or more housekeeping genes is performed.
  • one housekeeping gene is used for normalization.
  • two, three, four or more housekeeping genes are used for normalization.
  • labels that can be used on the FRET probes include colorimetric and fluorescent labels such as Alexa Fluor dyes, BODIPY dyes, such as BODIPY FL; Cascade Blue; Cascade Yellow; coumarin and its derivatives, such as 7-amino-4-methylcoumarin, aminocoumarin and hydroxycoumarin; cyanine dyes, such as Cy3 and Cy5; eosins and erythrosins; fluorescein and its derivatives, such as fluorescein isothiocyanate; macrocyclic chelates of lanthanide ions, such as Quantum DyeTM; Marina Blue; Oregon Green; rhodamine dyes, such as rhodamine red, tetramethylrhodamine and rhodamine 6G; Texas Red; fluorescent energy transfer dyes, such as thiazole orange-ethidium heterodimer; and, TOTAB.
  • Alexa Fluor dyes such as Alexa Fluor dyes, BODIPY dyes,
  • dyes include, but are not limited to, those identified above and the following: Alexa Fluor 350, Alexa Fluor 405, Alexa Fluor 430, Alexa Fluor 488, Alexa Fluor 500. Alexa Fluor 514, Alexa Fluor 532, Alexa Fluor 546, Alexa Fluor 555, Alexa Fluor 568, Alexa Fluor 594, Alexa Fluor 610, Alexa Fluor 633, Alexa Fluor 647, Alexa Fluor 660, Alexa Fluor 680, Alexa Fluor 700, and, Alexa Fluor 750; amine-reactive BODIPY dyes, such as BODIPY 493/503, BODIPY 530/550, BODIPY 558/568, BODIPY 564/570, BODIPY 576/589, BODIPY 581/591, BODIPY 630/650, BODIPY 650/655, BODIPY FL, BODIPY R6G, BODIPY TMR, and, BODIP
  • fluorescently labeled ribonucleotides useful in the preparation of RT-PCR probes for use in some embodiments of the methods described herein are available from Molecular Probes (Invitrogen), and these include, Alexa Fluor 488-5-UTP, Fluorescein-12-UTP, BODIPY FL-14-UTP, BODIPY TMR-14-UTP, Tetramethylrhodamine-6-UTP, Alexa Fluor 546-14-UTP, Texas Red-5-UTP, and BODIPY TR-14-UTP.
  • Other fluorescent ribonucleotides are available from Amersham Biosciences (GE Healthcare), such as Cy3-UTP and Cy5-UTP.
  • Examples of fluorescently labeled deoxyribonucleotides useful in the preparation of RT-PCR probes for use in the methods described herein include Dinitrophenyl (DNP)-1′-dUTP, Cascade Blue-7-dUTP, Alexa Fluor 488-5-dUTP, Fluorescein-12-dUTP, Oregon Green 488-5-dUTP, BODIPY FL-14-dUTP, Rhodamine Green-5-dUTP, Alexa Fluor 532-5-dUTP, BODIPY TMR-14-dUTP, Tetramethylrhodamine-6-dUTP, Alexa Fluor 546-14-dUTP, Alexa Fluor 568-5-dUTP, Texas Red-12-dUTP, Texas Red-5-dUTP, BODIPY TR-14-dUTP, Alexa Fluor 594-5-dUTP, BODIPY 630/650-14-dUTP, BODIPY 650/665-14-dUTP; Alexa Fluor 488-7-
  • dyes and other moieties are introduced into nucleic acids used in the methods described herein, such as FRET probes, via modified nucleotides.
  • a “modified nucleotide” refers to a nucleotide that has been chemically modified, but still functions as a nucleotide.
  • the modified nucleotide has a chemical moiety, such as a dye or quencher, covalently attached, and can be introduced into an oligonucleotide, for example, by way of solid phase synthesis of the oligonucleotide.
  • the modified nucleotide includes one or more reactive groups that can react with a dye or quencher before, during, or after incorporation of the modified nucleotide into the nucleic acid.
  • the modified nucleotide is an amine-modified nucleotide, i.e., a nucleotide that has been modified to have a reactive amine group.
  • the modified nucleotide comprises a modified base moiety, such as uridine, adenosine, guanosine, and/or cytosine.
  • the amine-modified nucleotide is selected from 5-(3-aminoallyl)-UTP; 8-[(4-amino)butyl]-amino-ATP and 8-[(6-amino)butyl]-amino-ATP; N6-(4-amino)butyl-ATP, N6-(6-amino)butyl-ATP, N4-[2,2-oxy-bis-(ethylamine)]-CTP; N6-(6-Amino)hexyl-ATP; 8-[(6-Amino)hexyl]-amino-ATP; 5-propargylamino-CTP, 5-propargylamino-UTP.
  • nucleotides with different nucleobase moieties are similarly modified, for example, 5-(3-aminoallyl)-GTP instead of 5-(3-aminoallyl)-UTP.
  • Many amine modified nucleotides are commercially available from, e.g., Applied Biosystems, Sigma, Jena Bioscience and TriLink.
  • the methods of detecting at least one biomarker RNA described herein employ one or more modified oligonucleotides, such as oligonucleotides comprising one or more affinity-enhancing nucleotides.
  • modified oligonucleotides useful in the methods described herein include primers for reverse transcription, PCR amplification primers, and probes.
  • the incorporation of affinity-enhancing nucleotides increases the binding affinity and specificity of an oligonucleotide for its target nucleic acid as compared to oligonucleotides that contain only deoxyribonucleotides, and allows for the use of shorter oligonucleotides or for shorter regions of complementarity between the oligonucleotide and the target nucleic acid.
  • affinity-enhancing nucleotides include nucleotides comprising one or more base modifications, sugar modifications and/or backbone modifications.
  • modified bases for use in affinity-enhancing nucleotides include 5-methylcytosine, isocytosine, pseudoisocytosine, 5-bromouracil, 5-propynyluracil, 6-aminopurine, 2-aminopurine, inosine, diaminopurine, 2-chloro-6-aminopurine, xanthine and hypoxanthine.
  • affinity-enhancing modifications include nucleotides having modified sugars such as 2′-substituted sugars, such as 2′-O-alkyl-ribose sugars, 2′-amino-deoxyribose sugars, 2′-fluoro-deoxyribose sugars, 2′-fluoro-arabinose sugars, and 2′-O-methoxyethyl-ribose (2′MOE) sugars.
  • modified sugars are arabinose sugars, or d-arabino-hexitol sugars.
  • affinity-enhancing modifications include backbone modifications such as the use of peptide nucleic acids (e.g., an oligomer including nucleobases linked together by an amino acid backbone).
  • backbone modifications include phosphorothioate linkages, phosphodiester modified nucleic acids, combinations of phosphodiester and phosphorothioate nucleic acid, methylphosphonate, alkylphosphonates, phosphate esters, alkylphosphonothioates, phosphoramidates, carbamates, carbonates, phosphate triesters, acetamidates, carboxymethyl esters, methylphosphorothioate, phosphorodithioate, p-ethoxy, and combinations thereof.
  • the oligomer includes at least one affinity-enhancing nucleotide that has a modified base, at least nucleotide (which may be the same nucleotide) that has a modified sugar, and at least one internucleotide linkage that is non-naturally occurring.
  • the affinity-enhancing nucleotide contains a locked nucleic acid (“LNA”) sugar, which is a bicyclic sugar.
  • an oligonucleotide for use in the methods described herein comprises one or more nucleotides having an LNA sugar.
  • the oligonucleotide contains one or more regions consisting of nucleotides with LNA sugars.
  • the oligonucleotide contains nucleotides with LNA sugars interspersed with deoxyribonucleotides. See, e.g., Frieden, M. et al. (2008) Curr. Pharm. Des. 14(11):1138-1142.
  • primer refers to a nucleic acid sequence, whether occurring naturally as in a purified restriction digest or produced synthetically, which is capable of acting as a point of synthesis when placed under conditions in which synthesis of a primer extension product, which is complementary to a nucleic acid strand is induced (e.g. in the presence of nucleotides and an inducing agent such as DNA polymerase and at a suitable temperature and pH).
  • the primer must be sufficiently long to prime the synthesis of the desired extension product in the presence of the inducing agent.
  • the exact length of the primer will depend upon factors, including temperature, sequences of the primer and the methods used.
  • a primer typically contains 15-25 or more nucleotides, although it can contain less. The factors involved in determining the appropriate length of primer are readily known to one of ordinary skill in the art.
  • primer sets for the 15 genes are those listed in Table 7.
  • a person skilled in the art will appreciate that a number of methods can be used to determine the amount of a protein product of the biomarker of the invention, including immunoassays such as Western blots, ELISA, and immunoprecipitation followed by SDS-PAGE and immunocytochemistry.
  • immunoassays such as Western blots, ELISA, and immunoprecipitation followed by SDS-PAGE and immunocytochemistry.
  • an antibody is used to detect the polypeptide products of the fifteen biomarkers listed in Table 4.
  • the sample comprises a tissue sample.
  • the tissue sample is suitable for immunohistochemistry.
  • antibody as used herein is intended to include monoclonal antibodies, polyclonal antibodies, and chimeric antibodies. The antibody may be from recombinant sources and/or produced in transgenic animals.
  • antibody fragment as used herein is intended to include Fab, Fab′, F(ab′)2, scFv, dsFv, ds-scFv, dimers, minibodies, diabodies, and multimers thereof and bispecific antibody fragments.
  • Antibodies can be fragmented using conventional techniques. For example, F(ab′)2 fragments can be generated by treating the antibody with pepsin. The resulting F(ab′)2 fragment can be treated to reduce disulfide bridges to produce Fab′ fragments.
  • Papain digestion can lead to the formation of Fab fragments.
  • Fab, Fab′ and F(ab′)2, scFv, dsFv, ds-scFv, dimers, minibodies, diabodies, bispecific antibody fragments and other fragments can also be synthesized by recombinant techniques.
  • antibodies having specificity for a specific protein may be prepared by conventional methods.
  • a mammal e.g. a mouse, hamster, or rabbit
  • an immunogenic form of the peptide which elicits an antibody response in the mammal.
  • Techniques for conferring immunogenicity on a peptide include conjugation to carriers or other techniques well known in the art.
  • the peptide can be administered in the presence of adjuvant.
  • the progress of immunization can be monitored by detection of antibody titers in plasma or serum. Standard ELISA or other immunoassay procedures can be used with the immunogen as antigen to assess the levels of antibodies.
  • antisera can be obtained and, if desired, polyclonal antibodies isolated from the sera.
  • antibody producing cells can be harvested from an immunized animal and fused with myeloma cells by standard somatic cell fusion procedures thus immortalizing these cells and yielding hybridoma cells.
  • myeloma cells can be harvested from an immunized animal and fused with myeloma cells by standard somatic cell fusion procedures thus immortalizing these cells and yielding hybridoma cells.
  • Such techniques are well known in the art, (e.g. the hybridoma technique originally developed by Kohler and Milstein (Nature 256:495-497 (1975)) as well as other techniques such as the human B-cell hybridoma technique (Kozbor et al., Immunol.
  • Hybridoma cells can be screened immunochemically for production of antibodies specifically reactive with the peptide and the monoclonal antibodies can be isolated.
  • recombinant antibodies are provided that specifically bind protein products of the fifteen genes listed in Table 4, and optionally expression products of one or more genes listed in Table 3.
  • Recombinant antibodies include, but are not limited to, chimeric and humanized monoclonal antibodies, comprising both human and non-human portions, single-chain antibodies and multi-specific antibodies.
  • a chimeric antibody is a molecule in which different portions are derived from different animal species, such as those having a variable region derived from a murine monoclonal antibody (mAb) and a human immunoglobulin constant region.
  • mAb murine monoclonal antibody
  • Single-chain antibodies have an antigen binding site and consist of single polypeptides. They can be produced by techniques known in the art, for example using methods described in Ladner et. al U.S. Pat. No. 4,946,778 (which is incorporated herein by reference in its entirety); Bird et al., (1988) Science 242:423-426; Whitlow et al., (1991) Methods in Enzymology 2:1-9; Whitlow et al., (1991) Methods in Enzymology 2:97-105; and Huston et al., (1991) Methods in Enzymology Molecular Design and Modeling: Concepts and Applications 203:46-88.
  • Multi-specific antibodies are antibody molecules having at least two antigen-binding sites that specifically bind different antigens.
  • Such molecules can be produced by techniques known in the art, for example using methods described in Segal, U.S. Pat. No. 4,676,980 (the disclosure of which is incorporated herein by reference in its entirety); Holliger et al., (1993) Proc. Natl. Acad. Sci. USA 90:6444-6448; Whitlow et al., (1994) Protein Eng 7:1017-1026 and U.S. Pat. No. 6,121,424.
  • Monoclonal antibodies directed against any of the expression products of the genes listed in Table 4 and, optionally, against expression products of one or more genes listed in Table 3, can be identified and isolated by screening a recombinant combinatorial immunoglobulin library (e.g., an antibody phage display library) with the polypeptide(s) of interest.
  • Kits for generating and screening phage display libraries are commercially available (e.g., the Pharmacia Recombinant Phage Antibody System, Catalog No. 27-9400-01; and the Stratagene SurfZAP Phage Display Kit, Catalog No. 240612). Additionally, examples of methods and reagents particularly amenable for use in generating and screening antibody display library can be found in, for example, U.S. Pat. No.
  • Humanized antibodies are antibody molecules from non-human species having one or more complementarity determining regions (CDRs) from the non-human species and a framework region from a human immunoglobulin molecule.
  • CDRs complementarity determining regions
  • Humanized monoclonal antibodies can be produced by recombinant DNA techniques known in the art, for example using methods described in PCT Publication No. WO 87/02671; European Patent Application 184,187; European Patent Application 171,496; European Patent Application 173,494; PCT Publication No. WO 86/01533; U.S. Pat. No.
  • humanized antibodies can be produced, for example, using transgenic mice which are incapable of expressing endogenous immunoglobulin heavy and light chains genes, but which can express human heavy and light chain genes.
  • the transgenic mice are immunized in the normal fashion with a selected antigen, e.g., all or a portion of a polypeptide corresponding to a protein product.
  • Monoclonal antibodies directed against the antigen can be obtained using conventional hybridoma technology.
  • the human immunoglobulin transgenes harbored by the transgenic mice rearrange during B cell differentiation, and subsequently undergo class switching and somatic mutation. Thus, using such a technique, it is possible to produce therapeutically useful IgG, IgA and IgE antibodies.
  • Antibodies may be isolated after production (e.g., from the blood or serum of the subject) or synthesis and further purified by well-known techniques. For example, IgG antibodies can be purified using protein A chromatography. Antibodies specific for a protein can be selected or (e.g., partially purified) or purified by, e.g., affinity chromatography. For example, a recombinantly expressed and purified (or partially purified) expression product may be produced, and covalently or non-covalently coupled to a solid support such as, for example, a chromatography column.
  • the column can then be used to affinity purify antibodies specific for the protein products of the genes listed in Tables 3 and 4 from a sample containing antibodies directed against a large number of different epitopes, thereby generating a substantially purified antibody composition, i.e., one that is substantially free of contaminating antibodies.
  • a substantially purified antibody composition it is meant, in this context, that the antibody sample contains at most only 30% (by dry weight) of contaminating antibodies directed against epitopes other than those of the protein products of the genes listed in Tables 3 and 4, and preferably at most 20%, yet more preferably at most 10%, and most preferably at most 5% (by dry weight) of the sample is contaminating antibodies.
  • a purified antibody composition means that at least 99% of the antibodies in the composition are directed against the desired protein.
  • substantially purified antibodies may specifically bind to a signal peptide, a secreted sequence, an extracellular domain, a transmembrane or a cytoplasmic domain or cytoplasmic membrane of a protein product of one of the genes listed in Tables 3 and 4. In an embodiment, substantially purified antibodies specifically bind to a secreted sequence or an extracellular domain of the amino acid sequences of a protein product of one of the genes listed in Tables 3 and 4.
  • antibodies directed against a protein product of one of the genes listed in Tables 3 and 4 can be used to detect the protein products or fragment thereof (e.g., in a cellular lysate or cell supernatant) in order to evaluate the level and pattern of expression of the protein. Detection can be facilitated by the use of an antibody derivative, which comprises an antibody coupled to a detectable substance. Examples of detectable substances include various enzymes, prosthetic groups, fluorescent materials, luminescent materials, bioluminescent materials, and radioactive materials.
  • suitable enzymes include horseradish peroxidase, alkaline phosphatase, .beta.-galactosidase, or acetylcholinesterase;
  • suitable prosthetic group complexes include streptavidin/biotin and avidin/biotin;
  • suitable fluorescent materials include umbelliferone, fluorescein, fluorescein isothiocyanate, rhodamine, dichlorotriazinylamine fluorescein, dansyl chloride or phycoerythrin;
  • an example of a luminescent material includes luminol;
  • examples of bioluminescent materials include luciferase, luciferin, and aequorin, and examples of suitable radioactive material include 125I, 131I, 35S or 3H.
  • a variety of techniques can be employed to measure expression levels of each of the fifteen, and optional additional, genes given a sample that contains protein products that bind to a given antibody. Examples of such formats include, but are not limited to, enzyme immunoassay (EIA), radioimmunoassay (RIA), Western blot analysis and enzyme linked immunoabsorbant assay (ELISA).
  • EIA enzyme immunoassay
  • RIA radioimmunoassay
  • ELISA enzyme linked immunoabsorbant assay
  • antibodies, or antibody fragments or derivatives can be used in methods such as Western blots or immunofluorescence techniques to detect the expressed proteins.
  • either the antibodies or proteins are immobilized on a solid support.
  • Suitable solid phase supports or carriers include any support capable of binding an antigen or an antibody.
  • Well-known supports or carriers include glass, polystyrene, polypropylene, polyethylene, dextran, nylon, amylases, natural and modified celluloses, polyacrylamides, gabbros, and magnetite.
  • the support can then be washed with suitable buffers followed by treatment with the detectably labeled antibody.
  • the solid phase support can then be washed with the buffer a second time to remove unbound antibody.
  • the amount of bound label on the solid support can then be detected by conventional means.
  • Immunohistochemistry methods are also suitable for detecting the expression levels of the prognostic markers.
  • antibodies or antisera including polyclonal antisera, and monoclonal antibodies specific for each marker may be used to detect expression.
  • the antibodies can be detected by direct labeling of the antibodies themselves, for example, with radioactive labels, fluorescent labels, hapten labels such as, biotin, or an enzyme such as horse radish peroxidase or alkaline phosphatase.
  • unlabeled primary antibody is used in conjunction with a labeled secondary antibody, comprising antisera, polyclonal antisera or a monoclonal antibody specific for the primary antibody. Immunohistochemistry protocols and kits are well known in the art and are commercially available.
  • Immunological methods for detecting and measuring complex formation as a measure of protein expression using either specific polyclonal or monoclonal antibodies are known in the art. Examples of such techniques include enzyme-linked immunosorbent assays (ELISAs), radioimmunoassays (RIAs), fluorescence-activated cell sorting (FACS) and antibody arrays. Such immunoassays typically involve the measurement of complex formation between the protein and its specific antibody. These assays and their quantitation against purified, labeled standards are well known in the art (Ausubel, supra, unit 10.1-10.6).
  • a two-site, monoclonal-based immunoassay utilizing antibodies reactive to two non-interfering epitopes is preferred, but a competitive binding assay may be employed (Pound (1998) Immunochemical Protocols, Humana Press, Totowa N.J.).
  • a detection label is indirectly conjugated with the antibody.
  • the antibody can be conjugated with biotin and any of the three broad categories of labels mentioned above can be conjugated with avidin, or vice versa. Biotin binds selectively to avidin and thus, the label can be conjugated with the antibody in this indirect manner.
  • the antibody is conjugated with a small hapten (e.g. digoxin) and one of the different types of labels mentioned above is conjugated with an anti-hapten antibody (e.g. anti-digoxin antibody).
  • the antibody need not be labeled, and the presence thereof can be detected using a labeled antibody, which binds to the antibody.
  • the 15-gene signature described herein can be used to select treatment for NCSLC patients.
  • the biomarkers can classify patients with NSCLC into a poor survival group or a good survival group and into groups that might benefit from adjuvant chemotherapy or not.
  • the application provides a method of selecting a therapy for a subject with NSCLC, comprising the steps:
  • the application provides a method of selecting a therapy for a subject with NSCLC, comprising the steps:
  • adjuvant chemotherapy means treatment of cancer with chemotherapeutic agents after surgery where all detectable disease has been removed, but where there still remains a risk of small amounts of remaining cancer.
  • chemotherapeutic agents include cisplatin, carboplatin, vinorelbine, gemcitabine, doccetaxel, paclitaxel and navelbine.
  • the application provides compositions useful in detecting changes in the expression levels of the 15 genes listed in Table 4. Accordingly in one embodiment, the application provides a composition comprising a plurality of isolated nucleic acid sequences wherein each isolated nucleic acid sequence hybridizes to:
  • the composition is used to measure the level of RNA expression of the 15 genes.
  • the plurality of isolated nucleic acid sequences comprise isolated nucleic acids hybridizable to the 15 probe target sequences as set out in Table 9.
  • the plurality of isolated nucleic acid sequences comprise isolated nucleic acids hybridizable to SEQ ID NO: 3, 11-15, 22, 26, 35, 49, 78, 85, 130, 133, and 169.
  • the application provides a composition comprising 15 forward and 15 reverse primers for amplifying a region of each gene listed in Table 4.
  • the 30 primers are as set out in Table 7.
  • the 30 primers each comprise a sequence that is identical to the sequence of one of SEQ ID NO: 173-202.
  • the application also provides an array that is useful in detecting the expression levels of the 15 genes set out in Table 4. Accordingly, in one embodiment, the application provides an array comprising for each gene shown in Table 4 one or more nucleic acid probes complementary and hybridizable to an expression product of the gene. In a particular embodiment, the array comprises the nucleic acid probes hybridizable to the probe target sequences listed in Table 9. In one embodiment, the array comprises the nucleic acid probes hybridizable to sequences identical to each of SEQ ID NO: 3, 11-15, 22, 26, 35, 49, 78, 85, 130, 133, and 169.
  • kits used to prognose or classify a subject with NSCLC into a good survival group or a poor survival group or to select a therapy for a subject with NSCLC that includes detection agents that can detect the expression products of the biomarkers.
  • the application provides a kit to prognose or classify a subject with early stage NSCLC comprising detection agents that can detect the expression products of 15 biomarkers, wherein the 15 biomarkers comprise 15 genes in Table 4.
  • kits for classifying a subject comprise detection agents that can detect the expression of 16, 17, or 18 biomarkers, wherein 15 biomarkers comprise the 15 genes in Table 4, and the additional biomarkers are selected from the genes listed in Table 3.
  • the additional sixteenth, seventeenth, and eighteenth biomarkers may be selected from RGS4, UGT2B4, and MCF2 listed in Table 3.
  • kits to select a therapy for a subject with NSCLC comprising detection agents that can detect the expression products of 15 biomarkers, wherein the 15 biomarkers comprise 15 genes in Table 4.
  • kits for selecting therapy for a subject comprise detection agents that can detect the expression of 16, 17, or 18 biomarkers, wherein 15 biomarkers comprise the 15 genes in Table 4, and the additional biomarkers are selected from the genes listed in Table 3.
  • the additional sixteenth, seventeenth, and eighteenth biomarkers may be selected from RGS4, UGT2B4, and MCF2 listed in Table 3.
  • kits comprise agents (like the polynucleotides and/or antibodies described herein as non-limiting examples) for the detection of expression of the disclosed sequences, such as for example, SEQ ID NO: 3, 11-15, 22, 26, 35, 49, 78, 85, 130, 133, and 169, the target sequences listed in Table 9, or the target sequences listed in Table 11.
  • agents like the polynucleotides and/or antibodies described herein as non-limiting examples
  • Kits may comprise containers, each with one or more of the various reagents (sometimes in concentrated form), for example, pre-fabricated microarrays, buffers, the appropriate nucleotide triphosphates (e.g., dATP, dCTP, dGTP and dTTP; or rATP, rCTP, rGTP and UTP), reverse transcriptase, DNA polymerase, RNA polymerase, and one or more primer complexes (e.g., appropriate length poly(T) or random primers linked to a promoter reactive with the RNA polymerase).
  • the appropriate nucleotide triphosphates e.g., dATP, dCTP, dGTP and dTTP; or rATP, rCTP, rGTP and UTP
  • reverse transcriptase e.g., DNA polymerase, RNA polymerase
  • primer complexes e.g., appropriate length poly(T) or random primers linked to a promoter
  • a kit may comprise a plurality of reagents, each of which is capable of binding specifically with a target nucleic acid or protein.
  • Suitable reagents for binding with a target protein include antibodies, antibody derivatives, antibody fragments, and the like.
  • Suitable reagents for binding with a target nucleic acid include complementary nucleic acids.
  • nucleic acid reagents may include oligonucleotides (labeled or non-labeled) fixed to a substrate, labeled oligonucleotides not bound with a substrate, pairs of PCR primers, molecular beacon probes, and the like.
  • kits may comprise additional components useful for detecting gene expression levels.
  • kits may comprise fluids (e.g. SSC buffer) suitable for annealing complementary nucleic acids or for binding an antibody with a protein with which it specifically binds, one or more sample compartments, a material which provides instruction for detecting expression levels, and the like.
  • kits for use in the RT-PCR methods described herein comprise one or more target RNA-specific FRET probes and one or more primers for reverse transcription of target RNAs or amplification of cDNA reverse transcribed therefrom.
  • one or more of the primers is “linear”.
  • a “linear” primer refers to an oligonucleotide that is a single stranded molecule, and typically does not comprise a short region of, for example, at least 3, 4 or 5 contiguous nucleotides, which are complementary to another region within the same oligonucleotide such that the primer forms an internal duplex.
  • the primers for use in reverse transcription comprise a region of at least 4, such as at least 5, such as at least 6, such as at least 7 or more contiguous nucleotides at the 3′-end that has a base sequence that is complementary to region of at least 4, such as at least 5, such as at least 6, such as at least 7 or more contiguous nucleotides at the 5′-end of a target RNA.
  • the kit further comprises one or more pairs of linear primers (a “forward primer” and a “reverse primer”) for amplification of a cDNA reverse transcribed from a target RNA.
  • the forward primer comprises a region of at least 4, such as at least 5, such as at least 6, such as at least 7, such as at least 8, such as at least 9, such as at least 10 contiguous nucleotides having a base sequence that is complementary to the base sequence of a region of at least 4, such as at least 5, such as at least 6, such as at least 7, such as at least 8, such as at least 9, such as at least 10 contiguous nucleotides at the 5′-end of a target RNA.
  • the reverse primer comprises a region of at least 4, such as at least 5, such as at least 6, such as at least 7, such as at least 8, such as at least 9, such as at least 10 contiguous nucleotides having a base sequence that is complementary to the base sequence of a region of at least 4, such as at least 5, such as at least 6, such as at least 7, such as at least 8, such as at least 9, such as at least 10 contiguous nucleotides at the 3′-end of a target RNA.
  • the kit comprises at least a first set of primers for amplification of a cDNA that is reverse transcribed from a target RNA capable of specifically hybridizing to a nucleic acid comprising a sequence identically present in one of the genes listed in Table 4.
  • the kit comprises at least fifteen sets of primers, each of which is for amplification of a different target RNA capable of specifically hybridizing to a nucleic acid comprising a sequence identically present in a different gene listed in Table 4.
  • the kit comprises fifteen forward and fifteen reverse primers described in Table 7, comprising sequences identical to SEQ ID NOs 173-202.
  • the kit comprises one, two, or three more sets of primers, in addition to the fifteen sets of primers, each of the additional sets being for amplification of a different target RNA capable of specifically hybridizing to a nucleic acid comprising a sequence identically present in a different gene listed in Table 3.
  • the kit comprises one, two, or three more sets of primers, in addition to the fifteen sets of primers, each of the additional sets being for amplification of a different target RNA capable of specifically hybridizing to a nucleic acid comprising a sequence identically present in RGS4, UGT2B4, or MCF2 listed in Table 3.
  • the kit comprises at least one set of primers that is capable of amplifying more than one cDNA reverse transcribed from a target RNA in a sample.
  • probes and/or primers for use in the compositions described herein comprise deoxyribonucleotides.
  • probes and/or primers for use in the compositions described herein comprise deoxyribonucleotides and one or more nucleotide analogs, such as LNA analogs or other duplex-stabilizing nucleotide analogs described above.
  • probes and/or primers for use in the compositions described herein comprise all nucleotide analogs.
  • the probes and/or primers comprise one or more duplex-stabilizing nucleotide analogs, such as LNA analogs, in the region of complementarity.
  • compositions described herein also comprise probes, and in the case of RT-PCR, primers, that are specific to one or more housekeeping genes for use in normalizing the quantities of target RNAs.
  • probes and primers
  • Such probes (and primers) include those that are specific for one or more products of housekeeping genes selected from ACTB, BAT1, B2M, TBP, U6 snRNA, RNU44, RNU 48, and U47.
  • kits for use in real time RT-PCR methods described herein further comprise reagents for use in the reverse transcription and amplification reactions.
  • the kits comprise enzymes such as reverse transcriptase, and a heat stable DNA polymerase, such as Taq polymerase.
  • the kits further comprise deoxyribonucleotide triphosphates (dNTP) for use in reverse transcription and amplification.
  • the kits comprise buffers optimized for specific hybridization of the probes and primers.
  • kits are provided containing antibodies to each of the protein products of the genes listed in Table 4, conjugated to a detectable substance, and instructions for use.
  • the kits comprise antibodies to one, two, or three protein products of the genes listed in Table 3, in addition to antibodies to each of the protein products of the genes listed in Table 4.
  • the kit comprises antibodies to the protein product of one, two, or all three of RGS4, UGT2B4, or MCF2 listed in Table 3, in addition to antibodies to each of the protein products of the genes listed in Table 4.
  • Kits may comprise an antibody, an antibody derivative, or an antibody fragment, which binds specifically with a marker protein, or a fragment of the protein.
  • Such kits may also comprise a plurality of antibodies, antibody derivatives, or antibody fragments wherein the plurality of such antibody agents binds specifically with a marker protein, or a fragment of the protein.
  • kits may comprise antibodies such as a labeled or labelable antibody and a compound or agent for detecting protein in a biological sample; means for determining the amount of protein in the sample; means for comparing the amount of protein in the sample with a standard; and instructions for use.
  • kits can be supplied to detect a single protein or epitope or can be configured to detect one of a multitude of epitopes, such as in an antibody detection array. Arrays are described in detail herein for nucleic acid arrays and similar methods have been developed for antibody arrays.
  • RNA products of the biomarkers can be used to determine the expression of the biomarkers.
  • probes, primers, complementary nucleotide sequences or nucleotide sequences that hybridize to the RNA products can be used to detect protein products of the biomarkers.
  • ligands or antibodies that specifically bind to the protein products can be used to detect protein products of the biomarkers.
  • the detection agents are probes that hybridize to the 15 biomarkers.
  • the probe target sequences are as set out in Table 9.
  • the probe target sequences are identical to SEQ ID NO: 3, 11-15, 22, 26, 35, 49, 78, 85, 130, 133, and 169.
  • the detection agents are forward and reverse primers that amplify a region of each of the 15 genes listed in Table 4.
  • the primers are as set out in Table 7.
  • the primers comprise the polynucleotide sequences of SEQ ID NO: 173-202.
  • detection agents can be labeled.
  • the label is preferably capable of producing, either directly or indirectly, a detectable signal.
  • the label may be radio-opaque or a radioisotope, such as 3 H, 14 C, 32 P, 35 S, 123 I, 125 I, 131 I; a fluorescent (fluorophore) or chemiluminescent (chromophore) compound, such as fluorescein isothiocyanate, rhodamine or luciferin; an enzyme, such as alkaline phosphatase, beta-galactosidase or horseradish peroxidase; an imaging agent; or a metal ion.
  • a radioisotope such as 3 H, 14 C, 32 P, 35 S, 123 I, 125 I, 131 I
  • a fluorescent (fluorophore) or chemiluminescent (chromophore) compound such as fluorescein isothiocyanate, rhodamine or luciferin
  • an enzyme such as
  • the kit can also include a control or reference standard and/or instructions for use thereof.
  • the kit can include ancillary agents such as vessels for storing or transporting the detection agents and/or buffers or stabilizers.
  • a multi-gene signature is provided for prognosis or classifying patients with lung cancer.
  • a fifteen-gene signature is provided, comprising reference values for each of the fifteen genes based on relative expression data from a historical data set with a known outcome, such as good or poor survival, and/or known treatment, such as adjuvant chemotherapy.
  • four reference values are provided for each of the fifteen genes listed in Table 4.
  • the reference values for each of the fifteen genes are principal component values set forth in Table 10.
  • relative expression data from a patient are combined with the gene-specific reference values on a gene-by-gene basis for each of the fifteen, and, optionally, additional genes, to generate a test value which allows prognosis or therapy recommendation.
  • relative expression data are subjected to an algorithm that yields a single test value, or combined score, which is then compared to a control value obtained from the historical expression data for a patient or pool of patients.
  • control value is a numerical threshold for predicting outcomes, for example good and poor outcome, or making therapy recommendations for a subject, for example adjuvant chemotherapy in addition to surgical resection or surgical resection alone.
  • a test value or combined score greater than the control value is predictive, for example, of a poor outcome or benefit from adjuvant chemotherapy, whereas a combined score falling below the control value is predictive, for example, of a good outcome or lack of benefit from adjuvant chemotherapy for a subject.
  • a method for prognosing or classifying a subject with NSCLC comprises:
  • the combined score is calculated from relative expression data multiplied by reference values, determined from historical data, for each gene. Accordingly, the combined score may be calculated using Formula I below:
  • PC1 is the sum of the relative expression level for each gene in a multi-gene signature multiplied by a first principal component for each gene in the multi-gene signature
  • PC2 is the sum of the relative expression level for each gene multiplied by a second principal component for each gene
  • PC3 is the sum of the relative expression level for each gene multiplied by a third principal component for each gene
  • PC4 is the sum of the relative expression level for each gene multiplied by a fourth principal component for each gene.
  • the combined score is referred to as a risk score.
  • a risk score for a subject can be calculated by applying Formula I to relative expression data from a test sample obtained from the subject.
  • PC1 is the sum of the relative expression level for each gene provided in Table 4 multiplied by a first principal component for each gene, respectively, as set forth in Table 10;
  • PC2 is the sum of the relative expression level for each gene provided in Table 4 multiplied by a second principal component for each gene, respectively, as set forth in Table 10;
  • PC3 is the sum of the relative expression level for each gene provided in Table 4 multiplied by a third principal component for each gene, respectively, as set forth in Table 10;
  • PC4 is the sum of the relative expression level for each gene provided in Table 4 multiplied by a fourth principal component for each gene, respectively, as set forth in Table 10.
  • control value is equal to ⁇ 0.1.
  • a subject with a risk score of more than ⁇ 0.1 is classified as high risk (poor prognosis).
  • a patient with a risk score of less than ⁇ 0.1 is classified as lower risk (good prognosis).
  • adjuvant chemotherapy is recommended for a subject with a risk score of more than ⁇ 0.1 and not recommended for a subject with a risk score of less than ⁇ 0.1.
  • the application provides computer programs and computer implemented products for carrying out the methods described herein. Accordingly, in one embodiment, the application provides a computer program product for use in conjunction with a computer having a processor and a memory connected to the processor, the computer program product comprising a computer readable storage medium having a computer mechanism encoded thereon, wherein the computer program mechanism may be loaded into the memory of the computer and cause the computer to carry out the methods described herein.
  • the application provides a computer implemented product for predicting a prognosis or classifying a subject with NSCLC comprising:
  • a database comprising a reference expression profile associated with a prognosis, wherein the subject biomarker expression profile and the biomarker reference profile each has fifteen values, each value representing the expression level of a biomarker, wherein each biomarker corresponds to one gene in Table 4; wherein the computer implemented product selects the biomarker reference expression profile most similar to the subject biomarker expression profile, to thereby predict a prognosis or classify the subject.
  • the application provides a computer implemented product for determining therapy for a subject with NSCLC comprising:
  • a database comprising a reference expression profile associated with a therapy, wherein the subject biomarker expression profile and the biomarker reference profile each has fifteen values, each value representing the expression level of a biomarker, wherein each biomarker corresponds to one gene in Table 4; wherein the computer implemented product selects the biomarker reference expression profile most similar to the subject biomarker expression profile, to thereby predict the therapy.
  • Another aspect relates to computer readable mediums such as CD-ROMs.
  • the application provides computer readable medium having stored thereon a data structure for storing a computer implemented product described herein.
  • the data structure is capable of configuring a computer to respond to queries based on records belonging to the data structure, each of the records comprising:
  • the application provides a computer system comprising
  • the application provides a computer implemented product comprising
  • FIG. 1A and Table 3 Using a p0.005 as cut-off, 172 of 19,619 probe sets were significantly associated with prognosis in 62 observation patients ( FIG. 1A and Table 3). Using a method that was designed to identify the minimum expression gene set that can distinguish most patients with poor and good survival outcomes, a 15-gene prognostic signature was identified ( FIG. 1A and Table 4). This signature was able to separate the 62 non-adjuvant treated patients into 31 low-risk and 31 high-risk patients for death (HR 15.020, 95% Cl 5.12-44.04, p ⁇ 0.0001; FIG. 2B ).
  • the initial study population comprised a subset of the patients randomized in the JBR.10 trial.
  • the samples were harvested using a standardized protocol that was agreed upon during trial protocol development by designated pathologists from each participating centre. All tumors and corresponding normal lung tissue were collected as soon as or within 30 min after resection, and were snap-frozen in liquid nitrogen.
  • Affymetrix U133A (Affymetrix, Santa Clara, Calif.) were pre-processed using RMAexpress v0.32, then were twice log 2 transformed since the distribution of additional log 2 transformed data appeared more normal. Probe sets were annotated using NetAffx v4.2 annotation tool and only grade A level probe sets 3 (NA24) were included for further analysis. Affymetrix U133A chip contains 22,215 probe sets (19,619 probe sets with grade A annotation). Since the microarray hybridizations were performed in two batches at two separate occasions (January 2004, and June 2005), and unsupervised clustering showed that a batch difference was significant ( FIG.
  • a distance-weighted discrimination (DWD) algorithm https://genome.unc.edu/pubsup/dwd/index.html was applied to homogenize the two batches.
  • the DWD algorithm first finds a hyperplane that separates the two batches and adjusts the data by projecting the different batches on the DWD plane, finds the batch mean, and then subtracts out the DWD plane multiplied by this mean.
  • the data were Z score transformed which made the validation across different datasets possible.
  • the MAximizing R Square Algorithm included 3 sequential steps: a) probe set pre-selection; b) signature optimization; and c) leave-one-out-cross-validation.
  • the candidate probe sets were pre-selected by their associations with survival at p ⁇ 0.005 level.
  • risk score z score weighted by the coefficient of the univariate Cox regression
  • the exclusion procedure excluded one probe at a time, summed up the risk score of the remaining 171 probes, the calculated the R square (R 2 , Goodness-of-fit) of the Cox model 5,6 .
  • Risk score was dichotomized by an outcome-orientated optimization of cutoff macro based on log-rank statistics (http://ndc.mayo.edu/mayo/research/biostat/sasmacros.cfm) before being introduced to the Cox proportional hazards model.
  • a probe set was excluded if its exclusion resulted in obtaining the largest R 2 . The procedure was repeated until there was only one probe set left. An inclusion procedure was followed using the probe set left by the exclusion procedure as the starting probe set.
  • PCA Principal components analysis
  • multivariate Cox regression model with the first 4 principal components were fitted to the disease specific survival of the 62 observation patients.
  • the linear prognostic scores were calculated by the sum of the multiplication of the estimated coefficient from Cox model and the corresponding principal component value.
  • prognostic score patients were divided into low and high risk group based on the median of the prognostic score, i.e., those with prognostic score less than the median as low risk group, while those with score no less than the median as high risk group.
  • 31 patients were classified in each group. Applying the same rule to the 73 chemo-treated patients, 36 patients were classified in low risk group and 37 patients in high-risk group.
  • the DC dataset contained only adenocarcinoma cases.
  • Applying the 15-gene signature on DC stage I and II, was able to separate 87 low risk cases from the 82 high risk cases (log rank p 0.0002, FIG. 2E ).
  • Multivariate analysis revealed that the 15-gene signature was an independent prognostic factor.
  • Gene expression signature is thought to represent the altered key pathways in carcinogenesis and thus is able to predict patients' outcome. However, being able to faithfully represent the altered key pathways, the signature must be generated from genome-wide gene expression data.
  • the present study used all information generated by Affymetrix U133A chip on NSCLC samples from a randomized clinical trial to derive a 15-gene signature. The 15-gene signature was able to identify 50% (31/62) stage IB-II NSCLC patients had relative good outcome. Multivariate analysis indicated that the 15-gene signature was an independent prognostic factor.
  • stage IB NSCLC stage IB NSCLC
  • MB Myoglobin
  • Uridine monophosphate synthetase (UMPS) participated in the ‘de novo’ pyrimidine base biosynthetic process, however, none of them has not been explored in lung cancer.
  • L1 cell adhesion molecule L1 cell adhesion molecule
  • ATPase, Na+/K+ transporting, beta 1 polypeptide (ATP1B1) was involved in ion transport which was reported recently to be able to discriminate the serous low malignant potential and invasive epithelial ovarian tumors 29 .
  • the range of expression levels of members of the 15-gene signature was broad, from very low expression level such as MDM2 and ZNF236 to fairly high expression such as TRIM14 or very high expression such as ATP1B1 (Table 4).
  • Least variable gene ( ⁇ 5%), such as UMPS (Table 4) was also a member of the signature. These data suggested that it may not be a good practice to exclude low expressed and least variable probe set in the data pre-selection process in an arbitrary way.
  • the signature generated using the present strategy performed better than that of Raponi's method of using the top 50 genes. There are only 3 genes (IKBKAP, L1CAM, and FAM64A) whose significance in association with survival is in the top 50 genes (Table 4).
  • JBR.10 protocol included in the JBR.10 protocol was the collection of snap-frozen or formalin-fixed paraffin embedded tumor samples for KRAS mutation analysis and tissue banking for future laboratory studies 3 . Altogether 445 of 482 randomized patients consented to banking. Snap-frozen tissues were collected from 169 Canadian patients ( FIG. 4 ). Histological evaluation of the HE section from the snap-frozen tumor samples revealed 166 that contained an estimated >20% tumor cellularity; gene expression profiling was completed in 133 of these patient samples, using the U133A oligonucleotide microarrays (Affymetrix, Santa Clara, Calif.). Profiling was not completed in 33 patient samples. Of 133 patients with microarray profiles, 62 did not received post-operative adjuvant chemotherapy and were group as observation patients, while 71 patients were received chemotherapy. University Health Network Research Ethics Board approved the study protocol.
  • the raw microarray data were pre-processed using RMAexpress v0.3 22 .
  • Probe sets were annotated using NetAffx v4.2 annotation tool and only grade A level probe sets 23 (NA22) were included for further analysis. Because the microarray profiling was done in two separate batches at different times and unsupervised heuristic K-means clustering identified a systematic difference between the two batches ( FIG. 6 ), the distance-weighted discrimination (DWD) method (https://genome.unc.edu/pubsup/dwd/index.html) was used to adjust the difference.
  • DWD distance-weighted discrimination
  • the DWD method first finds a separating hyperplane between the two batches and adjusts the data by projecting the different batches on the DWD plane, discover the batch mean, and then subtracts out the DWD plane multiplied by this mean.
  • the data were then transformed to Z score by centering to its mean and scaling to its standard deviation. This transformation was necessary for validation on different datasets in which different expression ranges are likely to exist, and for validation on different platforms, such as qPCR where the data scale is different.
  • the pre-selected probe sets by univariate analysis at p ⁇ 0.005 were selected by an exclusion procedure.
  • the exclusion selection excluded one probe set at a time based on the resultant R square (R 2 , Goodness-of-fit 15,16 ) of the Cox model. It kept repeating until there was only one probe set left. The procedure was repeated until there was only one probe set left. An inclusion procedure was followed using the probe set left by the exclusion procedure as the starting probe set. It included one probe set at a time based on the resultant R 2 of the Cox model. Finally, the R 2 was plotted against the probe set and a set of minimum number of probe sets yet having the largest R 2 was chosen as candidate signature.
  • Gene signature was established after passing the internal validation by leave-one-out-cross-validation (LOOCV) and external validation on other datasets (listed below). All statistical analyses were performed using SAS v9.1 (SAS Institute, CA).
  • the DCC dataset used in this validation study included only 169 patients: 67 from UM, 46 from HLM, 56 from MSK.
  • Two additional published microarray datasets were also used for validation: the Duke's University dataset of 85 non-small cell lung cancer patients (Potti, et al, NEJM), and the University of Michigan dataset of 106 squamous cell carcinomas patients (UM-SQ) (Rapponi et al).
  • Raw data of these microarray studies were downloaded and RMA pre-processed.
  • the expression levels were Z score transformed after double log 2 transformation. Risk score was the Z score weighted by the coefficient of the Cox model from the OBS.
  • Demographic data of the DC cohort was listed in Table 5.
  • Risk score was the product of coefficient of Cox proportional model and the standardized expression level.
  • the univariate association of the expression of the individual probe set with overall survival (date of randomization to date of last followup or death) was evaluated by Cox proportional hazards regression.
  • a stringent p ⁇ 0.005 was set as a selection criteria in order to minimize the possibility of false-positive results.

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Abstract

The application provides methods of prognosing and classifying lung cancer patients into poor survival groups or good survival groups and for determining the benefit of adjuvant chemotherapy by way of a multigene signature. The application also includes kits and computer products for use in the methods of the application.

Description

    CROSS-REFERENCE TO RELATED APPLICATION
  • This application claims benefit under 35 U.S.C. §119(e) to U.S. provisional application Ser. No. 61/071,728, filed 14 May 2008, incorporated herein by reference in its entirety.
    • FIELD
  • The application relates to compositions and methods for prognosing and classifying non-small cell lung cancer and for determining the benefit of adjuvant chemotherapy.
    • BACKGROUND OF THE INVENTION
  • In North America, lung cancer is the leading cancer in males and the leading cause of cancer deaths in both males and females'. Non-small cell lung cancer (NSCLC) represents 80% of all lung cancers and has an overall 5-year survival rate of only 16% 1. Tumor stage is the primary determinant for treatment selection for NSCLC patients. Recent clinical trials have led to the adoption of adjuvant cisplatin-based chemotherapy in early stage NSCLC patients (Stages IB-IIIA). The 5-year survival advantage conferred by adjuvant chemotherapy in recent trials are 4% in the International Adjuvant Lung Trial (IALT) involving 1,867 stage I-III patients2, 15% in the National Cancer Institute of Canada Clinical Trials Group (NCIC CTG) BR.10 Trial involving 483 stage IB-II patients3, and 9% in the Adjuvant Navelbine International Trialist Association (ANITA) trial involving 840 stage IB-IIIA patients4. Pre-planned stratification analysis in the later two trials showed no significant survival benefit for stage IB patients3,4. This was also demonstrated in the Cancer and Leukemia Group (CALGB) Trial 9633 that tested the benefit of chemotherapy on 344 stage IB patients receiving carboplatin and paclitaxel or observation5. Although initially presented in 2004 as a positive trial, recent survival analyses show no significant survival advantage with chemotherapy for either disease-free survival (HR=0.80, p=0.065) or overall survival (HR=0.83, p=0.12)5. In an attempt to draw an overall conclusion regarding the effectiveness of adjuvant cisplatin-based chemotherapy, the Lung Adjuvant Cisplatin Evaluation (LACE) meta-analysis6 was conducted which synthesized information from the 5 largest published, cisplatin-based trials that did not administer concurrent thoracic radiation [Adjuvant Lung Project Italy (ALPI)7, Big Lung Trial (BLT)8, IALT2, BR.103, and ANITA9]. The study found a 5.3% absolute survival advantage at 5-year (HR=0.89, 95% Cl 0.82-0.96, p=0.004). However, stratified analysis by stage showed that the stage IB patients did not benefit significantly from cisplatin treatment (HR=0.92, 95% Cl 0.78-1.10). Moreover, a detriment for chemotherapy was suggested in stage IA patients (HR=1.41, 95% Cl 0.96-2.09)6. Therefore, the current standard of treatment for patients with stage I NSCLC remains surgical resection alone. However, 30 to 40 percent of these stage I patients are expected to relapse after the initial surgery10,11, indicating that a subgroup of these patients might benefit from adjuvant chemotherapy.
  • The lack of consistent prognostic molecular markers for early stage NSCLC patients led to attempts to identify novel gene expression signatures using genome wide microarray platforms. Such multi-gene signatures might be stronger than individual genes to predict poor prognosis and poor prognostic patients could potentially benefit from adjuvant therapies. Previous microarray studies have identified prognostic signatures that demonstrated minimal overlaps in the gene sets.12-20 While only one of the early studies involved secondary signature validation in independent datasets12, all recently reported signatures were tested for validation13-16, 20. Nevertheless, lack of direct overlaps between signatures remains. One of the potential confounding factors is that signatures were derived from patients operated at single institutions, which may introduce biases.
    • SUMMARY OF THE INVENTION
  • As discussed in the Background section, certain patients suffering from NSCLC benefit from adjuvant chemotherapy. Attempts to identify systematically patient subpopulations in which adjuvant therapy would lead to increased survival or improve patient prognosis have generally failed. Efforts to assemble prognostic molecular markers have yielded various non-overlapping gene sets but have fallen short of establishing a gene signature with a minimal set of genes that is predictive regardless of the form of NSCLC (eg. adenocarcinoma or squamous cell carcinoma) or stage, and serves as a reliable classifier for adjuvant therapy benefit.
  • As will be discussed in more detail below, Applicants have identified from historical patient data a minimal set of fifteen genes whose expression levels, either alone or in combination with that of one to 3 additional genes, is prognostic of survival outcome and diagnostic of adjuvant therapy benefit. The fifteen genes are provided in Table 4. Optional additional genes may be selected from those provided in Table 3. The prognostic and diagnostic value of the gene sets identified by Applicants was verified by validation against independent data sets, as set forth in the Examples below. The present disclosure provides methods and kits useful for obtaining and utilizing expression information for the fifteen, and optionally one to 3 additional genes, to obtain prognostic and diagnostic information for patient with NSCLC.
  • The methods of the present disclosure generally involve obtaining from a patient relative expression data, at the DNA, mRNA, or protein level, for each of the fifteen, and optional additional, genes, processing the data and comparing the resulting information to one or more reference values. Relative expression levels are expression data normalized according to techniques known to those skilled in the art. Expression data may be normalized with respect to one or more genes with invariant expression, such as “housekeeping” genes. In some embodiments, expression data may be processed using standard techniques, such as transformation to a z-score, and/or software tools, such as RMAexpress v0.3.
  • In one aspect, a multi-gene signature is provided for prognosing or classifying patients with lung cancer. In some embodiments, a fifteen-gene signature is provided, comprising reference values for each of fifteen different genes based on relative expression data for each gene from a historical data set with a known outcome, such as good or poor survival, and/or known treatment, such as adjuvant chemotherapy. In one embodiment, four reference values are provided for each of the fifteen genes listed in Table 4. In one embodiment, the reference values for each of the fifteen genes are principal component values set forth in Table 10.
  • In some embodiments, a sixteen-, seventeen-, or eighteen-gene signature comprises reference values for each of sixteen, seventeen, or eighteen different genes based on relative expression data for each gene from a historical data set with a known outcome and/or known treatment. In some embodiments, reference values are provided for one, two, three genes in addition to those listed in Table 4, and the genes are selected from those listed in Table 3. In some embodiments, a single reference value for each gene is provided.
  • In one aspect, relative expression data from a patient are combined with the gene-specific reference values on a gene-by-gene basis for each of the fifteen, and optional additional, genes, to generate a test value which allows prognosis or therapy recommendation. In some embodiments, relative expression data are subjected to an algorithm that yields a single test value, or combined score, which is then compared to a control value obtained from the historical expression data for a patient or pool of patients. In some embodiments, the control value is a numerical threshold for predicting outcomes, for example good and poor outcome, or making therapy recommendations, for example adjuvant therapy in addition to surgical resection or surgical resection alone. In some embodiments, a test value or combined score greater than the control value is predictive, for example, of high risk (poor outcome) or benefit from adjuvant therapy, whereas a combined score falling below the control value is predictive, for example, of low risk (good outcome) or lack of benefit from adjuvant therapy.
  • In one embodiment, the combined score is calculated from relative expression data multiplied by reference values, determined from historical data, for each gene. Accordingly, the combined score may be calculated using the algorithm of Formula I below:

  • Combined score=0.557×PC1+0.328×PC2+0.43×PC3+0.335×PC4
  • Where PC1 is the sum of the relative expression level for each gene in a multi-gene signature multiplied by a first principal component for each gene in the multi-gene signature, PC2 is the sum of the relative expression level for each gene multiplied by a second principal component for each gene, PC3 is the sum of the relative expression level for each gene multiplied by a third principal component for each gene, and PC4 is the sum of the relative expression level for each gene multiplied by a fourth principal component for each gene. In some embodiments, the combined score is referred to as a risk score. A risk score for a subject can be calculated by applying Formula I to relative expression data from a test sample obtained from the subject.
  • In some embodiments, PC1 is the sum of the relative expression level for each gene provided in Table 4 multiplied by a first principal component for each gene, respectively, as set forth in Table 10; PC2 is the sum of the relative expression level for each gene provided in Table 4 multiplied by a second principal component for each gene, respectively, as set forth in Table 10; PC3 is the sum of the relative expression level for each gene provided in Table 4 multiplied by a third principal component for each gene, respectively, as set forth in Table 10; and PC4 is the sum of the relative expression level for each gene provided in Table 4 multiplied by a fourth principal component for each gene, respectively, as set forth in Table 10.
  • The present inventors have identified a gene signature that is prognostic for survival as well as predictive for benefit from adjuvant chemotherapy.
  • Accordingly in one embodiment, the application provides a method of prognosing or classifying a subject with non-small cell lung cancer comprising the steps:
      • a. determining the expression of fifteen biomarkers in a test sample from the subject, wherein the biomarkers correspond to genes in Table 4, and
      • b. comparing the expression of the fifteen biomarkers in the test sample with expression of the fifteen biomarkers in a control sample,
        wherein a difference or a similarity in the expression of the fifteen biomarkers between the control and the test sample is used to prognose or classify the subject with NSCLC into a poor survival group or a good survival group.
  • In an aspect, the application provides a method of predicting prognosis in a subject with non-small cell lung cancer comprising the steps:
      • a. obtaining a subject biomarker expression profile in a sample of the subject;
      • b. obtaining a biomarker reference expression profile associated with a prognosis, wherein the subject biomarker expression profile and the biomarker reference expression profile each have fifteen values, each value representing the expression level of a biomarker, wherein each biomarker corresponds to one gene in Table 4; and
      • c. selecting the biomarker reference expression profile most similar to the subject biomarker expression profile, to thereby predict a prognosis for the subject.
  • In another aspect, the prognoses and classifying methods of the application can be used to select treatment. For example, the methods can be used to select or identify subjects who might benefit from adjuvant chemotherapy. Accordingly, in one embodiment, the application provides a method of selecting a therapy for a subject with NSCLC, comprising the steps:
      • a. classifying the subject with NSCLC into a poor survival group or a good survival group according to the method of the application; and
      • b. selecting adjuvant chemotherapy for the poor survival group or no adjuvant chemotherapy for the good survival group.
  • In another embodiment, the application provides a method of selecting a therapy for a subject with NSCLC, comprising the steps:
      • a. determining the expression of fifteen biomarkers in a test sample from the subject, wherein the fifteen biomarkers correspond to the fifteen genes in Table 4;
      • b. comparing the expression of the fifteen biomarkers in the test sample with the fifteen biomarkers in a control sample;
      • c. classifying the subject in a poor survival group or a good survival group, wherein a difference or a similarity in the expression of the fifteen biomarkers between the control sample and the test sample is used to classify the subject into a poor survival group or a good survival group;
      • d. selecting adjuvant chemotherapy if the subject is classified in the poor survival group and selecting no adjuvant chemotherapy if the subject is classified in the good survival group.
  • Another aspect of the application provides compositions useful for use with the methods described herein.
  • The application also provides for kits used to prognose or classify a subject with NSCLC into a good survival group or a poor survival group or for selecting therapy for a subject with NSCLC that includes detection agents that can detect the expression products of the biomarkers.
  • In one aspect, the present disclosure provides kits useful for carrying out the diagnostic and prognostic tests described herein. The kits generally comprise reagents and compositions for obtaining relative expression data for the fifteen, and optional additional, genes described in Tables 3 and 4. As will be recognized by the skilled artisans, the contents of the kits will depend upon the means used to obtain the relative expression information.
  • Kits may comprise a labeled compound or agent capable of detecting protein product(s) or nucleic acid sequence(s) in a sample and means for determining the amount of the protein or mRNA in the sample (e.g., an antibody which binds the protein or a fragment thereof, or an oligonucleotide probe which binds to DNA or mRNA encoding the protein). Kits can also include instructions for interpreting the results obtained using the kit.
  • In some embodiments, the kits are oligonucleotide-based kits, which may comprise, for example: (1) an oligonucleotide, e.g., a detectably labeled oligonucleotide, which hybridizes to a nucleic acid sequence encoding a marker protein or (2) a pair of primers useful for amplifying a marker nucleic acid molecule. Kits may also comprise, e.g., a buffering agent, a preservative, or a protein stabilizing agent. The kits can further comprise components necessary for detecting the detectable label (e.g., an enzyme or a substrate). The kits can also contain a control sample or a series of control samples which can be assayed and compared to the test sample. Each component of a kit can be enclosed within an individual container and all of the various containers can be within a single package, along with instructions for interpreting the results of the assays performed using the kit.
  • In some embodiments, the kits are antibody-based kits, which may comprise, for example: (1) a first antibody (e.g., attached to a solid support) which binds to a marker protein; and, optionally, (2) a second, different antibody which binds to either the protein or the first antibody and is conjugated to a detectable label.
  • A further aspect provides computer implemented products, computer readable mediums and computer systems that are useful for the methods described herein.
  • Other features and advantages of the present invention will become apparent from the following detailed description. It should be understood, however, that the detailed description and the specific examples while indicating preferred embodiments of the invention are given by way of illustration only, since various changes and modifications within the spirit and scope of the invention will become apparent to those skilled in the art from this detailed description.
    • BRIEF DESCRIPTION OF THE DRAWINGS
  • The invention will now be described in relation to the drawings in which:
  • FIG. 1 shows the derivation and testing of the prognostic signature.
  • FIG. 2 shows the survival outcome based on the 15-gene signature in training and test sets.
  • FIG. 3 shows a comparison of chemotherapy vs. observation in low and high risk patients with microarray data.
  • FIG. 4 shows a consort diagram for microarray study of BR. 10 patients.
  • FIG. 5 shows the effect of adjuvant chemotherapy in microarray profiled patients.
  • FIG. 6 shows the effect of microarray batch processing at 2 different times. The samples were profiled in 2 batches at 2 times (January 2004 and June 2005). Unsupervised clustering shows that the expression patterns of these two batches differed significantly with samples arrayed on January 2004 aggregated in cluster 1 (93%) and samples arrayed on June 2005 in cluster 2 (73%).
    •  DETAILED DESCRIPTION OF THE INVENTION
  • The application relates to 15 biomarkers that form a 15-gene signature, and provides methods, compositions, computer implemented products, detection agents and kits for prognosing or classifying a subject with non-small cell lung cancer (NSCLC) and for determining the benefit of adjuvant chemotherapy.
  • The term “biomarker” as used herein refers to a gene that is differentially expressed in individuals with non-small cell lung cancer (NSCLC) according to prognosis and is predictive of different survival outcomes and of the benefit of adjuvant chemotherapy. In some embodiments, a 15-gene signature comprises 15 biomarker genes listed in Table 4. Optional additional biomarkers for a 16-, 17-, or 18-gene signature may be selected from the genes listed in Table 3.
  • Accordingly, one aspect of the invention is a method of prognosing or classifying a subject with non-small cell lung cancer, comprising the steps:
      • a. determining the expression of fifteen biomarkers in a test sample from the subject, wherein the biomarkers correspond to genes in Table 4, and
      • b. comparing the expression of the fifteen biomarkers in the test sample with expression of the fifteen biomarkers in a control sample,
      • wherein a difference or a similarity in the expression of the fifteen biomarkers between the control and the test sample is used to prognose or classify the subject with NSCLC into a poor survival group or a good survival group.
  • In another aspect, the application provides a method of predicting prognosis in a subject with non-small cell lung cancer (NSCLC) comprising the steps:
      • a. obtaining a subject biomarker expression profile in a sample of the subject;
      • b. obtaining a biomarker reference expression profile associated with a prognosis, wherein the subject biomarker expression profile and the biomarker reference expression profile each have fifteen values, each value representing the expression level of a biomarker, wherein each biomarker corresponds to a gene in Table 4; and
      • c. selecting the biomarker reference expression profile most similar to the subject biomarker expression profile, to thereby predict a prognosis for the subject.
  • The term “reference expression profile” as used herein refers to the expression of the 15 biomarkers or genes listed in Table 4 associated with a clinical outcome in a NSCLC patient. The reference expression profile comprises 15 values, each value representing the expression level of a biomarker, wherein each biomarker corresponds to one gene in Table 4. The reference expression profile is identified using one or more samples comprising tumor wherein the expression is similar between related samples defining an outcome class or group such as poor survival or good survival and is different to unrelated samples defining a different outcome class such that the reference expression profile is associated with a particular clinical outcome. The reference expression profile is accordingly a reference profile of the expression of the 15 genes in Table 4, to which the subject expression levels of the corresponding genes in a patient sample are compared in methods for determining or predicting clinical outcome.
  • As used herein, the term “control” refers to a specific value or dataset that can be used to prognose or classify the value e.g expression level or reference expression profile obtained from the test sample associated with an outcome class. In one embodiment, a dataset may be obtained from samples from a group of subjects known to have NSCLC and good survival outcome or known to have NSCLC and have poor survival outcome or known to have NSCLC and have benefited from adjuvant chemotherapy or known to have NSCLC and not have benefited from adjuvant chemotherapy. The expression data of the biomarkers in the dataset can be used to create a “control value” that is used in testing samples from new patients. A control value is obtained from the historical expression data for a patient or pool of patients with a known outcome. In some embodiments, the control value is a numerical threshold for predicting outcomes, for example good and poor outcome, or making therapy recommendations, for example adjuvant therapy in addition to surgical resection or surgical resection alone.
  • In some embodiments, the “control” is a predetermined value for the set of 15 biomarkers obtained from NSCLC patients whose biomarker expression values and survival times are known. Alternatively, the “control” is a predetermined reference profile for the set of fifteen biomarkers obtained from NSCLC patients whose survival times are known. Using values from known samples allows one to develop an algorithm for classifying new patient samples into good and poor survival groups as described in the Example.
  • Accordingly, in one embodiment, the control is a sample from a subject known to have NSCLC and good survival outcome. In another embodiment, the control is a sample from a subject known to have NSCLC and poor survival outcome.
  • A person skilled in the art will appreciate that the comparison between the expression of the biomarkers in the test sample and the expression of the biomarkers in the control will depend on the control used. For example, if the control is from a subject known to have NSCLC and poor survival, and there is a difference in expression of the biomarkers between the control and test sample, then the subject can be prognosed or classified in a good survival group. If the control is from a subject known to have NSCLC and good survival, and there is a difference in expression of the biomarkers between the control and test sample, then the subject can be prognosed or classified in a poor survival group. For example, if the control is from a subject known to have NSCLC and good survival, and there is a similarity in expression of the biomarkers between the control and test sample, then the subject can be prognosed or classified in a good survival group. For example, if the control is from a subject known to have NSCLC and poor survival, and there is a similarity in expression of the biomarkers between the control and test sample, then the subject can be prognosed or classified in a poor survival group.
  • As used herein, a “reference value” refers to a gene-specific coefficient derived from historical expression data. The multi-gene signatures of the present disclosure comprise gene-specific reference values. In some embodiments, the multi-gene signature comprises one reference value for each gene in the signature. In some embodiments, the multi-gene signature comprises four reference values for each gene in the signature. In some embodiments, the reference values are the first four components derived from principal component analysis for each gene in the signature.
  • The term “differentially expressed” or “differential expression” as used herein refers to a difference in the level of expression of the biomarkers that can be assayed by measuring the level of expression of the products of the biomarkers, such as the difference in level of messenger RNA transcript expressed or proteins expressed of the biomarkers. In a preferred embodiment, the difference is statistically significant. The term “difference in the level of expression” refers to an increase or decrease in the measurable expression level of a given biomarker as measured by the amount of messenger RNA transcript and/or the amount of protein in a sample as compared with the measurable expression level of a given biomarker in a control. In one embodiment, the differential expression can be compared using the ratio of the level of expression of a given biomarker or biomarkers as compared with the expression level of the given biomarker or biomarkers of a control, wherein the ratio is not equal to 1.0. For example, an RNA or protein is differentially expressed if the ratio of the level of expression in a first sample as compared with a second sample is greater than or less than 1.0. For example, a ratio of greater than 1, 1.2, 1.5, 1.7, 2, 3, 3, 5, 10, 15, 20 or more, or a ratio less than 1, 0.8, 0.6, 0.4, 0.2, 0.1, 0.05, 0.001 or less. In another embodiment the differential expression is measured using p-value. For instance, when using p-value, a biomarker is identified as being differentially expressed as between a first sample and a second sample when the p-value is less than 0.1, preferably less than 0.05, more preferably less than 0.01, even more preferably less than 0.005, the most preferably less than 0.001.
  • The term “similarity in expression” as used herein means that there is no or little difference in the level of expression of the biomarkers between the test sample and the control or reference profile. For example, similarity can refer to a fold difference compared to a control. In a preferred embodiment, there is no statistically significant difference in the level of expression of the biomarkers.
  • The term “most similar” in the context of a reference profile refers to a reference profile that is associated with a clinical outcome that shows the greatest number of identities and/or degree of changes with the subject profile.
  • The term “prognosis” as used herein refers to a clinical outcome group such as a poor survival group or a good survival group associated with a disease subtype which is reflected by a reference profile such as a biomarker reference expression profile or reflected by an expression level of the fifteen biomarkers disclosed herein. The prognosis provides an indication of disease progression and includes an indication of likelihood of death due to lung cancer. In one embodiment the clinical outcome class includes a good survival group and a poor survival group.
  • The term “prognosing or classifying” as used herein means predicting or identifying the clinical outcome group that a subject belongs to according to the subject's similarity to a reference profile or biomarker expression level associated with the prognosis. For example, prognosing or classifying comprises a method or process of determining whether an individual with NSCLC has a good or poor survival outcome, or grouping an individual with NSCLC into a good survival group or a poor survival group.
  • The term “good survival” as used herein refers to an increased chance of survival as compared to patients in the “poor survival” group. For example, the biomarkers of the application can prognose or classify patients into a “good survival group”. These patients are at a lower risk of death after surgery.
  • The term “poor survival” as used herein refers to an increased risk of death as compared to patients in the “good survival” group. For example, biomarkers or genes of the application can prognose or classify patients into a “poor survival group”. These patients are at greater risk of death from surgery.
  • Accordingly, in one embodiment, the biomarker reference expression profile comprises a poor survival group. In another embodiment, the biomarker reference expression profile comprises a good survival group.
  • The term “subject” as used herein refers to any member of the animal kingdom, preferably a human being that has NSCLC or that is suspected of having NSCLC.
  • NSCLC patients are classified into stages, which are used to determine therapy. Staging classification testing may include any or all of history, physical examination, routine laboratory evaluations, chest x-rays, and chest computed tomography scans or positron emission tomography scans with infusion of contrast materials. For example, stage I includes cancer in the lung, but has not spread to adjacent lymph nodes or outside the chest. Stage I is divided into two categories based on the size of the tumor (IA and IB). Stage II includes cancer located in the lung and proximal lymph nodes. Stage II is divided into 2 categories based on the size of tumor and nodal status (IIA and IIB). Stage III includes cancer located in the lung and the lymph nodes. Stage III is divided into 2 categories based on the size of tumor and nodal status (IIIA and IIIB). Stage IV includes cancer that has metastasized to distant locations. The term “early stage NSCLC” includes patients with Stage I to IIIA NSCLC. These patients are treated primarily by complete surgical resection.
  • In an aspect, a multi-gene signature is prognostic of patient outcome and/or response to adjuvant chemotherapy. In some embodiments, a minimal signature for 15 genes is provided. In one embodiment, the signature comprises reference values for each of the 15 genes listed in Table 4. In some embodiments, the 15-gene signature is associated with the early stages of NSCLC. Accordingly, in one embodiment, the subject has stage I NSCLC. In another embodiment, the subject has stage II NSCLC. In some embodiments, a 16-, 17-, 18-gene signature is prognostic of patient outcome and/or response to adjuvant chemotherapy. In some embodiments, the signature comprises reference values for one, two or three genes selected from those listed in Table 3, in addition to reference values for each of the genes listed in Table 4. In some embodiments, the additional one, two, or three genes are selected from RGS4, UGT2B4, and MCF2 listed in Table 3.
  • In some embodiments, the multi-gene signature comprises four coefficients, or reference values, for each gene in the signature. In one embodiment, the four coefficients are the first four principal components derived from principal component analysis described in Example 1 below. In one embodiment, the 15-gene signature comprises the principal component values listed in Table 10 below. In some embodiments, a 16-, 17-, 18-gene signature comprises coefficients for a sixteenth, seventeenth, and eighteenth gene, respectively, derived from principal component analysis as described in Example 1 below. In some embodiments, the coefficients for a sixteenth, seventeenth, and eighteenth gene, respectively, are the first four principal components derived according to Example 1. In some embodiments, the additional one, two, or three genes are selected from RGS4, UGT2B4, and MCF2 listed in Table 3.
  • The term “test sample” as used herein refers to any cancer-affected fluid, cell or tissue sample from a subject which can be assayed for biomarker expression products and/or a reference expression profile, e.g. genes differentially expressed in subjects with NSCLC according to survival outcome.
  • The phrase “determining the expression of biomarkers” as used herein refers to determining or quantifying RNA or proteins expressed by the biomarkers. The term “RNA” includes mRNA transcripts, and/or specific spliced variants of mRNA. The terms “RNA product of the biomarker,” “biomarker RNA,” or “target RNA” as used herein refers to RNA transcripts transcribed from the biomarkers and/or specific spliced variants. In the case of “protein”, it refers to proteins translated from the RNA transcripts transcribed from the biomarkers. The term “protein product of the biomarker” or “biomarker protein” refers to proteins translated from RNA products of the biomarkers.
  • A person skilled in the art will appreciate that a number of methods can be used to detect or quantify the level of RNA products of the biomarkers within a sample, including arrays, such as microarrays, RT-PCR (including quantitative PCR), nuclease protection assays and Northern blot analyses. Any analytical procedure capable of permitting specific and quantifiable (or semi-quantifiable) detection of the and, optionally, additional biomarkers may be used in the methods herein presented, such as the microarray methods set forth herein, and methods known to those skilled in the art.
  • Accordingly, in one embodiment, the biomarker expression levels are determined using arrays, optionally microarrays, RT-PCR, optionally quantitative RT-PCR, nuclease protection assays or Northern blot analyses.
  • In some embodiments, the biomarker expression levels are determined by using an array. cDNA microarrays consist of multiple (usually thousands) of different cDNAs spotted (usually using a robotic spotting device) onto known locations on a solid support, such as a glass microscope slide. Microarrays for use in the methods described herein comprise a solid substrate onto which the probes are covalently or non-covalently attached. The cDNAs are typically obtained by PCR amplification of plasmid library inserts using primers complementary to the vector backbone portion of the plasmid or to the gene itself for genes where sequence is known. PCR products suitable for production of microarrays are typically between 0.5 and 2.5 kB in length. In a typical microarray experiment, RNA (either total RNA or poly A RNA) is isolated from cells or tissues of interest and is reverse transcribed to yield cDNA. Labeling is usually performed during reverse transcription by incorporating a labeled nucleotide in the reaction mixture. A microarray is then hybridized with labeled RNA, and relative expression levels calculated based on the relative concentrations of cDNA molecules that hybridized to the cDNAs represented on the microarray. Microarray analysis can be performed by commercially available equipment, following manufactuer's protocols, such as by using Affymetrix GeneChip technology, Agilent Technologies cDNA microarrays, Illumina Whole-Genome DASL array assays, or any other comparable microarray technology.
  • In some embodiments, probes capable of hybridizing to one or more biomarker RNAs or cDNAs are attached to the substrate at a defined location (“addressable array”). Probes can be attached to the substrate in a wide variety of ways, as will be appreciated by those in the art. In some embodiments, the probes are synthesized first and subsequently attached to the substrate. In other embodiments, the probes are synthesized on the substrate. In some embodiments, probes are synthesized on the substrate surface using techniques such as photopolymerization and photolithography.
  • In some embodiments, microarrays are utilized in a RNA-primed, Array-based Klenow Enzyme (“RAKE”) assay. See Nelson, P. T. et al. (2004) Nature Methods 1(2):1-7; Nelson, P. T. et al. (2006) RNA 12(2):1-5, each of which is incorporated herein by reference in its entirety. In these embodiments, total RNA is isolated from a sample. Optionally, small RNAs can be further purified from the total RNA sample. The RNA sample is then hybridized to DNA probes immobilized at the 5′-end on an addressable array. The DNA probes comprise a base sequence that is complementary to a target RNA of interest, such as one or more biomarker RNAs capable of specifically hybridizing to a nucleic acid comprising a sequence that is identically present in one of the genes listed in Table 4 under standard hybridization conditions.
  • In some embodiments, the addressable array comprises DNA probes for no more than the 15 genes listed in Table 4. In some embodiments, the addressable array comprises DNA probes for each of the 15 genes listed in Table 4 and optionally, no more than one, two, or three additional genes selected from those listed in Table 3. In one embodiment, the addressable array comprises DNA probes for each of the 15 genes listed in Table 4 and DNA probes for one, two, or all three of RGS4, UGT2B4, and MCF2 listed in Table 3.
  • In some embodiments, quantitation of biomarker RNA expression levels requires assumptions to be made about the total RNA per cell and the extent of sample loss during sample preparation. In some embodiments, the addressable array comprises DNA probes for each of the 15 genes listed in Table 4 and, optionally, one, two, three, or four housekeeping genes. In one embodiment, the addressable array comprises DNA probes for each of the 15 genes listed in Table 4, one, two, three, or four housekeeping genes, and, additionally, no more than one, two, three or four additional genes selected from those listed in Table 3.
  • In some embodiments, expression data are pre-processed to correct for variations in sample preparation or other non-experimental variables affecting expression measurements. For example, background adjustment, quantile adjustment, and summarization may be performed on microarray data, using standard software programs such as RMAexpress v0.3, followed by centering of the data to the mean and scaling to the standard deviation.
  • After the sample is hybridized to the array, it is exposed to exonuclease I to digest any unhybridized probes. The Klenow fragment of DNA polymerase I is then applied along with biotinylated dATP, allowing the hybridized biomarker RNAs to act as primers for the enzyme with the DNA probe as template. The slide is then washed and a streptavidin-conjugated fluorophore is applied to detect and quantitate the spots on the array containing hybridized and Klenow-extended biomarker RNAs from the sample.
  • In some embodiments, the RNA sample is reverse transcribed using a biotin/poly-dA random octamer primer. The RNA template is digested and the biotin-containing cDNA is hybridized to an addressable microarray with bound probes that permit specific detection of biomarker RNAs. In typical embodiments, the microarray includes at least one probe comprising at least 8, at least 9, at least 10, at least 11, at least 12, at least 13, at least 14, at least 15, at least 16, at least 17, at least 18, at least 19, even at least 20, 21, 22, 23, or 24 contiguous nucleotides identically present in each of the genes listed in Table 4. After hybridization of the cDNA to the microarray, the microarray is exposed to a streptavidin-bound detectable marker, such as a fluorescent dye, and the bound cDNA is detected. See Liu C. G. et al. (2008) Methods 44:22-30, which is incorporated herein by reference in its entirety.
  • In one embodiment, the array is a U133A chip from Affymetrix. In another embodiment, a plurality of nucleic acid probes that are complementary or hybridizable to an expression product of the genes listed in Table 4 are used on the array. In a particular embodiment, the probe target sequences are listed in Table 9. In some embodiments, the probe target sequences are selected from SEQ ID NO: 3, 11-15, 22, 26, 35, 49, 78, 85, 130, 133, and 169. In one embodiment, fifteen probes are used, each probe hybridizable to a different target sequence selected from SEQ ID NO: 3, 11-15, 22, 26, 35, 49, 78, 85, 130, 133, and 169. In some embodiments, a plurality of nucleic acid probes that are complementary or hybridizable to an expression product of some or all the genes listed in Table 3 are used on the array. In some embodiments, the probe target sequences are selected from those listed in Table 11. In some embodiments, the probe target sequences are selected from SEQ ID NO:1-172.
  • The term “nucleic acid” includes DNA and RNA and can be either double stranded or single stranded.
  • The term “hybridize” or “hybridizable” refers to the sequence specific non-covalent binding interaction with a complementary nucleic acid. In a preferred embodiment, the hybridization is under high stringency conditions. Appropriate stringency conditions which promote hybridization are known to those skilled in the art, or can be found in Current Protocols in Molecular Biology, John Wiley & Sons, N.Y. (1989), 6.3.1 6.3.6. For example, 6.0× sodium chloride/sodium citrate (SSC) at about 45° C., followed by a wash of 2.0×SSC at 50° C. may be employed.
  • The term “probe” as used herein refers to a nucleic acid sequence that will hybridize to a nucleic acid target sequence. In one example, the probe hybridizes to an RNA product of the biomarker or a nucleic acid sequence complementary thereof. The length of probe depends on the hybridization conditions and the sequences of the probe and nucleic acid target sequence. In one embodiment, the probe is at least 8, 10, 15, 20, 25, 50, 75, 100, 150, 200, 250, 400, 500 or more nucleotides in length.
  • In some embodiments, compositions are provided that comprise at least one biomarker or target RNA-specific probe. The term “target RNA-specific probe” encompasses probes that have a region of contiguous nucleotides having a sequence that is either (i) identically present in one of the genes listed in Tables 3 or 4, or (ii) complementary to the sequence of a region of contiguous nucleotides found in one of the genes listed in Tables 3 or 4, where “region” can comprise the full length sequence of any one of the genes listed in Tables 3 or 4, a complementary sequence of the full length sequence of any one of the genes listed in Tables 3 or 4, or a subsequence thereof.
  • In some embodiments, target RNA-specific probes consist of deoxyribonucleotides. In other embodiments, target RNA-specific probes consist of both deoxyribonucleotides and nucleotide analogs. In some embodiments, biomarker RNA-specific probes comprise at least one nucleotide analog which increases the hybridization binding energy. In some embodiments, a target RNA-specific probe in the compositions described herein binds to one biomarker RNA in the sample.
  • In some embodiments, more than one probe specific for a single biomarker RNA is present in the compositions, the probes capable of binding to overlapping or spatially separated regions of the biomarker RNA.
  • It will be understood that in some embodiments in which the compositions described herein are designed to hybridize to cDNAs reverse transcribed from biomarker RNAs, the composition comprises at least one target RNA-specific probe comprising a sequence that is identically present in a biomarker RNA (or a subsequence thereof).
  • In some embodiments, a biomarker RNA is capable of specifically hybridizing to at least one probe comprising a base sequence that is identically present in one of the genes listed in Table 4. In some embodiments, a biomarker RNA is capable of specifically hybridizing to at least one nucleic acid probe comprising a sequence that is identically present in one of the genes listed in Table 3. In some embodiments, a target RNA is capable of specifically hybridizing to at least one nucleic acid probe, and comprises a sequence that is identical to a sequence selected from SEQ ID NO:1-172, or a sequence listed in Table 11. In some embodiments, a target RNA is capable of specifically hybridizing to at least one nucleic acid probe, and comprises a sequence that is identical to a sequence listed in Table 9. In some embodiments, a target RNA is capable of specifically hybridizing to at least one nucleic acid probe, and comprises a sequence that is identical to a sequence selected from SEQ ID NO: 3, 11-15, 22, 26, 35, 49, 78, 85, 130, 133, and 169. In some embodiments, a biomarker RNA is capable of specifically hybridizing to at least one probe comprising a base sequence that is identically present in one of the genes listed in Table 4.
  • In some embodiments, the composition comprises a plurality of target or biomarker RNA-specific probes each comprising a region of contiguous nucleotides comprising a base sequence that is identically present in one or more of the genes listed in Table 4, or in a subsequence thereof. In some embodiments, the composition comprises a plurality of target or biomarker RNA-specific probes each comprising a region of contiguous nucleotides comprising a base sequence that is complementary to a sequence listed in Table 9. In some embodiments, the composition comprises a plurality of target RNA-specific probes each comprising a region of contiguous nucleotides comprising a base sequence that is complementary to a sequence selected from SEQ ID NO: 3, 11-15, 22, 26, 35, 49, 78, 85, 130, 133, and 169.
  • As used herein, the terms “complementary” or “partially complementary” to a biomarker or target RNA (or target region thereof), and the percentage of “complementarity” of the probe sequence to that of the biomarker RNA sequence is the percentage “identity” to the reverse complement of the sequence of the biomarker RNA. In determining the degree of “complementarity” between probes used in the compositions described herein (or regions thereof) and a biomarker RNA, such as those disclosed herein, the degree of “complementarity” is expressed as the percentage identity between the sequence of the probe (or region thereof) and the reverse complement of the sequence of the biomarker RNA that best aligns therewith. The percentage is calculated by counting the number of aligned bases that are identical as between the 2 sequences, dividing by the total number of contiguous nucleotides in the probe, and multiplying by 100.
  • In some embodiments, the microarray comprises probes comprising a region with a base sequence that is fully complementary to a target region of a biomarker RNA. In other embodiments, the microarray comprises probes comprising a region with a base sequence that comprises one or more base mismatches when compared to the sequence of the best-aligned target region of a biomarker RNA.
  • As noted above, a “region” of a probe or biomarker RNA, as used herein, may comprise or consist of 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29 or more contiguous nucleotides from a particular gene or a complementary sequence thereof. In some embodiments, the region is of the same length as the probe or the biomarker RNA. In other embodiments, the region is shorter than the length of the probe or the biomarker RNA.
  • In some embodiments, the microarray comprises fifteen probes each comprising a region of at least 10 contiguous nucleotides, such as at least 11 contiguous nucleotides, such as at least 13 contiguous nucleotides, such as at least 14 contiguous nucleotides, such as at least 15 contiguous nucleotides, such as at least 16 contiguous nucleotides, such as at least 17 contiguous nucleotides, such as at least 18 contiguous nucleotides, such as at least 19 contiguous nucleotides, such as at least 20 contiguous nucleotides, such as at least 21 contiguous nucleotides, such as at least 22 contiguous nucleotides, such as at least 23 contiguous nucleotides, such as at least 24 contiguous nucleotides, such as at least 25 contiguous nucleotides with a base sequence that is identically present in one of the genes listed in Table 4.
  • In some embodiments, the microarray component comprises fifteen probes each comprising a region with a base sequence that is identically present in each of the genes listed in Table 4. In some embodiments, the microarray comprises sixteen, seventeen, eighteen probes, each of which comprises a region with a base sequence that is identically present in each of the genes listed in Table 4 and, optionally, one, two, or three of the genes listed in Table 3. In one embodiment, the one, two, or three genes from Table 3 are selected from RGS4, UGT2B4, and MCF2.
  • In another embodiment, the biomarker expression levels are determined by using quantitative RT-PCR. RT-PCR is one of the most sensitive, flexible, and quantitative methods for measuring expression levels. The first step is the isolation of mRNA from a target sample. The starting material is typically total RNA isolated from human tumors or tumor cell lines. General methods for mRNA extraction are well known in the art and are disclosed in standard textbooks of molecular biology, including Ausubel et al., Current Protocols of Molecular Biology, John Wiley and Sons (1997). Methods for RNA extraction from paraffin embedded tissues are disclosed, for example, in Rupp and Locker, Lab Invest. 56:A67 (1987), and De Andres et al., BioTechniques 18:42044 (1995). In particular, RNA isolation can be performed using purification kit, buffer set and protease from commercial manufacturers, such as Qiagen, according to the manufacturer's instructions. For example, total RNA from cells in culture can be isolated using Qiagen RNeasy mini-columns. Numerous RNA isolation kits are commercially available.
  • In some embodiments, the primers used for quantitative RT-PCR comprise a forward and reverse primer for each gene listed in Table 4. In one embodiment, the primers used for quantitative RT-PCR are listed in Table 7. In one embodiment, primers comprising sequences identical to the sequences of SEQ ID NO: 173-202 are used for quantitative RT-PCR, wherein primers with sequences identifical to SEQ ID NO:173-187 are forward primers and primers with sequences identifical to SEQ ID NO:188-202 are reverse primers.
  • In some embodiments the analytical method used for detecting at least one biomarker RNA in the methods set forth herein includes real-time quantitative RT-PCR. See Chen, C. et al. (2005) Nucl. Acids Res. 33:e179, which is incorporated herein by reference in its entirety. Although PCR can use a variety of thermostable DNA-dependent DNA polymerases, it typically employs the Taq DNA polymerase, which has a 5′-3′ nuclease activity but lacks a 3′-5′ proofreading endonuclease activity. In some embodiments, RT-PCR is done using a TaqMan® assay sold by Applied Biosystems, Inc. In a first step, total RNA is isolated from the sample. In some embodiments, the assay can be used to analyze about 10 ng of total RNA input sample, such as about 9 ng of input sample, such as about 8 ng of input sample, such as about 7 ng of input sample, such as about 6 ng of input sample, such as about 5 ng of input sample, such as about 4 ng of input sample, such as about 3 ng of input sample, such as about 2 ng of input sample, and even as little as about 1 ng of input sample containing RNA.
  • The TaqMan® assay utilizes a stem-loop primer that is specifically complementary to the 3′-end of a biomarker RNA. The step of hybridizing the stem-loop primer to the biomarker RNA is followed by reverse transcription of the biomarker RNA template, resulting in extension of the 3′ end of the primer. The result of the reverse transcription step is a chimeric (DNA) amplicon with the step-loop primer sequence at the 5′ end of the amplicon and the cDNA of the biomarker RNA at the 3′ end. Quantitation of the biomarker RNA is achieved by RT-PCR using a universal reverse primer comprising a sequence that is complementary to a sequence at the 5′ end of all stem-loop biomarker RNA primers, a biomarker RNA-specific forward primer, and a biomarker RNA sequence-specific TaqMan® probe.
  • The assay uses fluorescence resonance energy transfer (“FRET”) to detect and quantitate the synthesized PCR product. Typically, the TaqMan® probe comprises a fluorescent dye molecule coupled to the 5′-end and a quencher molecule coupled to the 3′-end, such that the dye and the quencher are in close proximity, allowing the quencher to suppress the fluorescence signal of the dye via FRET. When the polymerase replicates the chimeric amplicon template to which the TaqMan® probe is bound, the 5′-nuclease of the polymerase cleaves the probe, decoupling the dye and the quencher so that FRET is abolished and a fluorescence signal is generated. Fluorescence increases with each RT-PCR cycle proportionally to the amount of probe that is cleaved.
  • In some embodiments, quantitation of the results of RT-PCR assays is done by constructing a standard curve from a nucleic acid of known concentration and then extrapolating quantitative information for biomarker RNAs of unknown concentration. In some embodiments, the nucleic acid used for generating a standard curve is an RNA of known concentration. In some embodiments, the nucleic acid used for generating a standard curve is a purified double-stranded plasmid DNA or a single-stranded DNA generated in vitro.
  • In some embodiments, where the amplification efficiencies of the biomarker nucleic acids and the endogenous reference are approximately equal, quantitation is accomplished by the comparative Ct (cycle threshold, e.g., the number of PCR cycles required for the fluorescence signal to rise above background) method. Ct values are inversely proportional to the amount of nucleic acid target in a sample. In some embodiments, Ct values of the target RNA of interest can be compared with a control or calibrator, such as RNA from normal tissue. In some embodiments, the Ct values of the calibrator and the target RNA samples of interest are normalized to an appropriate endogenous housekeeping gene (see above).
  • In addition to the TaqMan® assays, other RT-PCR chemistries useful for detecting and quantitating PCR products in the methods presented herein include, but are not limited to, Molecular Beacons, Scorpion probes and SYBR Green detection.
  • In some embodiments, Molecular Beacons can be used to detect and quantitate PCR products. Like TaqMan® probes, Molecular Beacons use FRET to detect and quantitate a PCR product via a probe comprising a fluorescent dye and a quencher attached at the ends of the probe. Unlike TaqMan® probes, Molecular Beacons remain intact during the PCR cycles. Molecular Beacon probes form a stem-loop structure when free in solution, thereby allowing the dye and quencher to be in close enough proximity to cause fluorescence quenching. When the Molecular Beacon hybridizes to a target, the stem-loop structure is abolished so that the dye and the quencher become separated in space and the dye fluoresces. Molecular Beacons are available, e.g., from Gene Link™ (see http://www.genelink.com/newsite/products/mbintro.asp).
  • In some embodiments, Scorpion probes can be used as both sequence-specific primers and for PCR product detection and quantitation. Like Molecular Beacons, Scorpion probes form a stem-loop structure when not hybridized to a target nucleic acid. However, unlike Molecular Beacons, a Scorpion probe achieves both sequence-specific priming and PCR product detection. A fluorescent dye molecule is attached to the 5′-end of the Scorpion probe, and a quencher is attached to the 3′-end. The 3′ portion of the probe is complementary to the extension product of the PCR primer, and this complementary portion is linked to the 5′-end of the probe by a non-amplifiable moiety. After the Scorpion primer is extended, the target-specific sequence of the probe binds to its complement within the extended amplicon, thus opening up the stem-loop structure and allowing the dye on the 5′-end to fluoresce and generate a signal. Scorpion probes are available from, e.g, Premier Biosoft International (see http://www.premierbiosoft.com/tech_notes/Scorpion.html).
  • In some embodiments, RT-PCR detection is performed specifically to detect and quantify the expression of a single biomarker RNA. The biomarker RNA, in typical embodiments, is selected from a biomarker RNA capable of specifically hybridizing to a nucleic acid comprising a sequence that is identically present in one of the genes set forth in Table 4. In some embodiments, the biomarker RNA specifically hybridizes to a nucleic acid comprising a sequence that is identically present in at least one of the genes in Table 3.
  • In various other embodiments, RT-PCR detection is utilized to detect, in a single multiplex reaction, each of 15, each of 16, each of 17, even each of 18 biomarker RNAs. The biomarker RNAs, in some embodiments, are capable of specifically hybridizing to a nucleic acid comprising a sequence that is identically present in one of the fifteen genes listed in Table 4 and optionally one, two, or three additional genes listed in Table 3.
  • In some multiplex embodiments, a plurality of probes, such as TaqMan probes, each specific for a different RNA target, is used. In typical embodiments, each target RNA-specific probe is spectrally distinguishable from the other probes used in the same multiplex reaction.
  • In some embodiments, quantitation of RT-PCR products is accomplished using a dye that binds to double-stranded DNA products, such as SYBR Green. In some embodiments, the assay is the QuantiTect SYBR Green PCR assay from Qiagen. In this assay, total RNA is first isolated from a sample. Total RNA is subsequently poly-adenylated at the 3′-end and reverse transcribed using a universal primer with poly-dT at the 5′-end. In some embodiments, a single reverse transcription reaction is sufficient to assay multiple biomarker RNAs. RT-PCR is then accomplished using biomarker RNA-specific primers and an miScript Universal Primer, which comprises a poly-dT sequence at the 5′-end. SYBR Green dye binds non-specifically to double-stranded DNA and upon excitation, emits light. In some embodiments, buffer conditions that promote highly-specific annealing of primers to the PCR template (e.g., available in the QuantiTect SYBR Green PCR Kit from Qiagen) can be used to avoid the formation of non-specific DNA duplexes and primer dimers that will bind SYBR Green and negatively affect quantitation. Thus, as PCR product accumulates, the signal from SYBR green increases, allowing quantitation of specific products.
  • RT-PCR is performed using any RT-PCR instrumentation available in the art. Typically, instrumentation used in real-time RT-PCR data collection and analysis comprises a thermal cycler, optics for fluorescence excitation and emission collection, and optionally a computer and data acquisition and analysis software.
  • In some embodiments, the method of detectably quantifying one or more biomarker RNAs includes the steps of: (a) isolating total RNA; (b) reverse transcribing a biomarker RNA to produce a cDNA that is complementary to the biomarker RNA; (c) amplifying the cDNA from step (b); and (d) detecting the amount of a biomarker RNA with RT-PCR.
  • As described above, in some embodiments, the RT-PCR detection is performed using a FRET probe, which includes, but is not limited to, a TaqMan® probe, a Molecular beacon probe and a Scorpion probe. In some embodiments, the RT-PCR detection and quantification is performed with a TaqMan® probe, i.e., a linear probe that typically has a fluorescent dye covalently bound at one end of the DNA and a quencher molecule covalently bound at the other end of the DNA. The FRET probe comprises a base sequence that is complementary to a region of the cDNA such that, when the FRET probe is hybridized to the cDNA, the dye fluorescence is quenched, and when the probe is digested during amplification of the cDNA, the dye is released from the probe and produces a fluorescence signal. In such embodiments, the amount of biomarker RNA in the sample is proportional to the amount of fluorescence measured during cDNA amplification.
  • The TaqMan® probe typically comprises a region of contiguous nucleotides comprising a base sequence that is complementary to a region of a biomarker RNA or its complementary cDNA that is reverse transcribed from the biomarker RNA template (i.e., the sequence of the probe region is complementary to or identically present in the biomarker RNA to be detected) such that the probe is specifically hybridizable to the resulting PCR amplicon. In some embodiments, the probe comprises a region of at least 6 contiguous nucleotides having a base sequence that is fully complementary to or identically present in a region of a cDNA that has been reverse transcribed from a biomarker RNA template, such as comprising a region of at least 8 contiguous nucleotides, or comprising a region of at least 10 contiguous nucleotides, or comprising a region of at least 12 contiguous nucleotides, or comprising a region of at least 14 contiguous nucleotides, or even comprising a region of at least 16 contiguous nucleotides having a base sequence that is complementary to or identically present in a region of a cDNA reverse transcribed from a biomarker RNA to be detected.
  • Preferably, the region of the cDNA that has a sequence that is complementary to the TaqMan® probe sequence is at or near the center of the cDNA molecule. In some embodiments, there are independently at least 2 nucleotides, such as at least 3 nucleotides, such as at least 4 nucleotides, such as at least 5 nucleotides of the cDNA at the 5′-end and at the 3′-end of the region of complementarity.
  • In typical embodiments, all biomarker RNAs are detected in a single multiplex reaction. In these embodiments, each TaqMan® probe that is targeted to a unique cDNA is spectrally distinguishable when released from the probe. Thus, each biomarker RNA is detected by a unique fluorescence signal.
  • In some embodiments, expression levels may be represented by gene transcript numbers per nanogram of cDNA. To control for variability in cDNA quantity, integrity and the overall transcriptional efficiency of individual primers, RT-PCR data can be subjected to standardization and normalization against one or more housekeeping genes as has been previously described. See e.g., Rubie et al., Mol. Cell. Probes 19(2):101-9 (2005).
  • Appropriate genes for normalization in the methods described herein include those as to which the quantity of the product does not vary between between different cell types, cell lines or under different growth and sample preparation conditions. In some embodiments, endogenous housekeeping genes useful as normalization controls in the methods described herein include, but are not limited to, ACTB, BAT1, B2M, TBP, U6 snRNA, RNU44, RNU 48, and U47. In typical embodiments, the at least one endogenous housekeeping gene for use in normalizing the measured quantity of RNA is selected from ACTB, BAT1, B2M, TBP, U6 snRNA, U6 snRNA, RNU44, RNU 48, and U47. In some embodiments, normalization to the geometric mean of two, three, four or more housekeeping genes is performed. In some embodiments, one housekeeping gene is used for normalization. In some embodiments, two, three, four or more housekeeping genes are used for normalization.
  • In some embodiments, labels that can be used on the FRET probes include colorimetric and fluorescent labels such as Alexa Fluor dyes, BODIPY dyes, such as BODIPY FL; Cascade Blue; Cascade Yellow; coumarin and its derivatives, such as 7-amino-4-methylcoumarin, aminocoumarin and hydroxycoumarin; cyanine dyes, such as Cy3 and Cy5; eosins and erythrosins; fluorescein and its derivatives, such as fluorescein isothiocyanate; macrocyclic chelates of lanthanide ions, such as Quantum Dye™; Marina Blue; Oregon Green; rhodamine dyes, such as rhodamine red, tetramethylrhodamine and rhodamine 6G; Texas Red; fluorescent energy transfer dyes, such as thiazole orange-ethidium heterodimer; and, TOTAB.
  • Specific examples of dyes include, but are not limited to, those identified above and the following: Alexa Fluor 350, Alexa Fluor 405, Alexa Fluor 430, Alexa Fluor 488, Alexa Fluor 500. Alexa Fluor 514, Alexa Fluor 532, Alexa Fluor 546, Alexa Fluor 555, Alexa Fluor 568, Alexa Fluor 594, Alexa Fluor 610, Alexa Fluor 633, Alexa Fluor 647, Alexa Fluor 660, Alexa Fluor 680, Alexa Fluor 700, and, Alexa Fluor 750; amine-reactive BODIPY dyes, such as BODIPY 493/503, BODIPY 530/550, BODIPY 558/568, BODIPY 564/570, BODIPY 576/589, BODIPY 581/591, BODIPY 630/650, BODIPY 650/655, BODIPY FL, BODIPY R6G, BODIPY TMR, and, BODIPY-TR; Cy3, Cy5, 6-FAM, Fluorescein Isothiocyanate, HEX, 6-JOE, Oregon Green 488, Oregon Green 500, Oregon Green 514, Pacific Blue, REG, Rhodamine Green, Rhodamine Red, Renographin, ROX, SYPRO, TAMRA, 2′,4′,5′,7′-Tetrabromosulfonefluorescein, and TET.
  • Specific examples of fluorescently labeled ribonucleotides useful in the preparation of RT-PCR probes for use in some embodiments of the methods described herein are available from Molecular Probes (Invitrogen), and these include, Alexa Fluor 488-5-UTP, Fluorescein-12-UTP, BODIPY FL-14-UTP, BODIPY TMR-14-UTP, Tetramethylrhodamine-6-UTP, Alexa Fluor 546-14-UTP, Texas Red-5-UTP, and BODIPY TR-14-UTP. Other fluorescent ribonucleotides are available from Amersham Biosciences (GE Healthcare), such as Cy3-UTP and Cy5-UTP.
  • Examples of fluorescently labeled deoxyribonucleotides useful in the preparation of RT-PCR probes for use in the methods described herein include Dinitrophenyl (DNP)-1′-dUTP, Cascade Blue-7-dUTP, Alexa Fluor 488-5-dUTP, Fluorescein-12-dUTP, Oregon Green 488-5-dUTP, BODIPY FL-14-dUTP, Rhodamine Green-5-dUTP, Alexa Fluor 532-5-dUTP, BODIPY TMR-14-dUTP, Tetramethylrhodamine-6-dUTP, Alexa Fluor 546-14-dUTP, Alexa Fluor 568-5-dUTP, Texas Red-12-dUTP, Texas Red-5-dUTP, BODIPY TR-14-dUTP, Alexa Fluor 594-5-dUTP, BODIPY 630/650-14-dUTP, BODIPY 650/665-14-dUTP; Alexa Fluor 488-7-OBEA-dCTP, Alexa Fluor 546-16-OBEA-dCTP, Alexa Fluor 594-7-OBEA-dCTP, Alexa Fluor 647-12-OBEA-dCTP. Fluorescently labeled nucleotides are commercially available and can be purchased from, e.g., Invitrogen.
  • In some embodiments, dyes and other moieties, such as quenchers, are introduced into nucleic acids used in the methods described herein, such as FRET probes, via modified nucleotides. A “modified nucleotide” refers to a nucleotide that has been chemically modified, but still functions as a nucleotide. In some embodiments, the modified nucleotide has a chemical moiety, such as a dye or quencher, covalently attached, and can be introduced into an oligonucleotide, for example, by way of solid phase synthesis of the oligonucleotide. In other embodiments, the modified nucleotide includes one or more reactive groups that can react with a dye or quencher before, during, or after incorporation of the modified nucleotide into the nucleic acid. In specific embodiments, the modified nucleotide is an amine-modified nucleotide, i.e., a nucleotide that has been modified to have a reactive amine group. In some embodiments, the modified nucleotide comprises a modified base moiety, such as uridine, adenosine, guanosine, and/or cytosine. In specific embodiments, the amine-modified nucleotide is selected from 5-(3-aminoallyl)-UTP; 8-[(4-amino)butyl]-amino-ATP and 8-[(6-amino)butyl]-amino-ATP; N6-(4-amino)butyl-ATP, N6-(6-amino)butyl-ATP, N4-[2,2-oxy-bis-(ethylamine)]-CTP; N6-(6-Amino)hexyl-ATP; 8-[(6-Amino)hexyl]-amino-ATP; 5-propargylamino-CTP, 5-propargylamino-UTP. In some embodiments, nucleotides with different nucleobase moieties are similarly modified, for example, 5-(3-aminoallyl)-GTP instead of 5-(3-aminoallyl)-UTP. Many amine modified nucleotides are commercially available from, e.g., Applied Biosystems, Sigma, Jena Bioscience and TriLink.
  • In some embodiments, the methods of detecting at least one biomarker RNA described herein employ one or more modified oligonucleotides, such as oligonucleotides comprising one or more affinity-enhancing nucleotides. Modified oligonucleotides useful in the methods described herein include primers for reverse transcription, PCR amplification primers, and probes. In some embodiments, the incorporation of affinity-enhancing nucleotides increases the binding affinity and specificity of an oligonucleotide for its target nucleic acid as compared to oligonucleotides that contain only deoxyribonucleotides, and allows for the use of shorter oligonucleotides or for shorter regions of complementarity between the oligonucleotide and the target nucleic acid.
  • In some embodiments, affinity-enhancing nucleotides include nucleotides comprising one or more base modifications, sugar modifications and/or backbone modifications.
  • In some embodiments, modified bases for use in affinity-enhancing nucleotides include 5-methylcytosine, isocytosine, pseudoisocytosine, 5-bromouracil, 5-propynyluracil, 6-aminopurine, 2-aminopurine, inosine, diaminopurine, 2-chloro-6-aminopurine, xanthine and hypoxanthine.
  • In some embodiments, affinity-enhancing modifications include nucleotides having modified sugars such as 2′-substituted sugars, such as 2′-O-alkyl-ribose sugars, 2′-amino-deoxyribose sugars, 2′-fluoro-deoxyribose sugars, 2′-fluoro-arabinose sugars, and 2′-O-methoxyethyl-ribose (2′MOE) sugars. In some embodiments, modified sugars are arabinose sugars, or d-arabino-hexitol sugars.
  • In some embodiments, affinity-enhancing modifications include backbone modifications such as the use of peptide nucleic acids (e.g., an oligomer including nucleobases linked together by an amino acid backbone). Other backbone modifications include phosphorothioate linkages, phosphodiester modified nucleic acids, combinations of phosphodiester and phosphorothioate nucleic acid, methylphosphonate, alkylphosphonates, phosphate esters, alkylphosphonothioates, phosphoramidates, carbamates, carbonates, phosphate triesters, acetamidates, carboxymethyl esters, methylphosphorothioate, phosphorodithioate, p-ethoxy, and combinations thereof.
  • In some embodiments, the oligomer includes at least one affinity-enhancing nucleotide that has a modified base, at least nucleotide (which may be the same nucleotide) that has a modified sugar, and at least one internucleotide linkage that is non-naturally occurring.
  • In some embodiments, the affinity-enhancing nucleotide contains a locked nucleic acid (“LNA”) sugar, which is a bicyclic sugar. In some embodiments, an oligonucleotide for use in the methods described herein comprises one or more nucleotides having an LNA sugar. In some embodiments, the oligonucleotide contains one or more regions consisting of nucleotides with LNA sugars. In other embodiments, the oligonucleotide contains nucleotides with LNA sugars interspersed with deoxyribonucleotides. See, e.g., Frieden, M. et al. (2008) Curr. Pharm. Des. 14(11):1138-1142.
  • The term “primer” as used herein refers to a nucleic acid sequence, whether occurring naturally as in a purified restriction digest or produced synthetically, which is capable of acting as a point of synthesis when placed under conditions in which synthesis of a primer extension product, which is complementary to a nucleic acid strand is induced (e.g. in the presence of nucleotides and an inducing agent such as DNA polymerase and at a suitable temperature and pH). The primer must be sufficiently long to prime the synthesis of the desired extension product in the presence of the inducing agent. The exact length of the primer will depend upon factors, including temperature, sequences of the primer and the methods used. A primer typically contains 15-25 or more nucleotides, although it can contain less. The factors involved in determining the appropriate length of primer are readily known to one of ordinary skill in the art. In one embodiment, primer sets for the 15 genes are those listed in Table 7.
  • In addition, a person skilled in the art will appreciate that a number of methods can be used to determine the amount of a protein product of the biomarker of the invention, including immunoassays such as Western blots, ELISA, and immunoprecipitation followed by SDS-PAGE and immunocytochemistry.
  • Accordingly, in another embodiment, an antibody is used to detect the polypeptide products of the fifteen biomarkers listed in Table 4. In another embodiment, the sample comprises a tissue sample. In a further embodiment, the tissue sample is suitable for immunohistochemistry.
  • The term “antibody” as used herein is intended to include monoclonal antibodies, polyclonal antibodies, and chimeric antibodies. The antibody may be from recombinant sources and/or produced in transgenic animals. The term “antibody fragment” as used herein is intended to include Fab, Fab′, F(ab′)2, scFv, dsFv, ds-scFv, dimers, minibodies, diabodies, and multimers thereof and bispecific antibody fragments. Antibodies can be fragmented using conventional techniques. For example, F(ab′)2 fragments can be generated by treating the antibody with pepsin. The resulting F(ab′)2 fragment can be treated to reduce disulfide bridges to produce Fab′ fragments. Papain digestion can lead to the formation of Fab fragments. Fab, Fab′ and F(ab′)2, scFv, dsFv, ds-scFv, dimers, minibodies, diabodies, bispecific antibody fragments and other fragments can also be synthesized by recombinant techniques.
  • Conventional techniques of molecular biology, microbiology and recombinant DNA techniques are within the skill of the art. Such techniques are explained fully in the literature. See, e.g., Sambrook, Fritsch & Maniatis, 1989, Molecular Cloning: A Laboratory Manual, Second Edition; Oligonucleotide Synthesis (M. J. Gait, ed., 1984); Nucleic Acid Hybridization (B. D. Harnes & S. J. Higgins, eds., 1984); A Practical Guide to Molecular Cloning (B. Perbal, 1984); and a series, Methods in Enzymology (Academic Press, Inc.); Short Protocols In Molecular Biology, (Ausubel et al., ed., 1995).
  • For example, antibodies having specificity for a specific protein, such as the protein product of a biomarker, may be prepared by conventional methods. A mammal, (e.g. a mouse, hamster, or rabbit) can be immunized with an immunogenic form of the peptide which elicits an antibody response in the mammal. Techniques for conferring immunogenicity on a peptide include conjugation to carriers or other techniques well known in the art. For example, the peptide can be administered in the presence of adjuvant. The progress of immunization can be monitored by detection of antibody titers in plasma or serum. Standard ELISA or other immunoassay procedures can be used with the immunogen as antigen to assess the levels of antibodies. Following immunization, antisera can be obtained and, if desired, polyclonal antibodies isolated from the sera.
  • To produce monoclonal antibodies, antibody producing cells (lymphocytes) can be harvested from an immunized animal and fused with myeloma cells by standard somatic cell fusion procedures thus immortalizing these cells and yielding hybridoma cells. Such techniques are well known in the art, (e.g. the hybridoma technique originally developed by Kohler and Milstein (Nature 256:495-497 (1975)) as well as other techniques such as the human B-cell hybridoma technique (Kozbor et al., Immunol. Today 4:72 (1983)), the EBV-hybridoma technique to produce human monoclonal antibodies (Cole et al., Methods Enzymol, 121:140-67 (1986)), and screening of combinatorial antibody libraries (Huse et al., Science 246:1275 (1989)). Hybridoma cells can be screened immunochemically for production of antibodies specifically reactive with the peptide and the monoclonal antibodies can be isolated.
  • In some embodiments, recombinant antibodies are provided that specifically bind protein products of the fifteen genes listed in Table 4, and optionally expression products of one or more genes listed in Table 3. Recombinant antibodies include, but are not limited to, chimeric and humanized monoclonal antibodies, comprising both human and non-human portions, single-chain antibodies and multi-specific antibodies. A chimeric antibody is a molecule in which different portions are derived from different animal species, such as those having a variable region derived from a murine monoclonal antibody (mAb) and a human immunoglobulin constant region. (See, e.g., Cabilly et al., U.S. Pat. No. 4,816,567; and Boss et al., U.S. Pat. No. 4,816,397, which are incorporated herein by reference in their entirety.) Single-chain antibodies have an antigen binding site and consist of single polypeptides. They can be produced by techniques known in the art, for example using methods described in Ladner et. al U.S. Pat. No. 4,946,778 (which is incorporated herein by reference in its entirety); Bird et al., (1988) Science 242:423-426; Whitlow et al., (1991) Methods in Enzymology 2:1-9; Whitlow et al., (1991) Methods in Enzymology 2:97-105; and Huston et al., (1991) Methods in Enzymology Molecular Design and Modeling: Concepts and Applications 203:46-88. Multi-specific antibodies are antibody molecules having at least two antigen-binding sites that specifically bind different antigens. Such molecules can be produced by techniques known in the art, for example using methods described in Segal, U.S. Pat. No. 4,676,980 (the disclosure of which is incorporated herein by reference in its entirety); Holliger et al., (1993) Proc. Natl. Acad. Sci. USA 90:6444-6448; Whitlow et al., (1994) Protein Eng 7:1017-1026 and U.S. Pat. No. 6,121,424.
  • Monoclonal antibodies directed against any of the expression products of the genes listed in Table 4 and, optionally, against expression products of one or more genes listed in Table 3, can be identified and isolated by screening a recombinant combinatorial immunoglobulin library (e.g., an antibody phage display library) with the polypeptide(s) of interest. Kits for generating and screening phage display libraries are commercially available (e.g., the Pharmacia Recombinant Phage Antibody System, Catalog No. 27-9400-01; and the Stratagene SurfZAP Phage Display Kit, Catalog No. 240612). Additionally, examples of methods and reagents particularly amenable for use in generating and screening antibody display library can be found in, for example, U.S. Pat. No. 5,223,409; PCT Publication No. WO 92/18619; PCT Publication No. WO 91/17271; PCT Publication No. WO 92/20791; PCT Publication No. WO 92/15679; PCT Publication No. WO 93/01288; PCT Publication No. WO 92/01047; PCT Publication No. WO 92/09690; PCT Publication No. WO 90/02809; Fuchs et al. (1991) Bio/Technology 9:1370-1372; Hay et al. (1992) Hum. Antibod. Hybridomas 3:81-85; Huse et al. (1989) Science 246:1275-1281; Griffiths et al. (1993) EMBO J. 12:725-734.
  • Humanized antibodies are antibody molecules from non-human species having one or more complementarity determining regions (CDRs) from the non-human species and a framework region from a human immunoglobulin molecule. (See, e.g., Queen, U.S. Pat. No. 5,585,089, which is incorporated herein by reference in its entirety.) Humanized monoclonal antibodies can be produced by recombinant DNA techniques known in the art, for example using methods described in PCT Publication No. WO 87/02671; European Patent Application 184,187; European Patent Application 171,496; European Patent Application 173,494; PCT Publication No. WO 86/01533; U.S. Pat. No. 4,816,567; European Patent Application 125,023; Better et al. (1988) Science 240:1041-1043; Liu et al. (1987) Proc. Natl. Acad. Sci. USA 84:3439-3443; Liu et al. (1987) J. Immunol. 139:3521-3526; Sun et al. (1987) Proc. Natl. Acad. Sci. USA 84:214-218; Nishimura et al. (1987) Cancer Res. 47:999-1005; Wood et al. (1985) Nature 314:446-449; and Shaw et al. (1988) J. Natl. Cancer Inst. 80:1553-1559); Morrison (1985) Science 229:1202-1207; Oi et al. (1986) Bio/Techniques 4:214; U.S. Pat. No. 5,225,539; Jones et al. (1986) Nature 321:552-525; Verhoeyan et al. (1988) Science 239:1534; and Beidler et al. (1988) J. Immunol. 141:4053-4060.
  • In some embodiments, humanized antibodies can be produced, for example, using transgenic mice which are incapable of expressing endogenous immunoglobulin heavy and light chains genes, but which can express human heavy and light chain genes. The transgenic mice are immunized in the normal fashion with a selected antigen, e.g., all or a portion of a polypeptide corresponding to a protein product. Monoclonal antibodies directed against the antigen can be obtained using conventional hybridoma technology. The human immunoglobulin transgenes harbored by the transgenic mice rearrange during B cell differentiation, and subsequently undergo class switching and somatic mutation. Thus, using such a technique, it is possible to produce therapeutically useful IgG, IgA and IgE antibodies. For an overview of this technology for producing human antibodies, see Lonberg and Huszar (1995) Int. Rev. Immunol. 13:65-93). For a detailed discussion of this technology for producing human antibodies and human monoclonal antibodies and protocols for producing such antibodies, see, e.g., U.S. Pat. Nos. 5,625,126; 5,633,425; 5,569,825; 5,661,016; and 5,545,806. In addition, companies such as Abgenix, Inc. (Fremont, Calif.), can be engaged to provide human antibodies directed against a selected antigen using technology similar to that described above.
  • Antibodies may be isolated after production (e.g., from the blood or serum of the subject) or synthesis and further purified by well-known techniques. For example, IgG antibodies can be purified using protein A chromatography. Antibodies specific for a protein can be selected or (e.g., partially purified) or purified by, e.g., affinity chromatography. For example, a recombinantly expressed and purified (or partially purified) expression product may be produced, and covalently or non-covalently coupled to a solid support such as, for example, a chromatography column. The column can then be used to affinity purify antibodies specific for the protein products of the genes listed in Tables 3 and 4 from a sample containing antibodies directed against a large number of different epitopes, thereby generating a substantially purified antibody composition, i.e., one that is substantially free of contaminating antibodies. By a substantially purified antibody composition it is meant, in this context, that the antibody sample contains at most only 30% (by dry weight) of contaminating antibodies directed against epitopes other than those of the protein products of the genes listed in Tables 3 and 4, and preferably at most 20%, yet more preferably at most 10%, and most preferably at most 5% (by dry weight) of the sample is contaminating antibodies. A purified antibody composition means that at least 99% of the antibodies in the composition are directed against the desired protein.
  • In some embodiments, substantially purified antibodies may specifically bind to a signal peptide, a secreted sequence, an extracellular domain, a transmembrane or a cytoplasmic domain or cytoplasmic membrane of a protein product of one of the genes listed in Tables 3 and 4. In an embodiment, substantially purified antibodies specifically bind to a secreted sequence or an extracellular domain of the amino acid sequences of a protein product of one of the genes listed in Tables 3 and 4.
  • In some embodiments, antibodies directed against a protein product of one of the genes listed in Tables 3 and 4 can be used to detect the protein products or fragment thereof (e.g., in a cellular lysate or cell supernatant) in order to evaluate the level and pattern of expression of the protein. Detection can be facilitated by the use of an antibody derivative, which comprises an antibody coupled to a detectable substance. Examples of detectable substances include various enzymes, prosthetic groups, fluorescent materials, luminescent materials, bioluminescent materials, and radioactive materials. Examples of suitable enzymes include horseradish peroxidase, alkaline phosphatase, .beta.-galactosidase, or acetylcholinesterase; examples of suitable prosthetic group complexes include streptavidin/biotin and avidin/biotin; examples of suitable fluorescent materials include umbelliferone, fluorescein, fluorescein isothiocyanate, rhodamine, dichlorotriazinylamine fluorescein, dansyl chloride or phycoerythrin; an example of a luminescent material includes luminol; examples of bioluminescent materials include luciferase, luciferin, and aequorin, and examples of suitable radioactive material include 125I, 131I, 35S or 3H.
  • A variety of techniques can be employed to measure expression levels of each of the fifteen, and optional additional, genes given a sample that contains protein products that bind to a given antibody. Examples of such formats include, but are not limited to, enzyme immunoassay (EIA), radioimmunoassay (RIA), Western blot analysis and enzyme linked immunoabsorbant assay (ELISA). A skilled artisan can readily adapt known protein/antibody detection methods for use in determining protein expression levels of the fifteen, and optional additional products of the genes listed in Tables 4 and 3.
  • In one embodiment, antibodies, or antibody fragments or derivatives, can be used in methods such as Western blots or immunofluorescence techniques to detect the expressed proteins. In some embodiments, either the antibodies or proteins are immobilized on a solid support. Suitable solid phase supports or carriers include any support capable of binding an antigen or an antibody. Well-known supports or carriers include glass, polystyrene, polypropylene, polyethylene, dextran, nylon, amylases, natural and modified celluloses, polyacrylamides, gabbros, and magnetite.
  • One skilled in the art will know many other suitable carriers for binding antibody or antigen, and will be able to adapt such support for use with the present disclosure. The support can then be washed with suitable buffers followed by treatment with the detectably labeled antibody. The solid phase support can then be washed with the buffer a second time to remove unbound antibody. The amount of bound label on the solid support can then be detected by conventional means.
  • Immunohistochemistry methods are also suitable for detecting the expression levels of the prognostic markers. In some embodiments, antibodies or antisera, including polyclonal antisera, and monoclonal antibodies specific for each marker may be used to detect expression. The antibodies can be detected by direct labeling of the antibodies themselves, for example, with radioactive labels, fluorescent labels, hapten labels such as, biotin, or an enzyme such as horse radish peroxidase or alkaline phosphatase. Alternatively, unlabeled primary antibody is used in conjunction with a labeled secondary antibody, comprising antisera, polyclonal antisera or a monoclonal antibody specific for the primary antibody. Immunohistochemistry protocols and kits are well known in the art and are commercially available.
  • Immunological methods for detecting and measuring complex formation as a measure of protein expression using either specific polyclonal or monoclonal antibodies are known in the art. Examples of such techniques include enzyme-linked immunosorbent assays (ELISAs), radioimmunoassays (RIAs), fluorescence-activated cell sorting (FACS) and antibody arrays. Such immunoassays typically involve the measurement of complex formation between the protein and its specific antibody. These assays and their quantitation against purified, labeled standards are well known in the art (Ausubel, supra, unit 10.1-10.6). A two-site, monoclonal-based immunoassay utilizing antibodies reactive to two non-interfering epitopes is preferred, but a competitive binding assay may be employed (Pound (1998) Immunochemical Protocols, Humana Press, Totowa N.J.).
  • Numerous labels are available which can be generally grouped into the following categories:
      • (a) Radioisotopes, such as .sup.36S, .sup.14C, .sup.1251, .sup.3H, and .sup.131I. The antibody variant can be labeled with the radioisotope using the techniques described in Current Protocols in Immunology, vol 1-2, Coligen et al., Ed., Wiley-Interscience, New York, Pubs. (1991) for example and radioactivity can be measured using scintillation counting.
      • (b) Fluorescent labels such as rare earth chelates (europium chelates) or fluorescein and its derivatives, rhodamine and its derivatives, dansyl, Lissamine, phycoerythrin and Texas Red are available. The fluorescent labels can be conjugated to the antibody variant using the techniques disclosed in Current Protocols in Immunology, supra, for example. Fluorescence can be quantified using a fluorimeter.
      • (c) Various enzyme-substrate labels are available and U.S. Pat. Nos. 4,275,149, 4,318,980 provides a review of some of these. The enzyme generally catalyzes a chemical alteration of the chromogenic substrate which can be measured using various techniques. For example, the enzyme may catalyze a color change in a substrate, which can be measured spectrophotometrically. Alternatively, the enzyme may alter the fluorescence or chemiluminescence of the substrate. Techniques for quantifying a change in fluorescence are described above. The chemiluminescent substrate becomes electronically excited by a chemical reaction and may then emit light which can be measured (using a chemiluminometer, for example) or donates energy to a fluorescent acceptor. Examples of enzymatic labels include luciferases (e.g., firefly luciferase and bacterial luciferase; U.S. Pat. No. 4,737,456), luciferin, 2,3-dihydrophthalazinediones, malate dehydrogenase, urease, peroxidase such as horseradish peroxidase (HRPO), alkaline phosphatase, .beta.-galactosidase, glucoamylase, lysozyme, saccharide oxidases (e.g., glucose oxidase, galactose oxidase, and glucose-6-phosphate dehydrogenase), heterocyclic oxidases (such as uricase and xanthine oxidase), lactoperoxidase, microperoxidase, and the like. Techniques for conjugating enzymes to antibodies are described in O'Sullivan et al., Methods for the Preparation of Enzyme-Antibody Conjugates for Use in Enzyme Immunoassay, in Methods in Enzyme. (Ed. J. Langone & H. Van Vunakis), Academic press, New York, 73: 147-166 (1981).
  • In some embodiments, a detection label is indirectly conjugated with the antibody. The skilled artisan will be aware of various techniques for achieving this. For example, the antibody can be conjugated with biotin and any of the three broad categories of labels mentioned above can be conjugated with avidin, or vice versa. Biotin binds selectively to avidin and thus, the label can be conjugated with the antibody in this indirect manner. Alternatively, to achieve indirect conjugation of the label with the antibody, the antibody is conjugated with a small hapten (e.g. digoxin) and one of the different types of labels mentioned above is conjugated with an anti-hapten antibody (e.g. anti-digoxin antibody). In some embodiments, the antibody need not be labeled, and the presence thereof can be detected using a labeled antibody, which binds to the antibody.
  • The 15-gene signature described herein can be used to select treatment for NCSLC patients. As explained herein, the biomarkers can classify patients with NSCLC into a poor survival group or a good survival group and into groups that might benefit from adjuvant chemotherapy or not.
  • Accordingly, in one embodiment, the application provides a method of selecting a therapy for a subject with NSCLC, comprising the steps:
  • (a) classifying the subject with NSCLC into a poor survival group or a good survival group according to the methods described herein; and
  • (b) selecting adjuvant chemotherapy for the subject classified as being in the poor survival group or no adjuvant chemotherapy for the subject classified as being in the good survival group.
  • In another embodiment, the application provides a method of selecting a therapy for a subject with NSCLC, comprising the steps:
  • (a) determining the expression of fifteen biomarkers in a test sample from the subject, wherein the fifteen biomarkers correspond to the fifteen genes in Table 4;
  • (b) comparing the expression of the fifteen biomarkers in the test sample with the fifteen biomarkers in a control sample;
  • (c) classifying the subject in a poor survival group or a good survival group, wherein a difference or a similarity in the expression of the fifteen biomarkers between the control sample and the test sample is used to classify the subject into a poor survival group or a good survival group; and
  • (d) selecting adjuvant chemotherapy if the subject is classified in the poor survival group and selecting no adjuvant chemotherapy if the subject is classified in the good survival group.
  • The term “adjuvant chemotherapy” as used herein means treatment of cancer with chemotherapeutic agents after surgery where all detectable disease has been removed, but where there still remains a risk of small amounts of remaining cancer. Typical chemotherapeutic agents include cisplatin, carboplatin, vinorelbine, gemcitabine, doccetaxel, paclitaxel and navelbine.
  • In another aspect, the application provides compositions useful in detecting changes in the expression levels of the 15 genes listed in Table 4. Accordingly in one embodiment, the application provides a composition comprising a plurality of isolated nucleic acid sequences wherein each isolated nucleic acid sequence hybridizes to:
  • (a) a RNA product of one of the 15 genes listed in Table 4; and/or
  • (b) a nucleic acid complementary to a),
  • wherein the composition is used to measure the level of RNA expression of the 15 genes. In a particular embodiment, the plurality of isolated nucleic acid sequences comprise isolated nucleic acids hybridizable to the 15 probe target sequences as set out in Table 9. In one embodiment, the plurality of isolated nucleic acid sequences comprise isolated nucleic acids hybridizable to SEQ ID NO: 3, 11-15, 22, 26, 35, 49, 78, 85, 130, 133, and 169.
  • In another embodiment, the application provides a composition comprising 15 forward and 15 reverse primers for amplifying a region of each gene listed in Table 4. In particular embodiment, the 30 primers are as set out in Table 7. In one embodiment, the 30 primers each comprise a sequence that is identical to the sequence of one of SEQ ID NO: 173-202.
  • In a further aspect, the application also provides an array that is useful in detecting the expression levels of the 15 genes set out in Table 4. Accordingly, in one embodiment, the application provides an array comprising for each gene shown in Table 4 one or more nucleic acid probes complementary and hybridizable to an expression product of the gene. In a particular embodiment, the array comprises the nucleic acid probes hybridizable to the probe target sequences listed in Table 9. In one embodiment, the array comprises the nucleic acid probes hybridizable to sequences identical to each of SEQ ID NO: 3, 11-15, 22, 26, 35, 49, 78, 85, 130, 133, and 169.
  • In yet another aspect, the application also provides for kits used to prognose or classify a subject with NSCLC into a good survival group or a poor survival group or to select a therapy for a subject with NSCLC that includes detection agents that can detect the expression products of the biomarkers. Accordingly, in one embodiment, the application provides a kit to prognose or classify a subject with early stage NSCLC comprising detection agents that can detect the expression products of 15 biomarkers, wherein the 15 biomarkers comprise 15 genes in Table 4. In another embodiment, kits for classifying a subject comprise detection agents that can detect the expression of 16, 17, or 18 biomarkers, wherein 15 biomarkers comprise the 15 genes in Table 4, and the additional biomarkers are selected from the genes listed in Table 3. In one embodiment, the additional sixteenth, seventeenth, and eighteenth biomarkers may be selected from RGS4, UGT2B4, and MCF2 listed in Table 3.
  • In one embodiment, the application provides a kit to select a therapy for a subject with NSCLC, comprising detection agents that can detect the expression products of 15 biomarkers, wherein the 15 biomarkers comprise 15 genes in Table 4. In some embodiments, kits for selecting therapy for a subject comprise detection agents that can detect the expression of 16, 17, or 18 biomarkers, wherein 15 biomarkers comprise the 15 genes in Table 4, and the additional biomarkers are selected from the genes listed in Table 3. In one embodiment, the additional sixteenth, seventeenth, and eighteenth biomarkers may be selected from RGS4, UGT2B4, and MCF2 listed in Table 3.
  • The materials and methods of the present disclosure are ideally suited for preparation of kits produced in accordance with well known procedures. In some embodiments, kits comprise agents (like the polynucleotides and/or antibodies described herein as non-limiting examples) for the detection of expression of the disclosed sequences, such as for example, SEQ ID NO: 3, 11-15, 22, 26, 35, 49, 78, 85, 130, 133, and 169, the target sequences listed in Table 9, or the target sequences listed in Table 11. Kits, may comprise containers, each with one or more of the various reagents (sometimes in concentrated form), for example, pre-fabricated microarrays, buffers, the appropriate nucleotide triphosphates (e.g., dATP, dCTP, dGTP and dTTP; or rATP, rCTP, rGTP and UTP), reverse transcriptase, DNA polymerase, RNA polymerase, and one or more primer complexes (e.g., appropriate length poly(T) or random primers linked to a promoter reactive with the RNA polymerase). A set of instructions will also typically be included.
  • In some embodiments, a kit may comprise a plurality of reagents, each of which is capable of binding specifically with a target nucleic acid or protein. Suitable reagents for binding with a target protein include antibodies, antibody derivatives, antibody fragments, and the like. Suitable reagents for binding with a target nucleic acid (e.g. a genomic DNA, an mRNA, a spliced mRNA, a cDNA, or the like) include complementary nucleic acids. For example, nucleic acid reagents may include oligonucleotides (labeled or non-labeled) fixed to a substrate, labeled oligonucleotides not bound with a substrate, pairs of PCR primers, molecular beacon probes, and the like.
  • In some embodiments, kits may comprise additional components useful for detecting gene expression levels. By way of example, kits may comprise fluids (e.g. SSC buffer) suitable for annealing complementary nucleic acids or for binding an antibody with a protein with which it specifically binds, one or more sample compartments, a material which provides instruction for detecting expression levels, and the like.
  • In some embodiments, kits for use in the RT-PCR methods described herein comprise one or more target RNA-specific FRET probes and one or more primers for reverse transcription of target RNAs or amplification of cDNA reverse transcribed therefrom.
  • In some embodiments, one or more of the primers is “linear”. A “linear” primer refers to an oligonucleotide that is a single stranded molecule, and typically does not comprise a short region of, for example, at least 3, 4 or 5 contiguous nucleotides, which are complementary to another region within the same oligonucleotide such that the primer forms an internal duplex. In some embodiments, the primers for use in reverse transcription comprise a region of at least 4, such as at least 5, such as at least 6, such as at least 7 or more contiguous nucleotides at the 3′-end that has a base sequence that is complementary to region of at least 4, such as at least 5, such as at least 6, such as at least 7 or more contiguous nucleotides at the 5′-end of a target RNA.
  • In some embodiments, the kit further comprises one or more pairs of linear primers (a “forward primer” and a “reverse primer”) for amplification of a cDNA reverse transcribed from a target RNA. Accordingly, in some embodiments, the forward primer comprises a region of at least 4, such as at least 5, such as at least 6, such as at least 7, such as at least 8, such as at least 9, such as at least 10 contiguous nucleotides having a base sequence that is complementary to the base sequence of a region of at least 4, such as at least 5, such as at least 6, such as at least 7, such as at least 8, such as at least 9, such as at least 10 contiguous nucleotides at the 5′-end of a target RNA. Furthermore, in some embodiments, the reverse primer comprises a region of at least 4, such as at least 5, such as at least 6, such as at least 7, such as at least 8, such as at least 9, such as at least 10 contiguous nucleotides having a base sequence that is complementary to the base sequence of a region of at least 4, such as at least 5, such as at least 6, such as at least 7, such as at least 8, such as at least 9, such as at least 10 contiguous nucleotides at the 3′-end of a target RNA.
  • In some embodiments, the kit comprises at least a first set of primers for amplification of a cDNA that is reverse transcribed from a target RNA capable of specifically hybridizing to a nucleic acid comprising a sequence identically present in one of the genes listed in Table 4. In some embodiments, the kit comprises at least fifteen sets of primers, each of which is for amplification of a different target RNA capable of specifically hybridizing to a nucleic acid comprising a sequence identically present in a different gene listed in Table 4. In one embodiment, the kit comprises fifteen forward and fifteen reverse primers described in Table 7, comprising sequences identical to SEQ ID NOs 173-202. In some embodiments, the kit comprises one, two, or three more sets of primers, in addition to the fifteen sets of primers, each of the additional sets being for amplification of a different target RNA capable of specifically hybridizing to a nucleic acid comprising a sequence identically present in a different gene listed in Table 3. In some embodiments, the kit comprises one, two, or three more sets of primers, in addition to the fifteen sets of primers, each of the additional sets being for amplification of a different target RNA capable of specifically hybridizing to a nucleic acid comprising a sequence identically present in RGS4, UGT2B4, or MCF2 listed in Table 3. In some embodiments, the kit comprises at least one set of primers that is capable of amplifying more than one cDNA reverse transcribed from a target RNA in a sample.
  • In some embodiments, probes and/or primers for use in the compositions described herein comprise deoxyribonucleotides. In some embodiments, probes and/or primers for use in the compositions described herein comprise deoxyribonucleotides and one or more nucleotide analogs, such as LNA analogs or other duplex-stabilizing nucleotide analogs described above. In some embodiments, probes and/or primers for use in the compositions described herein comprise all nucleotide analogs. In some embodiments, the probes and/or primers comprise one or more duplex-stabilizing nucleotide analogs, such as LNA analogs, in the region of complementarity.
  • In some embodiments, the compositions described herein also comprise probes, and in the case of RT-PCR, primers, that are specific to one or more housekeeping genes for use in normalizing the quantities of target RNAs. Such probes (and primers) include those that are specific for one or more products of housekeeping genes selected from ACTB, BAT1, B2M, TBP, U6 snRNA, RNU44, RNU 48, and U47.
  • In some embodiments, the kits for use in real time RT-PCR methods described herein further comprise reagents for use in the reverse transcription and amplification reactions. In some embodiments, the kits comprise enzymes such as reverse transcriptase, and a heat stable DNA polymerase, such as Taq polymerase. In some embodiments, the kits further comprise deoxyribonucleotide triphosphates (dNTP) for use in reverse transcription and amplification. In further embodiments, the kits comprise buffers optimized for specific hybridization of the probes and primers.
  • In some embodiments, kits are provided containing antibodies to each of the protein products of the genes listed in Table 4, conjugated to a detectable substance, and instructions for use. In some embodiments, the kits comprise antibodies to one, two, or three protein products of the genes listed in Table 3, in addition to antibodies to each of the protein products of the genes listed in Table 4.
  • In some embodiments, the kit comprises antibodies to the protein product of one, two, or all three of RGS4, UGT2B4, or MCF2 listed in Table 3, in addition to antibodies to each of the protein products of the genes listed in Table 4. Kits may comprise an antibody, an antibody derivative, or an antibody fragment, which binds specifically with a marker protein, or a fragment of the protein. Such kits may also comprise a plurality of antibodies, antibody derivatives, or antibody fragments wherein the plurality of such antibody agents binds specifically with a marker protein, or a fragment of the protein.
  • In some embodiments, kits may comprise antibodies such as a labeled or labelable antibody and a compound or agent for detecting protein in a biological sample; means for determining the amount of protein in the sample; means for comparing the amount of protein in the sample with a standard; and instructions for use. Such kits can be supplied to detect a single protein or epitope or can be configured to detect one of a multitude of epitopes, such as in an antibody detection array. Arrays are described in detail herein for nucleic acid arrays and similar methods have been developed for antibody arrays.
  • A person skilled in the art will appreciate that a number of detection agents can be used to determine the expression of the biomarkers. For example, to detect RNA products of the biomarkers, probes, primers, complementary nucleotide sequences or nucleotide sequences that hybridize to the RNA products can be used. To detect protein products of the biomarkers, ligands or antibodies that specifically bind to the protein products can be used.
  • Accordingly, in one embodiment, the detection agents are probes that hybridize to the 15 biomarkers. In a particular embodiment, the probe target sequences are as set out in Table 9. In one embodiment, the probe target sequences are identical to SEQ ID NO: 3, 11-15, 22, 26, 35, 49, 78, 85, 130, 133, and 169. In another embodiment, the detection agents are forward and reverse primers that amplify a region of each of the 15 genes listed in Table 4. In a particular embodiment, the primers are as set out in Table 7. In one embodiment, the primers comprise the polynucleotide sequences of SEQ ID NO: 173-202.
  • A person skilled in the art will appreciate that the detection agents can be labeled.
  • The label is preferably capable of producing, either directly or indirectly, a detectable signal. For example, the label may be radio-opaque or a radioisotope, such as 3H, 14C, 32P, 35S, 123I, 125I, 131I; a fluorescent (fluorophore) or chemiluminescent (chromophore) compound, such as fluorescein isothiocyanate, rhodamine or luciferin; an enzyme, such as alkaline phosphatase, beta-galactosidase or horseradish peroxidase; an imaging agent; or a metal ion.
  • The kit can also include a control or reference standard and/or instructions for use thereof. In addition, the kit can include ancillary agents such as vessels for storing or transporting the detection agents and/or buffers or stabilizers.
  • In some aspects, a multi-gene signature is provided for prognosis or classifying patients with lung cancer. In some embodiments, a fifteen-gene signature is provided, comprising reference values for each of the fifteen genes based on relative expression data from a historical data set with a known outcome, such as good or poor survival, and/or known treatment, such as adjuvant chemotherapy. In one embodiment, four reference values are provided for each of the fifteen genes listed in Table 4. In one embodiment, the reference values for each of the fifteen genes are principal component values set forth in Table 10.
  • In one aspect, relative expression data from a patient are combined with the gene-specific reference values on a gene-by-gene basis for each of the fifteen, and, optionally, additional genes, to generate a test value which allows prognosis or therapy recommendation. In some embodiments, relative expression data are subjected to an algorithm that yields a single test value, or combined score, which is then compared to a control value obtained from the historical expression data for a patient or pool of patients.
  • In some embodiments, the control value is a numerical threshold for predicting outcomes, for example good and poor outcome, or making therapy recommendations for a subject, for example adjuvant chemotherapy in addition to surgical resection or surgical resection alone. In some embodiments, a test value or combined score greater than the control value is predictive, for example, of a poor outcome or benefit from adjuvant chemotherapy, whereas a combined score falling below the control value is predictive, for example, of a good outcome or lack of benefit from adjuvant chemotherapy for a subject.
  • In some embodiments, a method for prognosing or classifying a subject with NSCLC comprises:
      • (a) measuring expression levels of at least 15 biomarkers from Table 4, and optionally, an additional one, two, or three biomarkers from Table 3 in a test sample,
      • (b) calculating a combined score or test value for the subject from the expression levels of the, and,
      • (c) comparing the combined score to a control value,
        Wherein a combined score greater than the control value is used to classify a subject into a high risk or poor survival group and a combined score lower than the control value is used to classify a subject into a lower risk or good survival group.
  • In one embodiment, the combined score is calculated from relative expression data multiplied by reference values, determined from historical data, for each gene. Accordingly, the combined score may be calculated using Formula I below:

  • Combined score=0.557×PC1+0.328×PC2+0.43×PC3+0.335×PC4
  • Where PC1 is the sum of the relative expression level for each gene in a multi-gene signature multiplied by a first principal component for each gene in the multi-gene signature, PC2 is the sum of the relative expression level for each gene multiplied by a second principal component for each gene, PC3 is the sum of the relative expression level for each gene multiplied by a third principal component for each gene, and PC4 is the sum of the relative expression level for each gene multiplied by a fourth principal component for each gene. In some embodiments, the combined score is referred to as a risk score. A risk score for a subject can be calculated by applying Formula I to relative expression data from a test sample obtained from the subject.
  • In some embodiments, PC1 is the sum of the relative expression level for each gene provided in Table 4 multiplied by a first principal component for each gene, respectively, as set forth in Table 10; PC2 is the sum of the relative expression level for each gene provided in Table 4 multiplied by a second principal component for each gene, respectively, as set forth in Table 10; PC3 is the sum of the relative expression level for each gene provided in Table 4 multiplied by a third principal component for each gene, respectively, as set forth in Table 10; and PC4 is the sum of the relative expression level for each gene provided in Table 4 multiplied by a fourth principal component for each gene, respectively, as set forth in Table 10.
  • In one embodiment, the control value is equal to −0.1. A subject with a risk score of more than −0.1 is classified as high risk (poor prognosis). A patient with a risk score of less than −0.1 is classified as lower risk (good prognosis). In some embodiments, adjuvant chemotherapy is recommended for a subject with a risk score of more than −0.1 and not recommended for a subject with a risk score of less than −0.1.
  • In a further aspect, the application provides computer programs and computer implemented products for carrying out the methods described herein. Accordingly, in one embodiment, the application provides a computer program product for use in conjunction with a computer having a processor and a memory connected to the processor, the computer program product comprising a computer readable storage medium having a computer mechanism encoded thereon, wherein the computer program mechanism may be loaded into the memory of the computer and cause the computer to carry out the methods described herein.
  • In another embodiment, the application provides a computer implemented product for predicting a prognosis or classifying a subject with NSCLC comprising:
  • (a) a means for receiving values corresponding to a subject expression profile in a subject sample; and
  • (b) a database comprising a reference expression profile associated with a prognosis, wherein the subject biomarker expression profile and the biomarker reference profile each has fifteen values, each value representing the expression level of a biomarker, wherein each biomarker corresponds to one gene in Table 4; wherein the computer implemented product selects the biomarker reference expression profile most similar to the subject biomarker expression profile, to thereby predict a prognosis or classify the subject.
  • In yet another embodiment, the application provides a computer implemented product for determining therapy for a subject with NSCLC comprising:
  • (a) a means for receiving values corresponding to a subject expression profile in a subject sample; and
  • (b) a database comprising a reference expression profile associated with a therapy, wherein the subject biomarker expression profile and the biomarker reference profile each has fifteen values, each value representing the expression level of a biomarker, wherein each biomarker corresponds to one gene in Table 4; wherein the computer implemented product selects the biomarker reference expression profile most similar to the subject biomarker expression profile, to thereby predict the therapy.
  • Another aspect relates to computer readable mediums such as CD-ROMs. In one embodiment, the application provides computer readable medium having stored thereon a data structure for storing a computer implemented product described herein.
  • In one embodiment, the data structure is capable of configuring a computer to respond to queries based on records belonging to the data structure, each of the records comprising:
  • (a) a value that identifies a biomarker reference expression profile of the 15 genes in Table 4;
  • (b) a value that identifies the probability of a prognosis associated with the biomarker reference expression profile.
  • In another aspect, the application provides a computer system comprising
  • (a) a database including records comprising a biomarker reference expression profile of fifteen genes in Table 4 associated with a prognosis or therapy;
  • (b) a user interface capable of receiving a selection of gene expression levels of the 15 genes in Table 4 for use in comparing to the biomarker reference expression profile in the database; and
  • (c) an output that displays a prediction of prognosis or therapy according to the biomarker reference expression profile most similar to the expression levels of the fifteen genes.
  • In some embodiments, the application provides a computer implemented product comprising
  • (a) a means for receiving values corresponding to relative expression levels in a subject, of at least 15 biomarkers comprising the fifteen genes in Table 4, and optionally, additional one, two, or three genes selected from the genes listed in Table 3;
  • (b) an algorithm for calculating a combined scire based on the relative expression levels of the at least 15 biomarkers;
  • (c) an output that displays the combined score; and, optionally,
  • (d) an output that displays a prognosis or therapy recommendation based on the combined score.
  • The above disclosure generally describes the present invention. A more complete understanding can be obtained by reference to the following specific examples. These examples are described solely for the purpose of illustration and are not intended to limit the scope of the invention. Changes in form and substitution of equivalents are contemplated as circumstances might suggest or render expedient. Although specific terms have been employed herein, such terms are intended in a descriptive sense and not for purposes of limitation.
  • The following non-limiting example is illustrative of the present invention:
    • Example 1 Results
  • Table 1 compared the demographic features of 133 patients with microarray profiling to 349 without the profiling. Stage IB patients had more representation in the observation cohort (55% vs. 42%, p=0.01), but all other factors were similarly distributed. There was no significant difference in the overall survivals of patients with or without gene profiling (FIG. 2A). For these 133 patients, adjuvant chemotherapy reduced the death rate by 20% (HR 0.80, 95% Cl 0.48-1.32, p=0.38; FIG. 5).
    • Prognostic Gene Expression Signature in JBR.10 Patients
  • Using a p0.005 as cut-off, 172 of 19,619 probe sets were significantly associated with prognosis in 62 observation patients (FIG. 1A and Table 3). Using a method that was designed to identify the minimum expression gene set that can distinguish most patients with poor and good survival outcomes, a 15-gene prognostic signature was identified (FIG. 1A and Table 4). This signature was able to separate the 62 non-adjuvant treated patients into 31 low-risk and 31 high-risk patients for death (HR 15.020, 95% Cl 5.12-44.04, p<0.0001; FIG. 2B). Furthermore, stratified analysis showed that the signature was also highly prognostic in 34 stage IB patients (HR 13.32, 95% Cl 2.86-62.11, p<0.0001, FIG. 2C) and 28 stage 11 patients (HR 13.47, 95% Cl 3.0-60.43, p<0.0001, FIG. 2D). Multivariate analysis adjusting for tumor stage, age, gender and histology showed that the prognostic signature was an independent prognostic marker (HR 18.0, 95% Cl 5.8-56.1; p<0.0001, Table 2). This did not differ following additional adjustment for surgical procedure and tumor size.
    • Validation of General Applicability of Prognostic Signature (Summary)
  • Applying the risk score algorithm (equation) established from the 62 BR.10 observation patients, the 15-gene signature was demonstrated to be an independent prognostic marker among all 169 DCC patients (HR 2.9, 95% Cl 1.5-5.6, p=0.002; Table 2). Subgroup analyses also showed significant results among patients from DCC-UM (HR 1.5, 95% Cl 0.54-4.31, p=0.4; Table 2) and HLM (HR 1.2, 95% Cl 0.43-3.6, p=0.7; Table 2). The signature was also prognostic among UM-SQ patients (HR 2.3, 95% Cl 1.1-4.7, p=0.026; Table 2), and in the Duke's patients (HR 1.5, 95% Cl 0.81-2.89, p=0.19; Table 2).
  • The prognostic value of the signature was tested in stage I patients of the DCC (n=141) patients and was able to identify patients with significantly different survival outcome (Table 8).
    • Prediction of Chemotherapy Benefit
  • When tested on the microarray data of 71 JBR.10 patients who received adjuvant chemotherapy, the 15-gene signature was not prognostic (HR 1.5, 95% Cl 0.7-3.3, p=0.28, Table 2). The signature was also not prognostic when applied separately to stage IB and stage II patients (Table 2). Among the Director's Challenge patients, 41 were identified as having received adjuvant chemotherapy with or without radiotherapy. The 15-gene signature was also not prognostic for these 41 patients (HR 1.1, 95% Cl 0.5-2.5, p=0.8) (Table 2).
  • Stratified analysis showed that in JBR.10 patients with microarray data, only patients classified to the high-risk group derived benefit from the adjuvant chemotherapy (FIGS. 3C and 3D). High-risk patients showed 67% improved survival when treated by adjuvant chemotherapy compared to observation (HR=0.33, 95% Cl 0.17-0.63, p=0.0005, FIG. 3D), while those assigned to the low risk group did not benefit (FIG. 3C). These results were reproduced when applied separately to both the stage IB (FIGS. 3E and 3F) and stage II (FIGS. 3G and 3H) patients.
  • Multivariate analysis showed that the decrease of survival associated with adjuvant chemotherapy was independent of the stage (HR=2.26, 95% Cl 1.03-4.96, p=0.04). A Cox regression model with chemotherapy received and risk group indicator and their interaction term as independent covariates were performed to fit the overall survival data on the 133 patients with microarray data. This analysis revealed that the interaction term is highly significant (p=0.0003) with the high-risk group deriving significantly greater benefit from adjuvant chemotherapy.
    • The Initial Study Population
  • The initial study population comprised a subset of the patients randomized in the JBR.10 trial. There were 169 frozen tumor samples collected from patients who had their surgery at one of the BR.10 Canadian Centres have consented to the use of their samples for “future” studies in addition to RAS mutation analysis. The samples were harvested using a standardized protocol that was agreed upon during trial protocol development by designated pathologists from each participating centre. All tumors and corresponding normal lung tissue were collected as soon as or within 30 min after resection, and were snap-frozen in liquid nitrogen. For each frozen tissue fragment, a 1 mm cross-section slice was fixed in 10% buffered formalin and submitted for paraffin embedding. Histological evaluation of the HE stained sections revealed 166 samples that contained a 20% tumor cellularity. Among the latter, gene expression profiling was completed successfully in samples from 133 patients. These included 58 patients randomized to the observation (OBS) arm and 75 to the adjuvant chemotherapy (ACT) arm. However, 4 ACT patients refused chemotherapy, and for the purpose of this analysis, they were assigned to the OBS arm. Therefore, the final distribution included 62 OBS patients and 71 ACT patients (FIGS. 1 and 4).
    • Microarray Data Analysis
  • The raw microarray data from Affymetrix U133A (Affymetrix, Santa Clara, Calif.) were pre-processed using RMAexpress v0.32, then were twice log 2 transformed since the distribution of additional log 2 transformed data appeared more normal. Probe sets were annotated using NetAffx v4.2 annotation tool and only grade A level probe sets 3 (NA24) were included for further analysis. Affymetrix U133A chip contains 22,215 probe sets (19,619 probe sets with grade A annotation). Since the microarray hybridizations were performed in two batches at two separate occasions (January 2004, and June 2005), and unsupervised clustering showed that a batch difference was significant (FIG. 6), a distance-weighted discrimination (DWD) algorithm (https://genome.unc.edu/pubsup/dwd/index.html) was applied to homogenize the two batches. The DWD algorithm first finds a hyperplane that separates the two batches and adjusts the data by projecting the different batches on the DWD plane, finds the batch mean, and then subtracts out the DWD plane multiplied by this mean. In addition, the data were Z score transformed which made the validation across different datasets possible.
    • Univariate Analysis
  • The association of the expression of the individual probe set with overall survival (date of randomization to date of last follow up or death) was evaluated by Cox proportional hazards regression. The expression data for 62 patients in observation arm revealed 1312 probe sets that were associated with overall survival at p<0.05. Using a more stringent selection criteria of p<0.005, 172 probe sets with grade A annotation were prognostic.
    • Gene Set Signature Selection
  • To generate the gene expression signature, an exclusion selection procedure was firstly applied and followed by an inclusion process. The MAximizing R Square Algorithm (MARSA) included 3 sequential steps: a) probe set pre-selection; b) signature optimization; and c) leave-one-out-cross-validation. First, the candidate probe sets were pre-selected by their associations with survival at p<0.005 level. To remove the cross platform variation, expression data was z score transformed and risk score (z score weighted by the coefficient of the univariate Cox regression) was used to synthesize the information of the probe set combination. The candidate probe sets were then subjected to an exclusion followed by an inclusion selection procedure. For the preselected 172 probe sets, the exclusion procedure excluded one probe at a time, summed up the risk score of the remaining 171 probes, the calculated the R square (R2, Goodness-of-fit) of the Cox model5,6. Risk score was dichotomized by an outcome-orientated optimization of cutoff macro based on log-rank statistics (http://ndc.mayo.edu/mayo/research/biostat/sasmacros.cfm) before being introduced to the Cox proportional hazards model. A probe set was excluded if its exclusion resulted in obtaining the largest R2. The procedure was repeated until there was only one probe set left. An inclusion procedure was followed using the probe set left by the exclusion procedure as the starting probe set. It included one probe set at a time, summed up the risk score of the included probe sets and risk score was dichotomized and R2 was calculated. The probe set was included if its inclusion resulted in obtaining the largest R2. The exclusion procedure produced a largest R square of 0.67 by a minimal 7 probe combination and the inclusion procedure generated a largest R2 of 0.78 by a minimal 15 probe combination (FIG. 1B), therefore, the 15 gene combination (Table 4) was selected as a candidate signature. Finally, the 15-gene signature (Table 4) was established after passing the internal validation by leave-one-out-cross-validation (LOOCV) and external validation on other datasets (listed below). All statistical analyses were performed using SAS v9.1 (SAS Institute, CA). The risk score was calculated as Table 4.
    • Prognostic Modeling by Principal Component Analysis of Signature Genes
  • Principal components analysis (PCA) (based on correlation matrix) was carried out to synthesize the information across the chosen gene probe sets and reduce the number of covariates in building the prognostic model. The eigenvalue of greater than or equal to 1 was used as cutoff point in determining how many proponents to include in the model, and those significantly correlated to disease-specific survival (DSS) were included in the final multivariable model. The PCA analysis was done based on all 133 patients with microarray data. When correlated to the DSS based on the 62 observation patients, the first 4 principal components were found to satisfy the criteria and were included in the prognostic model. Table 10 lists the four principal components for each of the 15 genes in the 15-gene signature. The same analysis can be applied to derive principal component coefficients for additional genes selected from the 172 genes listed in Table 3, such as for example, RGS4, UGT2B4, and/or MCF2. Furthermore, one of skill will appreciate from the above description how to obtain the first four principal component coefficients for any of the genes listed in Table 3.
  • To determine the gene signature prognostic group, multivariate Cox regression model with the first 4 principal components were fitted to the disease specific survival of the 62 observation patients. The linear prognostic scores were calculated by the sum of the multiplication of the estimated coefficient from Cox model and the corresponding principal component value. Using the prognostic score, patients were divided into low and high risk group based on the median of the prognostic score, i.e., those with prognostic score less than the median as low risk group, while those with score no less than the median as high risk group. For the 62 observation patients with microarray data, 31 patients were classified in each group. Applying the same rule to the 73 chemo-treated patients, 36 patients were classified in low risk group and 37 patients in high-risk group.
    • Validation of General Applicability of Prognostic Signature
  • Validation of the 15-gene signature was carried out on stage I-11 cases from Duke, Raponi, and DC who did not receive adjuvant chemotherapy. When the risk score was dichotomized using the cutoff determined from the BR.10 training set, the 15-gene signature was able to separate 38 cases of low risk from 47 cases of high risk (log rank p=0.226) of NSCLC in the Duke dataset. Multivariate analysis (adjusted for stage, histology and patients' age and gender) showed that the 15-gene signature was an independent prognostic factor (HR=1.5, 95% Cl 0.81-2.89, p=0.19, Table 2). Raponi contains squamous cell carcinoma only and the cases have the worst survival rate. However, the 15-gene signature was still able to separate 50 cases of low risk from 56 cases with high risk (log rank p=0.0447) and this separation was independent of stage and patients' age and gender (HR=2.3, 95% Cl 1.1-4.7 p=0.026, Table 2). The DC dataset contained only adenocarcinoma cases. Applying the 15-gene signature on DC stage I and II, was able to separate 87 low risk cases from the 82 high risk cases (log rank p=0.0002, FIG. 2E). Multivariate analysis (adjusted for stage and patients' age and gender) showed that the prognostic value of the 15-gene signature was independent prognostic factor (HR=2.9, 95% Cl 1.5-5.6, p=0.002, Table 2). There were 67 stage IB-II cases without chemotherapy in MI, the 15-gene signature was able to separate 44 low risk cases from the 23 high risk cases (log rank p=0.013). Multivariate analysis (adjusted for stage and patients' age and gender) showed that the prognostic value of the 15-gene signature was independent prognostic factor (HR=1.5, 95% Cl 0.54-4.31, p=0.4, Table 2). Cases from MSKCC had a significantly better 5-year overall survival compared to other datasets. However, the 15-gene signature was able to separate 32 cases of low risk from 32 cases of high risk in MSKCC (log rank p=0.16). Multivariate analysis (adjusted for stage) revealed that the 15-gene signature was an independent prognostic factor. Validation of the 15-gene signature on HLM revealed that the 15-gene signature was able to separate 26 cases of low risk from 24 cases of high risk (log rank p=0.0084). Multivariate analysis (adjusted for stage) showed that there was a trend to separation by the 15-gene signature (HR=1.2, 95% Cl 0.43-3.6, p=0.7). These validation data confirm that the 15-gene signature is a strong prognostic signature and its power of predicting the outcome of NSCLC is independent of and superior to that of stage.
    • The Benefit of Chemotherapy was Limited to High Risk Patients
  • A total of 30 deaths were observed in the ACT. Six of them were due to other malignancies. The 15-gene signature was unable to separate the good/bad outcome patients (p=0.83, data not shown) in the ACT. However, stratified analysis showed that only patients with high risk derived benefit from adjuvant chemotherapy (FIG. 3D). Upon receiving adjuvant chemotherapy, the survival rate of the 36 high-risk patients was significantly improved (HR=0.33, 95% Cl 0.17-0.63, p=0.0005, FIG. 3D). On the other hand, the application of chemotherapy on low risk patients resulted in a decrease in survival rate (HR=3.67, 95% Cl 1.22-11.06, p=0.0133, FIG. 3C). Death was evenly distributed between the low and high risk groups in the ACT arm (15 deaths in low and high risk group, respectively). Each of these two groups contained 3 deaths that were not due to lung cancer. Stratification by risk group and stage showed that the survival rate of high risk patients from both stage IB and stage II was significantly improved by chemotherapy (FIGS. 3F and H). Moreover, for low risk patients of stage II, chemotherapy was associated with significantly decreased survival (FIGS. 3E and G). A Cox regression model with chemotherapy received and risk group indicator and their interaction term as independent covariates was performed to fit the overall survival data on the 133 patients with microarray data. This analysis revealed that the interaction term is highly significant (p=0.0002) with the high-risk group deriving significantly greater benefit from adjuvant chemotherapy.
    • Discussion:
  • Gene expression signature is thought to represent the altered key pathways in carcinogenesis and thus is able to predict patients' outcome. However, being able to faithfully represent the altered key pathways, the signature must be generated from genome-wide gene expression data. The present study used all information generated by Affymetrix U133A chip on NSCLC samples from a randomized clinical trial to derive a 15-gene signature. The 15-gene signature was able to identify 50% (31/62) stage IB-II NSCLC patients had relative good outcome. Multivariate analysis indicated that the 15-gene signature was an independent prognostic factor. Moreover, its independent prognostic effect had been in silico validated on 169 adenocarcinomas without adjuvant chemo- or radio-therapy from DC and 85 NSCLC from Duke and 106 squamous cell carcinomas of the lung from the University of Michigan. Importantly, the 15-gene signature was able to predict the response to adjuvant chemotherapy with high-risk patients across the stages being benefited from adjuvant chemotherapy. This finding was also validated on DC dataset.
  • Adjuvant chemotherapy for completely resected early stage NSCLC was a research question until the results of a series of positive trials2,4, including BR.103, were published. However, whether chemotherapy played a beneficial role in stage IB remained to be clarified2-6. The present study showed that the stage IB patients were potentially able to be separated into low (49.3%, 36/73) and high (50.7%, 37/73) risk groups using the 15-gene signature. Upon administering the adjuvant chemotherapy to stage IB patients, the survival rate of patients with high risk was significantly improved (p=0.0698, FIG. 3F) whereas patients with low risk did not experience a benefit in survival (p=0.0758, FIG. 3E). Therefore the effect of chemotherapy on stage IB NSCLC was neutralized and thus gave an incorrect impression that no beneficial effect was existed3. Based on the evidence provided here and from the meta-analysis6, it may be concluded that 50.7% (37/73) stage IB NSCLC patients have the potential to benefit from adjuvant chemotherapy.
  • Another significance of the present study was that the signature was able to identify a subgroup (50%, 30/60) of patients from stage II who did not benefit from adjuvant chemotherapy (p=0.1498, FIG. 3G). In current practice, adjuvant chemotherapy is recommended for all patients. However, the 15-gene signature suggests that about a half of the stage II patients may not benefit from adjuvant chemotherapy.
  • The gene ontology analysis showed that in the 15-gene signature, 4 genes (FOSL2, HEXIM1, IKBKAP, MYT1L, and ZNF236) were involved in the regulation of transcription. EDN3 and STMN2 played a role in signal transduction. Transformed 3T3 cell double minute 2 (MDM2), an E3 ubiquitin ligase, which targets p53 protein for degradation, plays a key role in cell cycle and apoptosis. Dworakowska D. et al.24 reported that overexpression of MDM2 protein was correlated with low apoptotic index, which was associated with poorer survival. Myoglobin (MB) palyed a role in response to hypoxia and Uridine monophosphate synthetase (UMPS) participated in the ‘de novo’ pyrimidine base biosynthetic process, however, none of them has not been explored in lung cancer. The L1 cell adhesion molecule (L1CAM) involved in cell adhesion whose overexpression was associated with tumor metastasis and poor prognosis25-28. ATPase, Na+/K+ transporting, beta 1 polypeptide (ATP1B1) was involved in ion transport which was reported recently to be able to discriminate the serous low malignant potential and invasive epithelial ovarian tumors29. These findings indicated that cellular transcription, cell cycle and apoptosis, cell adhesion and response to hypoxia were important for lung cancer progression.
  • The range of expression levels of members of the 15-gene signature was broad, from very low expression level such as MDM2 and ZNF236 to fairly high expression such as TRIM14 or very high expression such as ATP1B1 (Table 4). Least variable gene (<5%), such as UMPS (Table 4), was also a member of the signature. These data suggested that it may not be a good practice to exclude low expressed and least variable probe set in the data pre-selection process in an arbitrary way. The signature generated using the present strategy performed better than that of Raponi's method of using the top 50 genes. There are only 3 genes (IKBKAP, L1CAM, and FAM64A) whose significance in association with survival is in the top 50 genes (Table 4).
    • Materials and Methods: Patients and Samples
  • Included in the JBR.10 protocol was the collection of snap-frozen or formalin-fixed paraffin embedded tumor samples for KRAS mutation analysis and tissue banking for future laboratory studies3. Altogether 445 of 482 randomized patients consented to banking. Snap-frozen tissues were collected from 169 Canadian patients (FIG. 4). Histological evaluation of the HE section from the snap-frozen tumor samples revealed 166 that contained an estimated >20% tumor cellularity; gene expression profiling was completed in 133 of these patient samples, using the U133A oligonucleotide microarrays (Affymetrix, Santa Clara, Calif.). Profiling was not completed in 33 patient samples. Of 133 patients with microarray profiles, 62 did not received post-operative adjuvant chemotherapy and were group as observation patients, while 71 patients were received chemotherapy. University Health Network Research Ethics Board approved the study protocol.
    • RNA Isolation and Microarray Profiling
  • Total RNA was isolated from frozen tumor samples after homogenization in guanidium isothiocyanate solution and acid phenol-chloroform extraction. The quality of isolated RNA was assessed initially by gel electrophoresis, followed by the Agilent Bioanalyzer. Ten micrograms of total RNA was processed, labeled, and hybridized to Affymetrix's HG-U133A GeneChips. Microarray hybridization was performed at the Center for Cancer Genome Discovery of Dana Farber Cancer Institute.
    • Microarray Data Analysis and Gene Annotation
  • The raw microarray data were pre-processed using RMAexpress v0.322. Probe sets were annotated using NetAffx v4.2 annotation tool and only grade A level probe sets23 (NA22) were included for further analysis. Because the microarray profiling was done in two separate batches at different times and unsupervised heuristic K-means clustering identified a systematic difference between the two batches (FIG. 6), the distance-weighted discrimination (DWD) method (https://genome.unc.edu/pubsup/dwd/index.html) was used to adjust the difference. The DWD method first finds a separating hyperplane between the two batches and adjusts the data by projecting the different batches on the DWD plane, discover the batch mean, and then subtracts out the DWD plane multiplied by this mean. The data were then transformed to Z score by centering to its mean and scaling to its standard deviation. This transformation was necessary for validation on different datasets in which different expression ranges are likely to exist, and for validation on different platforms, such as qPCR where the data scale is different.
    • Derivation of Signature
  • The pre-selected probe sets by univariate analysis at p<0.005 were selected by an exclusion procedure. The exclusion selection excluded one probe set at a time based on the resultant R square (R2, Goodness-of-fit 15,16) of the Cox model. It kept repeating until there was only one probe set left. The procedure was repeated until there was only one probe set left. An inclusion procedure was followed using the probe set left by the exclusion procedure as the starting probe set. It included one probe set at a time based on the resultant R2 of the Cox model. Finally, the R2 was plotted against the probe set and a set of minimum number of probe sets yet having the largest R2 was chosen as candidate signature. Gene signature was established after passing the internal validation by leave-one-out-cross-validation (LOOCV) and external validation on other datasets (listed below). All statistical analyses were performed using SAS v9.1 (SAS Institute, CA).
    • Validation in Separate Microarray Datasets
  • The prognostic value of this 15-gene signature was tested on separate microarray datasets. Three represented subsets of microarray data from the NCl Director's Challenge Consortium (DCC) for the Molecular Classification of Lung Adenocarcinoma (Nature Medicine, in review/in press). In total, the Consortium analyzed the profiles of 442 tumors, including 177 from University of Michigan (UM), 79 from H. L. Moffitt Cancer Centre (HLM), 104 from Memorial Sloan-Kettering Cancer Centre (MSK), and 82 from our group. As 39 of the latter tumors overlap with samples used in this study, only data from the first 3 groups were used for validation. In addition, patients who were noted as either unknown or having received adjuvant chemotherapy and/or radiotherapy were excluded. Therefore, the DCC dataset used in this validation study included only 169 patients: 67 from UM, 46 from HLM, 56 from MSK. Two additional published microarray datasets were also used for validation: the Duke's University dataset of 85 non-small cell lung cancer patients (Potti, et al, NEJM), and the University of Michigan dataset of 106 squamous cell carcinomas patients (UM-SQ) (Rapponi et al). Raw data of these microarray studies were downloaded and RMA pre-processed. The expression levels were Z score transformed after double log 2 transformation. Risk score was the Z score weighted by the coefficient of the Cox model from the OBS. Demographic data of the DC cohort was listed in Table 5.
    • Statistical Analysis
  • Risk score was the product of coefficient of Cox proportional model and the standardized expression level. The univariate association of the expression of the individual probe set with overall survival (date of randomization to date of last followup or death) was evaluated by Cox proportional hazards regression. A stringent p<0.005 was set as a selection criteria in order to minimize the possibility of false-positive results.
  • While the present invention has been described with reference to what are presently considered to be the preferred examples, it is to be understood that the invention is not limited to the disclosed examples. To the contrary, the invention is intended to cover various modifications and equivalent arrangements included within the spirit and scope of the appended claims.
  • All publications, patents and patent applications are herein incorporated by reference in their entirety to the same extent as if each individual publication, patent or patent application was specifically and individually indicated to be incorporated by reference in its entirety.
  • TABLE 1
    Baseline factors of BR.10 patients with and
    without microarray profiles
    Microarray No microarray
    All profiled profiled
    Patients (n = 133) (n = 349)
    Factor (n = 482) n % n % P value
    Treatment received
    ACT 231 71 53% 160 46% 0.14
    OBS 251 62 47% 189 54%
    Age
    <65 324 87 65% 237 68% 0.6
    ≧65 158 46 35% 112 32%
    Gender
    Male 314 91 68% 223 64% 0.35
    Female 168 42 32% 126 36%
    Performance Status
    0 236 67 50% 169 49% 0.72
    1 245 66 50% 179 51%
    Stage of Disease
    IB
    219 73 55% 146 42% 0.01
    II 263 60 45% 203 58%
    Surgery
    Pneumonectomy 113 33 25% 80 23% 0.66
    Other Resection 369 100 75% 269 77%
    Pathologic type
    Adenocarcinoma 256 71 53% 185 53% 0.56
    Squamous 179 52 39% 127 36%
    Other 47 10 8% 37 11%
    Ras Mutation Status
    Present 117 28 21% 89 26% 0.12*
    Absent 333 105 79% 228 65%
    Unknown 32 0 0% 32 9%
    *P-value: Without include those missing or unknown.
  • TABLE 2
    Comparison of 5-yr Survival (multivariate) of High and Low Risk Groups
    in Untreated Patients and Patients who Received Adjuvant Chemotherapy.
    n HR* 95% CI p value
    Observation/untreated Patients
    JBR.10 (randomized with 62 18.0  5.8-56.1 <0.0001
    microarray)
    Stage IB 34 29.9  4.5-197.4 0.0004
    Stage II 28 16.4  3.0-88.1 0.001
    DCC (no adjuvant 169 2.9 1.5-5.6 0.002
    therapy)
    UM 67 1.5 0.54-4.31 0.4
    HLM 46 1.2 0.43-3.60 0.7
    MSK 56 NA** NA
    Duke 85 1.5 0.81-2.89 0.19
    UM-Squamous 106 2.3 1.1-4.7 0.026
    Patients Treated With Adjuvant Chemotherapy
    BR.10 (randomized with 71 1.5 0.7-3.3 0.28
    microarray)
    BR.10 Stage I 39 1.7 0.5-5.6 0.36
    BR.10 Stage II 32 1.2 0.4-3.6 0.8
    DCC (not randomized) 41 1.1 0.5-2.5 0.8
    n: number of patients;
    HR: hazard ratio;
    CI: confidence interval
    *HR compares the survival of the poor prognostic group to that of the good prognostic group as determined by the 15-gene signature with the adjustment of stage and patients' age and gender. For BR.10, and Duke, the effect of histology was also adjusted
    **All events were in high risk group and female patients.
  • TABLE 3
    172 U133A probe sets that were prognostic at p < 0.005 for the 62 BR.10
    observation arm patients.
    Representative p
    Probe Set ID Public ID UniGene ID Gene Symbol Coefficients HR HRL HRH value
    200878_at AF052094 Hs.468410 EPAS1 −0.58 0.56 0.37 0.84 0.0048
    201228_s_at NM_006321 Hs.31387 ARIH2 0.47 1.60 1.17 2.18 0.0029
    201242_s_at BC000006 Hs.291196 ATP1B1 −0.69 0.50 0.35 0.71 0.0001
    201243_s_at NM_001677 Hs.291196 ATP1B1 −0.54 0.58 0.41 0.83 0.0028
    201301_s_at NM_001153 Hs.422986 ANXA4 −0.55 0.58 0.40 0.83 0.0028
    201502_s_at NM_020529 Hs.81328 NFKBIA −0.62 0.54 0.36 0.79 0.0016
    202023_at NM_004428 Hs.516664 EFNA1 −0.67 0.51 0.35 0.76 0.0009
    202035_s_at AF017987 Hs.213424 SFRP1 0.69 1.99 1.39 2.86 0.0002
    202036_s_at AF017987 Hs.213424 SFRP1 0.84 2.31 1.56 3.44 0.0000
    202037_s_at AF017987 Hs.213424 SFRP1 0.74 2.09 1.43 3.07 0.0002
    202490_at AF153419 Hs.494738 IKBKAP 0.42 1.53 1.17 1.99 0.0018
    202707_at NM_000373 Hs.2057 UMPS 0.60 1.81 1.24 2.66 0.0023
    202814_s_at NM_006460 Hs.15299 HEXIM1 0.59 1.80 1.20 2.70 0.0045
    203001_s_at NM_007029 Hs.521651 STMN2 0.55 1.73 1.21 2.47 0.0027
    203147_s_at NM_014788 Hs.575631 TRIM14 −0.56 0.57 0.39 0.82 0.0028
    203438_at AI435828 Hs.233160 STC2 0.67 1.96 1.29 2.96 0.0015
    203444_s_at NM_004739 Hs.173043 MTA2 0.38 1.46 1.12 1.89 0.0046
    203475_at NM_000103 Hs.511367 CYP19A1 0.56 1.76 1.23 2.52 0.0021
    203509_at NM_003105 Hs.368592 SORL1 −0.58 0.56 0.39 0.81 0.0020
    203928_x_at AI870749 Hs.101174 MAPT 0.44 1.55 1.15 2.10 0.0044
    203973_s_at M83667 Hs.440829 CEBPD −0.61 0.54 0.38 0.77 0.0005
    204179_at NM_005368 Hs.517586 MB 0.47 1.60 1.16 2.22 0.0044
    204267_x_at NM_004203 Hs.77783 PKMYT1 0.63 1.87 1.28 2.73 0.0011
    204338_s_at AL514445 Hs.386726 RGS4 0.57 1.77 1.23 2.53 0.0021
    204531_s_at NM_007295 Hs.194143 BRCA1 0.60 1.82 1.21 2.75 0.0043
    204584_at AI653981 Hs.522818 L1CAM 0.56 1.75 1.30 2.35 0.0002
    204684_at NM_002522 Hs.645265 NPTX1 0.48 1.61 1.18 2.19 0.0024
    204810_s_at NM_001824 Hs.334347 CKM 0.46 1.58 1.20 2.09 0.0012
    204817_at NM_012291 — ESPL1 0.53 1.70 1.24 2.34 0.0010
    204933_s_at BF433902 Hs.81791 TNFRSF11B 0.51 1.67 1.27 2.20 0.0003
    204953_at NM_014841 Hs.368046 SNAP91 0.59 1.81 1.31 2.49 0.0003
    205046_at NM_001813 Hs.75573 CENPE 0.62 1.86 1.28 2.70 0.0012
    205189_s_at NM_000136 Hs.494529 FANCC 0.53 1.70 1.21 2.40 0.0023
    205217_at NM_004085 Hs.447877 TIMM8A 0.64 1.90 1.26 2.85 0.0020
    205386_s_at NM_002392 Hs.567303 MDM2 0.49 1.63 1.19 2.23 0.0025
    205433_at NM_000055 Hs.420483 BCHE 0.58 1.79 1.23 2.62 0.0024
    205481_at NM_000674 Hs.77867 ADORA1 0.49 1.63 1.20 2.23 0.0020
    205491_s_at NM_024009 Hs.522561 GJB3 0.46 1.58 1.18 2.11 0.0021
    205501_at AI143879 Hs.348762 — 0.40 1.49 1.13 1.97 0.0043
    205825_at NM_000439 Hs.78977 PCSK1 0.59 1.81 1.24 2.65 0.0023
    205893_at NM_014932 Hs.478289 NLGN1 0.40 1.49 1.13 1.97 0.0048
    205938_at NM_014906 Hs.245044 PPM1E 0.52 1.68 1.22 2.31 0.0013
    205946_at NM_003382 Hs.490817 VIPR2 0.50 1.65 1.17 2.33 0.0043
    206043_s_at NM_014861 Hs.6168 ATP2C2 −0.55 0.57 0.39 0.84 0.0044
    206096_at AI809774 Hs.288658 ZNF35 0.55 1.73 1.20 2.49 0.0034
    206228_at AW769732 Hs.155644 PAX2 0.50 1.65 1.27 2.15 0.0002
    206232_s_at NM_004775 Hs.591063 B4GALT6 0.44 1.56 1.17 2.07 0.0021
    206401_s_at J03778 Hs.101174 MAPT 0.39 1.48 1.13 1.94 0.0049
    206426_at NM_005511 Hs.154069 MLANA 0.63 1.87 1.26 2.77 0.0018
    206496_at NM_006894 Hs.445350 FMO3 0.53 1.70 1.22 2.37 0.0018
    206505_at NM_021139 Hs.285887 UGT2B4 0.61 1.84 1.26 2.69 0.0017
    206524_at NM_003181 Hs.389457 T 0.78 2.18 1.35 3.53 0.0015
    206552_s_at NM_003182 Hs.2563 TAC1 0.97 2.63 1.53 4.53 0.0005
    206619_at NM_014420 Hs.159311 DKK4 0.54 1.72 1.20 2.45 0.0029
    206622_at NM_007117 Hs.182231 TRH 0.53 1.70 1.23 2.37 0.0015
    206661_at NM_025104 Hs.369998 DBF4B 0.55 1.73 1.27 2.36 0.0005
    206672_at NM_000486 Hs.130730 AQP2 0.37 1.45 1.13 1.84 0.0030
    206678_at NM_000806 Hs.175934 GABRA1 0.39 1.48 1.16 1.89 0.0014
    206799_at NM_006551 Hs.204096 SCGB1D2 0.41 1.51 1.15 1.99 0.0032
    206835_at NM_003154 Hs.250959 STATH 0.46 1.59 1.16 2.18 0.0042
    206940_s_at NM_006237 Hs.493062 POU4F1 0.54 1.72 1.23 2.40 0.0017
    206984_s_at NM_002930 Hs.464985 RIT2 0.47 1.59 1.16 2.20 0.0045
    207003_at NM_002098 Hs.778 GUCA2A 0.62 1.85 1.23 2.79 0.0032
    207028_at NM_006316 Hs.651453 MYCNOS 0.48 1.61 1.19 2.18 0.0020
    207208_at NM_014469 Hs.121605 HNRNPG-T 0.51 1.66 1.23 2.26 0.0010
    207219_at NM_023070 Hs.133034 ZNF643 0.60 1.82 1.27 2.60 0.0011
    207529_at NM_021010 — DEFA5 0.65 1.91 1.38 2.64 0.0001
    207597_at NM_014237 Hs.127930 ADAM18 0.63 1.87 1.36 2.58 0.0001
    207814_at NM_001926 Hs.711 DEFA6 0.61 1.85 1.21 2.81 0.0041
    207843_x_at NM_001914 Hs.465413 CYB5A −0.55 0.58 0.39 0.84 0.0047
    207878_at NM_015848 — KRT76 0.41 1.51 1.17 1.95 0.0017
    207937_x_at NM_023110 Hs.264887 FGFR1 0.43 1.54 1.14 2.08 0.0045
    208157_at NM_009586 Hs.146186 SIM2 0.45 1.56 1.19 2.05 0.0013
    208233_at NM_013317 Hs.468675 PDPN 0.54 1.72 1.18 2.49 0.0043
    208292_at NM_014482 Hs.158317 BMP10 0.44 1.55 1.17 2.05 0.0025
    208314_at NM_006583 Hs.352262 RRH 0.56 1.75 1.19 2.58 0.0044
    208368_s_at NM_000059 Hs.34012 BRCA2 0.62 1.86 1.26 2.73 0.0018
    208399_s_at NM_000114 Hs.1408 EDN3 0.48 1.61 1.18 2.20 0.0028
    208511_at NM_021000 Hs.647156 PTTG3 0.49 1.63 1.17 2.29 0.0043
    208684_at U24105 Hs.162121 COPA −0.52 0.59 0.41 0.85 0.0041
    208992_s_at BC000627 Hs.463059 STAT3 −0.67 0.51 0.34 0.77 0.0012
    209434_s_at U00238 — PPAT 0.43 1.54 1.15 2.06 0.0033
    209839_at AL136712 Hs.584880 DNM3 0.54 1.72 1.18 2.50 0.0049
    209859_at AF220036 Hs.368928 TRIM9 0.45 1.57 1.16 2.12 0.0032
    210016_at BF223003 Hs.434418 MYT1L 0.60 1.82 1.31 2.52 0.0003
    210247_at AW139618 Hs.445503 SYN2 0.64 1.89 1.30 2.75 0.0008
    210302_s_at AF262032 Hs.584852 MAB21L2 0.59 1.81 1.34 2.44 0.0001
    210315_at AF077737 Hs.445503 SYN2 0.66 1.94 1.31 2.87 0.0009
    210455_at AF050198 Hs.419800 C10orf28 0.57 1.76 1.24 2.50 0.0015
    210758_at AF098482 Hs.493516 PSIP1 0.42 1.52 1.17 1.97 0.0015
    210918_at AF130075 — — 0.46 1.59 1.24 2.04 0.0003
    211204_at L34035 Hs.21160 ME1 0.54 1.72 1.26 2.33 0.0006
    211264_at M81882 Hs.231829 GAD2 0.53 1.71 1.19 2.44 0.0034
    211341_at L20433 Hs.493062 POU4F1 0.57 1.77 1.21 2.58 0.0031
    211516_at M96651 Hs.68876 IL5RA 0.60 1.82 1.26 2.62 0.0013
    211772_x_at BC006114 Hs.89605 CHRNA3 0.52 1.69 1.22 2.33 0.0014
    212359_s_at W89120 Hs.65135 KIAA0913 −0.53 0.59 0.42 0.82 0.0019
    212528_at AI348009 Hs.633087 — −0.79 0.45 0.29 0.70 0.0004
    212531_at NM_005564 Hs.204238 LCN2 −0.57 0.56 0.38 0.84 0.0049
    213197_at AB006627 Hs.495897 ASTN1 0.66 1.93 1.36 2.74 0.0002
    213260_at AU145890 Hs.599993 — 0.51 1.67 1.18 2.35 0.0036
    213458_at AB023191 — KIAA0974 0.43 1.54 1.19 1.99 0.0010
    213482_at BF593175 Hs.476284 DOCK3 0.53 1.70 1.19 2.42 0.0032
    213603_s_at BE138888 Hs.517601 RAC2 −0.62 0.54 0.37 0.79 0.0017
    213917_at BE465829 Hs.469728 PAX8 0.52 1.69 1.21 2.36 0.0022
    214457_at NM_006735 Hs.592177 HOXA2 0.72 2.06 1.40 3.03 0.0002
    214608_s_at AJ000098 Hs.491997 EYA1 0.55 1.73 1.24 2.42 0.0013
    214665_s_at AK000095 Hs.406234 CHP −0.52 0.59 0.43 0.82 0.0014
    214822_at AF131833 Hs.495918 FAM5B 0.54 1.72 1.23 2.41 0.0017
    215102_at AK026768 Hs.633705 DPY19L1P1 0.49 1.64 1.22 2.20 0.0011
    215180_at AL109703 Hs.651358 — 0.43 1.54 1.16 2.06 0.0029
    215289_at BE892698 — ZNF749 0.46 1.58 1.19 2.09 0.0017
    215356_at AK023134 Hs.646351 ECAT8 0.46 1.58 1.15 2.17 0.0048
    215476_at AF052103 Hs.159157 — 0.49 1.63 1.21 2.21 0.0016
    215705_at BC000750 — PPP5C 0.52 1.68 1.22 2.32 0.0016
    215715_at BC000563 Hs.78036 SLC6A2 0.75 2.12 1.37 3.29 0.0008
    215850_s_at AK022209 Hs.651219 NDUFA5 0.48 1.62 1.18 2.23 0.0030
    215944_at U80773 — — 0.49 1.64 1.20 2.24 0.0019
    215953_at AL050020 Hs.127384 DKFZP564C196 0.47 1.59 1.16 2.19 0.0038
    215973_at AF036973 — HCG4P6 0.55 1.74 1.30 2.32 0.0002
    216050_at AK024584 Hs.406847 — 0.44 1.55 1.15 2.08 0.0035
    216066_at AK024328 Hs.429294 ABCA1 0.50 1.65 1.22 2.22 0.0010
    216240_at M34428 Hs.133107 PVT1 0.46 1.58 1.15 2.18 0.0046
    216881_x_at X07882 Hs.528651 PRB4 0.41 1.51 1.14 1.99 0.0042
    216989_at L13779 Hs.121494 SPAM1 0.46 1.58 1.15 2.16 0.0044
    217004_s_at X13230 Hs.387262 MCF2 0.39 1.48 1.14 1.91 0.0032
    217253_at L37198 Hs.632861 — 0.51 1.66 1.17 2.35 0.0041
    217995_at NM_021199 Hs.511251 SQRDL −0.82 0.44 0.29 0.66 0.0001
    218768_at NM_020401 Hs.524574 NUP107 0.63 1.88 1.31 2.70 0.0006
    218881_s_at NM_024530 Hs.220971 FOSL2 −0.52 0.60 0.42 0.85 0.0044
    218980_at NM_025135 Hs.436636 FHOD3 0.63 1.88 1.29 2.74 0.0011
    219000_s_at NM_024094 Hs.315167 DCC1 1.06 2.90 1.89 4.44 0.0000
    219171_s_at NM_007345 Hs.189826 ZNF236 0.56 1.76 1.20 2.56 0.0035
    219182_at NM_024533 Hs.156784 FLJ22167 0.48 1.62 1.18 2.22 0.0027
    219425_at NM_014351 Hs.189810 SULT4A1 0.74 2.11 1.41 3.14 0.0003
    219520_s_at NM_018458 Hs.527524 WWC3 −0.49 0.61 0.44 0.84 0.0029
    219537_x_at NM_016941 Hs.127792 DLL3 0.55 1.73 1.23 2.44 0.0018
    219617_at NM_024766 Hs.468349 C2orf34 0.53 1.70 1.19 2.43 0.0035
    219643_at NM_018557 Hs.470117 LRP1B 0.55 1.73 1.30 2.30 0.0001
    219704_at NM_015982 Hs.567494 YBX2 0.75 2.12 1.42 3.16 0.0002
    219882_at NM_024686 Hs.445826 TTLL7 0.51 1.66 1.18 2.35 0.0038
    219937_at NM_013381 Hs.199814 TRHDE 0.54 1.71 1.23 2.38 0.0015
    219955_at NM_019079 Hs.562195 L1TD1 0.60 1.82 1.25 2.65 0.0018
    220029_at NM_017770 Hs.408557 ELOVL2 0.52 1.68 1.18 2.40 0.0038
    220076_at NM_019847 Hs.156727 ANKH 0.77 2.17 1.53 3.07 0.0000
    220294_at NM_014379 Hs.13285 KCNV1 0.45 1.56 1.16 2.11 0.0036
    220366_at NM_022142 Hs.104894 ELSPBP1 0.53 1.69 1.19 2.41 0.0034
    220394_at NM_019851 Hs.199905 FGF20 0.61 1.84 1.30 2.60 0.0006
    220397_at NM_020128 Hs.591036 MDM1 0.41 1.51 1.17 1.95 0.0015
    220541_at NM_021801 Hs.204732 MMP26 0.50 1.64 1.24 2.18 0.0006
    220653_at NM_015363 — ZIM2 0.60 1.83 1.33 2.53 0.0002
    220700_at NM_018543 Hs.188495 WDR37 0.59 1.80 1.22 2.66 0.0029
    220703_at NM_018470 Hs.644603 C10orf110 0.59 1.80 1.26 2.58 0.0012
    220771_at NM_016181 Hs.633593 LOC51152 0.60 1.81 1.23 2.67 0.0025
    220817_at NM_016179 Hs.262960 TRPC4 0.47 1.60 1.19 2.14 0.0019
    220834_at NM_017716 Hs.272789 MS4A12 0.52 1.68 1.27 2.22 0.0003
    220847_x_at NM_013359 Hs.631598 ZNF221 0.50 1.65 1.19 2.28 0.0025
    220852_at NM_014099 Hs.621386 PRO1768 0.48 1.62 1.19 2.20 0.0022
    220970_s_at NM_030977 Hs.406714 KRTAP2-4/ 0.49 1.64 1.16 2.31 0.0050
    LOC644350
    220981_x_at NM_022053 Hs.648337 NXF2 0.45 1.56 1.19 2.05 0.0014
    220993_s_at NM_030784 Hs.632612 GPR63 0.38 1.46 1.13 1.88 0.0041
    221018_s_at NM_031278 Hs.333132 TDRD1 0.81 2.25 1.51 3.37 0.0001
    221077_at NM_018076 Hs.127530 ARMC4 0.56 1.76 1.25 2.47 0.0013
    221137_at AF118071 — — 0.46 1.59 1.15 2.20 0.0049
    221168_at NM_021620 Hs.287386 PRDM13 0.68 1.96 1.33 2.91 0.0007
    221258_s_at NM_031217 Hs.301052 KIF18A 0.62 1.86 1.34 2.58 0.0002
    221319_at NM_019120 Hs.287793 PCDHB8 0.40 1.49 1.14 1.96 0.0041
    221393_at NM_014627 — TAAR3 0.50 1.64 1.17 2.31 0.0043
    221591_s_at BC005004 Hs.592116 FAM64A 0.72 2.05 1.38 3.05 0.0004
    221609_s_at AY009401 Hs.29764 WNT6 0.40 1.50 1.15 1.95 0.0028
    221718_s_at M90360 Hs.459211 AKAP13 −0.64 0.53 0.36 0.78 0.0013
    221950_at AI478455 Hs.202095 EMX2 0.67 1.96 1.41 2.72 0.0001
  • TABLE 4
    Features of 15 probe sets in the gene signature
    Rank of Rank of Rank of
    Entrez expression variation significant
    Gene Gene [n = 19619 [n = 19619 [n = 172
    Probe Set Symbol Gene Title ID Coef.* (%)] (%)] (%)]
    201243_s_at ATP1B1 ATPase, Na+/K+ transporting, beta 1 481 −0.54  517 (2.6) 2224 (11.3) 111 (64.5)
    polypeptide
    203147_s_at TRIM14 Tripartite motif-containing 14 8518 −0.56  3532 (18.0) 9499 (48.4) 112 (65.1)
    221591_s_at FAM64A Family with sequence similarity 64, member A 7372 0.72  6171 (31.5) 6108 (31.1)  29 (16.9)
    218881_s_at FOSL2 FOS-like antigen 2 10614 −0.52  6526 (33.3) 12445 (63.4)  155 (90.1)
    202814_s_at HEXIM1 Hexamethylene bis-acetamide inducible 1 11075 0.59  7415 (37.8) 9026 (46.0) 161 (93.6)
    204179_at MB myoglobin 9830 0.47  7703 (39.3) 7942 (40.5) 156 (90.7)
    204584_at L1CAM L1 cell adhesion molecule 4151 0.56  9327 (47.5) 3329 (17.0) 17 (9.9)
    202707_at UMPS Uridine monophosphate synthetase 3897 0.60 12311 (62.8) 18737 (95.5)  101 (58.7)
    208399_s_at EDN3 Endothelin 3 4193 0.48 16344 (83.3) 8234 (42.0) 110 (64.0)
    203001_s_at STMN2 Stathmin-like 2 2315 0.55 16948 (86.4) 5690 (29.0) 109 (63.4)
    210016_at MYT1L Myelin transcription factor 1-like 1908 0.60 17902 (91.2) 18637 (95.0)   27 (15.7)
    202490_at IKBKAP Inhibitor of kappa light polypeptide gene 23040 0.42 18769 (95.7) 10412 (53.1)   84 (48.8)
    enhancer in B-cells, kinase complex-
    associated protein
    206426_at MLANA Melan-A 2355 0.63 19159 (97.7) 17172 (87.5)   81 (47.1)
    205386_s_at MDM2 Mdm2, transformed 3T3 cell double minute 2 7776 0.49 19251 (98.1) 14275 (72.8)  104 (60.5)
    219171_s_at ZNF236 Zinc finger protein 236 54478 0.56 19383 (98.8) 17046 (86.9)  132 (76.7)
    *Coefficient of the Cox model
  • TABLE 5
    Demographic distributions of patients in validation sets
    Clinical DCC, All DCC, UM DCC, HLM DCC, MSK Duke UM-SQ
    Factors n = 360 (%) n = 177 (%) n = 79 (%) n = 104 (%) n = 89 (%) n = 129 (%)
    Pathology Type
    Adeno  360 (100) 177 (100)  79 (100) 104 (100) 43 (48) 0
    Non-Adeno  0 (0) 0 (0) 0 (0) 0 (0) 46 (52) 129 (100)
    Disease stage
    I 220 (61) 116 (66)  41 (52) 63 (61) 67 (75) 73 (57)
    II  69 (19) 29 (16) 20 (25) 20 (19) 18 (20) 33 (25)
    III  69 (19) 32 (18) 16 (20) 21 (20) 3 (3) 23 (18)
    IV  0 (0) 0 (0) 0 (0) 0 (0) 1 (2) 0 (0)
    Unknown  2 (1) 0 (0) 2 (3) 0 (0) 0 (0) 0 (0)
    Adjuvant chemotherapy
    No 210 (58) 76 (43) 61 (77) 73 (70)  89 (100) NS
    Yes  64 (18) 17 (10) 16 (20) 31 (30) 0 (0) NS
    Unknown  86 (24) 84 (47) 2 (3) 0 (0) 0 (0) NS
    Adjuvant radiotherapy
    No 209 (58) 76 (43) 57 (72) 76 (73)  89 (100) NS
    Yes  64 (18) 17 (10) 19 (24) 28 (27) 0 (0) NS
    Unknown  87 (24) 84 (47) 3 (4) 0 (0) 0 (0) NS
    Age (year)
    <65 163 (45) 87 (49) 17 (34) 49 (47) 33 (37) 52 (40)
    ≧65 197 (55) 90 (51) 25 (66) 55 (53) 56 (63) 77 (60)
    Gender
    Male 177 (49) 100 (56)  40 (51) 37 (36) 54 (61) 82 (64)
    Female 183 (51) 77 (44) 39 (49) 67 (64) 35 (39) 47 (36)
    DCC: Directors' Challenge Consortium;
    UM: University of Michigan;
    HLM: H. Lee Moffitt Cancer Center;
    MSK: Memorial Sloan-Kettering Cancer Center;
    NS: Not specified
  • TABLE 6
    Adjuvant therapies in the Director's Challenge Consortium (DCC)
    Patients
    Adjuvant radiotherapy
    Adjuvant Chemotherapy No Yes Unknown Total
    All
    No 190 20 0 210
    Yes 19 44 1 64
    Unknown 0 0 86 86
    University of Michigan (UM)
    No 76 0 0 76
    Yes 0 17 0 17
    Unknown 0 0 84 84
    H. Lee Moffitt (HLM)
    No 51 10 0 61
    Yes 6 9 1 16
    Unknown 0 0 2 2
    Memorial Sloan-Kettering (MSK)
    No 63 10 0 73
    Yes 13 18 0 31
    Unknown 0 0 0 0
  • TABLE 7
    Primers for qPCR validation
    SEQ SEQ Amplicon
    Gene ID NO Forward ID NO Reverse Length Tm
    FAM64A 173 AGTCACTCACCCACTGTGTTTCTG 188 GGTAGGGAAAGGAGGGATGAGA 71 83
    MB 174 CTGTGTTCTGCATGGTTTGGAT 189 GGTTGGAAGAAGTTCGGTTGG 71 76
    EDN3 175 ATTTGAGTGGGTGTCCAGGG 190 GGTCAAGGCCAATGCTCTGT 71 80
    ZNF236 176 AAAGGACCGCATCAGTGAGC 191 AGCAGTTGGCGTGCTTGG 71 85
    FOSL2 177 AAGAAGATTGGGCAGTTGGGT 192 TCCTGCTACTCCTGGCTCATTC 71 80
    MYT1L 178 AAGATAAACAGCCCCAGGAACC 193 CCACTGAGGAGCTGTCTGCTTT 72 81
    MLANA 179 GTAGGAAAAATGCAAGCCATCTCT 194 CATGATTAGTACTGCTAGCGGACC 77 74
    L1CAM 180 AAAGGAAAGATTGGTTCTCCCAG 195 AGTAGACCAAGCACAGGCATACAG 71 81
    TRIM14 181 TCACAGCTCCCTCCAGAAGC 196 GATGAGGACTGGGAGAGGGTT 71 82
    STMN2 182 CAGGCTTTTGAGCTGATCTTGAA 197 TTTGGAGAAGCTAAAGTTCGTGG 71 79
    UMPS 183 GCCAACAGTACAATAGCCCACAA 198 CCACGACCTACAATGATGATATCG 70 78
    ATP1B1 184 AGTTGGAAATGTGGAGTATTTTGGA 199 CATAGTACGGATAATACTGCAGAGGAA 71 78
    HEXIM1 185 CTGACCGAGAACGAACTGCA 200 AGTCCCCTTTGCCCCCTC 99 83
    IKBKAP 186 AGCGATTCACGTAGGATCTGC 201 ATCACCAGTGTTGGAAGTGGG 71 82
    MDM2 187 TGCCCCTTAATGCCATTGAA 202 TTTTGCCATGGACAATGCA 75 77
  • TABLE 8
    Risk group based on 15-gene signature in stage I patients
    n HR 95% CI p value
    BR.10 34 13.3  2.9-62.1 <0.0001
    Observation arm
    DCC
    141 3.3 1.5-7.4 0.002
    No adjuvant therapy
    UM
    57 1.9 0.6-6.1 0.28
    HLM 37 2.5 0.9-6.9 0.07
    MSK 47 NA NA 0.05
    Duke 67 1.06 0.5-2.2 0.88
    UM-SQ 73 1.4 0.6-3.1 0.44
    n: number of patients;
    HR: hazard ratio;
    CI: confidence interval
    *HR and CI cannot be calculated as no death occurred in the good prognosis group, p value the score test.
  • TABLE 9
    Probe set target sequences of the 15-gene signature
    SEQ
    ID NO: Probe setID Targetsequence
    35 205386_S_AT tttcccctagttgacctgtctataagagaattatatatttctaactatataaccctaggaatttagacaa
    cctgaaatttattcacatatatcaaagtgagaaaatgcctcaattcacatagatttcttctctttagtat
    aattgacctactttggtagtggaatagtgaatacttactataatttgacttgaatatgtagctcatcctt
    tacaccaactcctaattttaaataatttctactctgtcttaaatgagaagtacttggttttttttttctt
    aaatatgtatatgacatttaaatgtaacttattattttttttgagaccgagtcttgctctgttacccagg
    ctggagtgcagtgggtgatcttggctcactgcaagctctgccctccccgggttcgcaccattctcctgcc
    tcagcctcccaattagcttggcctacagtcatctgcc
    78 208399_S_AT ccgagccgagcttactgtgagtgtggagatgttatcccaccatgtaaagtcgcctgcgcaggggagggct
    gcccatctccccaacccagtcacagagagataggaaacggcatttgagtgggtgtccagggccccgtaga
    gagacatttaagatggtgtatgacagagcattggccttgaccaaatgttaaatcctctgtgtgtatttca
    taagttattacaggtataaaagtgatgacctatcatgaggaaatgaaagtggctgatttgctggtaggat
    tttgtacagtttagagaagcgattatttattgtgaaactgttctccactccaactcctttatgtggatct
    gttcaaagtagtcactgtatatacgtatagagaggtagataggtaggtagattttaaattgcattctgaa
    tacaaactcatactccttagagcttgaattacatttttaaaatgcatatgtgctgtttggcaccgtggca
    agatggtatcagagagaaacccatcaattgctcaaatactc
    4 201243_S_AT ggtgatgggttgtgttatgcttgtattgaatgctgtcttgacatctcttgccttgtcctccggtatgttc
    taaagctgtgtctgagatctggatctgcccatcactttggcctagggacagggctaattaatttgcttta
    tacattttcttttactttccttttttcctttctggaggcatcacatgctggtgctgtgtctttatgaatg
    ttttaaccattttcatggtggaagaattttatatttatgcagttgtacaattttatttttttctgcaaga
    aaaagtgtaatgtatgaaataaaccaaagtcacttgtttgaaaataaatctttattttgaactttataaa
    agcaatgcagtacccatagactggtgttaaatgttgtctacagtgcaaaatccatgttctaacatatgta
    ataattgccaggagtacagtgctcttgttgatcttgtattcagtcaggttaaaa
    22 204179_AT tgttccggaaggacatggcctccaactacaaggagctgggcttccagggctaggcccctgccgctcccac
    ccccacccatctgggccccgggttcaagagagagcggggtctgatctcgtgtagccatatagagtttgct
    tctgagtgtctgctttgtttagtagaggtgggcaggaggagctgaggggctggggctggggtgttgaagt
    tggctttgcatgcccagcgatgcgcctccctgtgggatgtcatcaccctgggaaccgggagtgcccttgg
    ctcactgtgttctgcatggtttggatctgaattaattgtcctttcttctaaatcccaaccgaacttcttc
    caacctccaaactggctgtaaccccaaatccaagccattaactacacctgacagtagcaattgtctgatt
    aatcactggccccttgaagacagcagaatgtccctttgcaatgaggaggagatctgggctgggcgggcca
    gctggggaagcatttgactatctggaacttgtgtgtgcctcctcaggtatggca
    169 221591_S_AT cacatctggacccatcagtgactgcctgccatagcctgagagtgtcttggggagaccttgcagaggggga
    gaattgttccttctgctttcctaggggactcttgagcttagaaactcatcgtacacttgaccttgagcct
    tctatttgcctcatctataacatgaagtgctagcatcagatatttgagagctcttagctctgtacccggg
    tgcctggtttttggggagtcatccgcagagtcactcacccactgtgtttctggtgccaaggctcttgagg
    gccccactctcatccctcctttccctaccagggactcggaggaaggcataggagatatttccaggcttac
    gaccctgggctcacgggtacctatttatatgctcagtgcagagcactgtggatgtgccaggaggggtagc
    cctgttcaagagcaatttctgccctttgtaaattatttaagaaacctgctttgtcattttattagaaaga
    aaccagcgtgtgactttcctagataacactgctttc
    15 203147_S_AT accaatcacgcctacagtgctttgaaggtttcctctcctaggctagtttcaaacaggccctaaacaagtc
    tgctgctgccctctcatcagacctccgcaccctcaccccaccatcacttanactactttaatccagttcc
    ttcaaagtgatacccccacaggtaagccctcagcatcctgaatacatcatccgcagcctgggaaccttct
    ccctcgtacagcacaggaacctgacacatagtaggcacacagtaaacgtttgtgaatgaatgggagtcat
    ccagtcctgactcttctgtctcttgaggtcccttgaatcttccgcttcctccccaccgatttcagcgtgt
    ccacatcacagctccctccagaagctgcaagagcttcttagcagttcctggtctgaaccctctcccagtc
    ctcatcttccaccctaaaactagagtgatcttcctaaaacttcacttaacccctcagctatgaaaaggct
    tccaggagtttccatgaa
    130 218881_S_AT aggtcacagtatcctcgtttgaaagataattaagatcccccgtggagaaagcagtgacacattcacacag
    ctgttccctcgcatgttatttcatgaacatgacctgttttcgtgcactagacacacagagtggaacagcc
    gtatgcttaaagtacatgggccagtgggactggaagtgacctgtacaagtgatgcagaaaggagggtttc
    aaagaaaaaggattttgtttaaaatactttaaaaatgttatttcctgcatcccttggctgtgatgcccct
    ctcccgatttcccaggggctctgggagggacccttctaagaagattgggcagttgggtttctggcttgag
    atgaatccaagcagcagaatgagccaggagtagcaggagatgggcaaagaaaactggggtgcactcagct
    ctcacaggggtaatca
    85 210016_AT ataacagcatatgcatttccccaccgcgttgtgtctgcagcttctttgccaatatagtaatgcttttagt
    agagtactagatagtatcagttttggattcttattgttatcacctatgtacaatggaaagggattttaag
    cacaaacctgctgctcatctaacgttggtacataatctcaaatcaaaagttatctgtgactattatatag
    ggatcacaaaagtgtcacatattagaatgctgacctttcatatggattattgtgagtcatcagagtttat
    tataacttattgttcatattcatttctaagttaatttaagtaatcatttattaagacagaattttgtata
    aactatttattgtgctctctgtggaactgaagtttgatttatttttgtactacacggcatgggtttgttg
    acactttaattttgctataaatgtgtggaatcacaagttgctgtgatacttcatttttaaattgtgaact
    ttgtacaaattttgtcatgctggatgttaacacat
    11 202490_AT gaggatggcacaagcgattcacgtaggatctgcccctgtgaccaaaacacctcccattgggccccacttc
    caacactggtgatcacatttcaacatgaggtttagggaaacaaatgcctaaactacagcactgtacataa
    actaacaggaaatgctgcttttgatcctcaaagaagtgatatagccaaaattgtaatttaagaagccttt
    gtcagtatagcaagatgttaactatagaatcaatctaggagtattcactgtaaaattcaacttttctgta
    tgtttgaacattttcacaatctcataggagtttttaaaaagaagagaaagaagatatactttgctttgga
    gaaatctactttttgacttacatgggtttgctgtaattaagtgcccaatattgaaaggctgcaagtactt
    tgtaatcactctttggcatgggtaaataagcatggtaacttatattgaaatatagtgctcttgctttgga
    taactgtaaagggacccatgctgatagactggaaa
    12 202707_AT aagttcattcttaagcttgctttttttgagactggtgtttgttagacagccacagtcctgtctgggttag
    ggtcttccacatttgaggatccttcctatctctccatgggactagactgctttgttattctatttatttt
    ttaatttttttcgagacaggatctcactctgttgcccaggatggagtgcagtggtgagatcacggctcat
    tgcagcctcgacctcccaggtgatcctcccacctcagcttccagattagctggtgctataggcatgcacc
    accacgtccatctaaatttctttattatttgtagagatgaggtcttgccatgttacccaggctggtctca
    actcctgggctcaagcgatcctcctgcctcagtctctcaaagtgctgggattacaggtgtgagccactgt
    gcccagcctaattgcagtaagacaa
    14 203001_S_AT acctcgcaacatcaacatctatacttacgatgatatggaagtgaagcaaatcaacaaacgtgcctctggc
    caggcttttgagctgatcttgaagccaccatctcctatctcagaagccccacgaactttagcttctccaa
    agaagaaagacctgtccctggaggagatccagaagaaactggaggctgcaggggaaagaagaaagtctca
    ggaggcccaggtgctgaaacaattggcagagaagagggaacacgagcgagaagtccttcagaaggctttg
    gaggagaacaacaacttcagcaagatggcggaggaaaagctgatcctgaaaatggaacaaattaaggaaa
    accgtgaggctaatctagctgctattattgaacgtctgcaggaaaaggagaggcatgctgcggaggtgcg
    caggaacaaggaactccaggttgaactgtctggctgaagcaagggagggtctggcacgcc
    13 202814_S_AT tgcctctcgcgcatggaggacgagaacaaccggctgcggctggagagcaagcggctgggtggcgacgacg
    cgcgtgtgcgggagctggagctggagctggaccggctgcgcgccgagaacctccagctgctgaccgagaa
    cgaactgcaccggcagcaggagcgagcgccgctttccaagtttggagactagactgaaacttttttgggg
    gagggggcaaaggggactttttacagtgatggaatgtaacattatatacatgtgtatataagacagtgga
    cctttttatgacacataatcagaagagaaatccccctggctttggttggtttcgtaaatttagctatatg
    tagcttgcgtgctttctcctgttcttttaattatgtgaaactgaagagttgcttttcttgttttcctttt
    tagaagtttttttccttaatgtgaaagtaatttgaccaagttataatgcatttttgtttttaacaaatcc
    cctccttaaacggagctataaggtggccaaatctga
    133 219171_S_AT cttttgttcttgctgggttatttattttgattttagcattaaatgtcatctcaggatatctctaaaaagg
    ggttgtttaattcctaattgtatagaaagctagtttggtgaattgtattggttaattgactgtttaaggc
    cttaacaggtgaatctagagcctacttttattttggttaaagaaaaagaaaatatcaataattcaatttt
    gtgtcttttctcaatttattagcaaacacaagacattttatgtattatttcgatttacttcctaattata
    aaagctgcttttttgcagaacattccttgaaaatataaggttttgaaaagacataattttacttgaatct
    ttgtggggtacaggttgatctttatattttactggttgttttaaaaattctagaaaagagatttctaggc
    ctcatgtataaccagggttttgaggataaagaactgtatttttagaactatctcatcatagcatatctgc
    tttggaataactat
    49 206426_AT gtaaagatcctatagctctttttttttgagatggagtttcgcttttgttgcccaggctggagtgcaatgg
    cgcgatcttggctcaccataacctccgcctcccaggttcaagcaattctcctgccttagcctcctgagta
    gctgggattacaggcgtgcgccactatgcctgactaattttgtagttttagtagagacggggtttctcca
    tgttggtcaggctggtctcaaactcctgacctcaggtgatctgcccgcctcagcctcccaaagtgctgga
    attacaggcgtgagccaccacgcctggctggatcctatatcttaggtaagacatataacgcagtctaatt
    actttcacttcaaggctcaatgctattctaactaatgacaagtattttctactaaaccagaaattggtag
    aaggatttaaataagtaaaagctactatgtactgccttagtgctgatgcctgtgtactgccttaaatgta
    cctatggcaatttagctctcttgggttcccaaatccctctcacaagaatgt
    26 204584_AT cctccctatcgtctgaacagttgtcttcctcagcctcctcccgcccccaccttgggaatgtaaatacacc
    gtgactttgaaagtttgtacccctgtccttccctttacgccactagtgtgtaggcagatgtctgagtccc
    taggtggtttctaggattgatagcaattagctttgatgaacccatcccaggaaaaataaaaacagacaaa
    aaaaaaggaaagattggttctcccagcactgctcagcagccacagcctccctgtatgcctgtgcttggtc
    tactgataagccctctacaaaa
  • TABLE 10
    Coefficient of individual genes in 15-gene signature: Principal
    Component values
    Gene
    Gene Symbol Probe set pc1 pc2 pc3 pc4
    1 ATP1B1 201243_s_at −0.189 −0.423 0.229 0.059
    2 IKBKAP 202490_at 0.364 0.070 −0.357 −0.120
    3 UMPS 202707_at 0.353 −0.009 0.136 0.011
    4 HEXIM1 202814_s_at −0.108 0.504 0.265 0.279
    5 STMN2 203001_s_at 0.326 0.044 −0.100 −0.122
    6 TRIM14 203147_s_at −0.148 0.212 0.132 −0.368
    7 MB 204179_at 0.197 0.028 0.548 −0.161
    8 L1CAM 204584_at 0.042 0.510 0.077 0.276
    9 MDM2 205386_s_at 0.180 0.081 0.325 −0.500
    10 MLANA 206426_at 0.366 −0.240 0.114 0.157
    11 EDN3 208399_s_at 0.413 0.042 −0.188 −0.260
    12 MYT1L 210016_at 0.270 0.014 0.273 0.245
    13 FOSL2 218881_s_at 0.036 −0.209 −0.225 0.190
    14 ZNF236 219171_s_at 0.188 −0.313 0.297 0.332
    15 FAM64A 221591_s_at 0.283 0.216 −0.174 0.320
    Eigenvalues of principal 3.33 1.82 1.37 1.32
    components
    Weight of each PC for risk 0.557 0.328 0.430 0.335
    score
    Risk score = 0.557 * PC1 + 0.328 * PC2 + 0.43 * PC3 + 0.335 * PC4
    where
    PC1 = Sum [pc1 * (expression data)]Gene 1-15
    PC2 = Sum [pc2 * (expression data)]Gene 1-15
    PC3 = Sum [pc3 * (expression data)]Gene 1-15
    PC4 = Sum [pc4 * (expression data)]Gene 1-15
    Patients classified as high risk or lower risk according to risk score ≧−0.1 or <−0.1.
  • TABLE 11
    Probe set target sequences for 172 genes
    SEQ Probe Gene
    ID NO: Set ID Symbol Target Sequence
    1 200878_at EPAS1 cactttgcaactccctgggtaagagggacgacacctctggtttttcaataccaattacatgg
    aacttttctgtaatgggtacnaatgaagaagtttctaaaaacacacacaaagcacattgggc
    caactatttagtaagcccggatagacttattgccaaaaacaaaaaatagctttcaaaagaaa
    tttaagttctatgagaaattccttagtcatggtgttgcgtaaatcatattttagctgcacgg
    cattaccccacacagggtggcagaacttgaagggttactgacgtgtaaatgctggtatttga
    tttcctgtgtgtgttgccctggcattaagggcattttacccttgcagttttactaaaacact
    gaaaaatattccaagcttcatattaaccctacctgtcaacgtaacgat
    2 201228_s_at ARIH2 cctacccacctcaaaatgtctgtactgcaagagggccctgggcctctgctttccatattcac
    gtttggccagagttgtagtcccaaagaagagcatgggtggcagatggtagggaattgaactg
    gcctgtgcaatgggcatggagcacaaggggtcacagcatgcctcctgccttaccgtggcagt
    acggagacagtccagaacatggtcttcttgccacggggtgttgttgtctctggtggtgctgc
    atgtctgtggctcacctttattcttgaaactgaggtttacctggatctggctactgaggcta
    gagcccacagcagaatggggttgggcctgtggccccccaaactagggggtgtgggttcatca
    cagtgttgccttttgtctcctaaagatagggatctacttttgaagggaattgttcctcccaa
    ata
    3 20124_s_at2 ATP1B1 agagctgatcacaagcacaaatctttcccactagccatttaataagttaaaaaaagatacaa
    aaacaaaaacctactagtcttgaacaaactgtcatacgtatgggacctacacttaatctata
    tgctttacactagctttctgcatttaataggttagaa
    4 201243_s_at ATP1B1 ggtgatgggttgtgttatgcttgtattgaatgctgtcttgacatctcttgccttgtcctccg
    gtatgttctaaagctgtgtctgagatctggatctgcccatcactttggcctagggacagggc
    taattaatttgctttatacattttcttttactttccttttttcctttctggaggcatcacat
    gctggtgctgtgtctttatgaatgttttaaccattttcatggtggaagaattttatatttat
    gcagttgtacaattttatttttttctgcaagaaaaagtgtaatgtatgaaataaaccaaagt
    cacttgtttgaaaataaatctttattttgaactttataaaagcaatgcagtaccccatagac
    tggtgttaaatgttgtctacagtgcaaaatccatgttctaacatatgtaataattgccagga
    gtacagtgctcttgttgatcttgtattcagtcaggttaaaa
    5 201301_s_at ANXA4 ggtgaaatttctaactgttctctgttcccggaaccgaaatcacctgttgcatgtgtttgatg
    aatacaaaaggatatcacagaaggatattgaacagagtattaaatctgaaacatctggtagc
    tttgaagatgctctgctggctatagtaaagtgcatgaggaacaaatctgcatattttgctga
    aaagctctataaatcgatgaagggcttgggcaccgatgataacaccctcatcagagtgatgg
    tttctcgagcagaaattgacatgttggatatccgggcacacttcaagagactctatggaaag
    tctctgtactcgttcatcaagggtgacacatctggagactacaggaaagtactgcttgttct
    ctgtggaggagatgattaaaataaaaatcccagaaggacaggaggattctcaacactttgaa
    tttttttaacttcatttttctacactgctattatcattatctc
    6 20150_s_at 2NFKBIA ccaactacaatggccacacgtgtctacacttagcctctatccatggctacctgggcatcgtg
    gagcttttggtgtccttgggtgctgatgtcaatgctcaggagccctgtaatggccggactgc
    ccttcacctcgcagtggacctgcaaaatcctgacctggtgtcactcctgttgaagtgtgggg
    ctgatgtcaacagagttacctaccagggctattctccctaccagctcacctggggccgccca
    agcacccggatacagcagcagctgggccagctgacactagaaaaccttcagatgctgccaga
    gagtgaggatgaggagagctatgacacagagtcagagttcacggagttcacagaggacgagc
    tgccctatgatgactgtgtgtttggaggccagcgtctgacgttatgag
    7 202023_at EFNA1 ccaccttcacctcggagggacggagaaagaagtggagacagtcctttcccaccattcctgcc
    tttaagccaaagaaacaagctgtgcaggcatggtcattaaggcacagtgggagctgagctgg
    aaggggccacgtggatgggcaaagcttgtcaaagatgccccctccaggagagagccaggatg
    cccagatgaactgactgaaggaaaagcaagaaacagtttcttgcttggaagccaggtacagg
    agaggcagcatgcttgggctgacccagcatctcccagcaagacctcatctgtggagctgcca
    cagagaagtttgtagccaggtactgcattctctcccatcctggggcagcactccccagagct
    gtgccagcaggggggctgtgccaacctgttcttagagtgtagctgtaagggcagtgcccatg
    tgtacattctgcctagagtgtagcctaaagggcagggcccacgtgtatagtatctgta
    8 202035_s_at SFRP1 tcggccagcgagtacgactacgtgagcttccagtcggacatcggcccgtaccagagcgggcg
    cttctacaccaagccacctcagtgcgtggacatccccgcggacctgcggctgtgccacaacg
    tgggctacaagaagatggtgctgcccaacctgctggagcacgagaccatggcggaggtgaag
    cagcaggccagcagctgggtgcccctgctcaacaagaactgccacgccggcacccaggtctt
    cctctgctcgctcttcgcgcccgtctgcctggaccggcccatctacccgtgtcgctggctct
    gcgaggccgtgcgcgactcgtgcgagccggtcatgcagttcttcggcttctactggcccgag
    atgcttaagtgtgacaagttccccgagggggacgtctgcatcgccatgacgccgcccaatgc
    caccgaagcctccaagccccaaggcacaacggtgtgtcctccctgtgacaacgagttgaaat
    ctgaggccatcattgaacatctctgt
    9 202036_s_at SFRP1 gacaaaccatttccaacagcaacacagccactaaaacacaaaaagggggattgggcggaaag
    tgagagccagcagcaaaaactacattttgcaacttgttggtgtggatctattggctgatcta
    tgcctttcaactagaaaattctaatgattggcaagtcacgttgttttcaggtccagagtagt
    ttctttctgtctgctttaaatggaaacagactcataccacacttacaattaaggtcaagccc
    agaaagtgataagtgcagggaggaaaagtgcaagtccattatgtaatagtgacagcaaaggc
    ccaggggagaggcattgccttctctgcccacagtctttccgtgtgattgtctttgaatctga
    atcagccagtctcagatgccccaaagtttcggttcctatgagcccggggcatgatctgatcc
    ccaagacatg
    10 202037_s_at SFRP1 taacacttggctcttggtacctgtgggttagcatcaagttctccccagggtagaattcaatc
    agagctccagtttgcatttggatgtgtaaattacagtaatcccatttcccaaacctaaaatc
    tgtttttctcatcagactctgagtaactggttgctgtgtcataacttcatagatgcaggagg
    ctcaggtgatctgtttgaggagagcaccctaggcagcctgcagggaataacatactggccgt
    tctgacctgttgccagcagatacacaggacatggatgaaattcccgtttcctctagtttctt
    cctgtagtactcctcttttagatcc
    11 202490_at IKBKAP gaggatggcacaagcgattcacgtaggatctgcccctgtgaccaaaacacctcccattgggc
    cccacttccaacactggtgatcacatttcaacatgaggtttagggaaacaaatgcctaaact
    acagcactgtacataaactaacaggaaatgctgcttttgatcctcaaagaagtgatatagcc
    aaaattgtaatttaagaagcctttgtcagtatagcaagatgttaactatagaatcaatctag
    gagtattcactgtaaaattcaacttttctgtatgtttgaacattttcacaatctcataggag
    tttttaaaaagaagagaaagaagatatactttgctttggagaaatctactttttgacttaca
    tgggtttgctgtaattaagtgcccaatattgaaaggctgcaagtactttgtaatcactcttt
    ggcatgggtaaataagcatggtaacttatattgaaatatagtgctcttgctttggataactg
    taaagggacccatgctgatagactggaaa
    12 202707_at UMPS aagttcattcttaagcttgctttttttgagactggtgtttgttagacagccacagtcctgtc
    tgggttagggtcttccacatttgaggatccttcctatctctccatgggactagactgctttg
    ttattctatttattttttaatttttttcgagacaggatctcactctgttgcccaggatggag
    tgcagtggtgagatcacggctcattgcagcctcgacctcccaggtgatcctcccacctcagc
    ttccagattagctggtgctataggcatgcaccaccacgtccatctaaatttctttattattt
    gtagagatgaggtcttgccatgttacccaggctggtctcaactcctgggctcaagcgatcct
    cctgcctcagtctctcaaagtgctgggattacaggtgtgagccactgtgcccagcctaattg
    cagtaagacaa
    13 202814_s_at HEXIM1 tgcctctcgcgcatggaggacgagaacaaccggctgcggctggagagcaagcggctgggtgg
    cgacgacgcgcgtgtgcgggagctggagctggagctggaccggctgcgcgccgagaacctcc
    agctgctgaccgagaacgaactgcaccggcagcaggagcgagcgccgctttccaagtttgga
    gactagactgaaacttttttgggggagggggcaaaggggactttttacagtgatggaatgta
    acattatatacatgtgtatataagacagtggacctttttatgacacataatcagaagagaaa
    tccccctggctttggttggtttcgtaaatttagctatatgtagcttgcgtgctttctcctgt
    tcttttaattatgtgaaactgaagagttgcttttcttgttttcctttttagaagtttttttc
    cttaatgtgaaagtaatttgaccaagttataatgcatttttgtttttaacaaatcccctcct
    taaacggagctataaggtggccaaatctga
    14 203001_s_at STMN2 acctcgcaacatcaacatctatacttacgatgatatggaagtgaagcaaatcaacaaacgtg
    cctctggccaggcttttgagctgatcttgaagccaccatctcctatctcagaagccccacga
    actttagcttctccaaagaagaaagacctgtccctggaggagatccagaagaaactggaggc
    tgcaggggaaagaagaaagtctcaggaggcccaggtgctgaaacaattggcagagaagaggg
    aacacgagcgagaagtccttcagaaggctttggaggagaacaacaacttcagcaagatggcg
    gaggaaaagctgatcctgaaaatggaacaaattaaggaaaaccgtgaggctaatctagctgc
    tattattgaacgtctgcaggaaaaggagaggcatgctgcggaggtgcgcaggaacaaggaac
    tccaggttgaactgtctggctgaagcaagggagggtctggcacgcc
    15 203147_s_at TRIM14 accaatcacgcctacagtgctttgaaggtttcctctcctaggctagtttcaaacaggcccta
    aacaagtctgctgctgccctctcatcagacctccgcaccctcaccccaccatcacttanact
    actttaatccagttccttcaaagtgatacccccacaggtaagccctcagcatcctgaataca
    tcatccgcagcctgggaaccttctccctcgtacagcacaggaacctgacacatagtaggcac
    acagtaaacgtttgtgaatgaatgggagtcatccagtcctgactcttctgtctcttgaggtc
    ccttgaatcttccgcttcctccccaccgatttcagcgtgtccacatcacagctccctccaga
    agctgcaagagcttcttagcagttcctggtctgaaccctctcccagtcctcatcttccaccc
    taaaactagagtgatcttcctaaaacttcacttaacccctcagctatgaaaaggcttccagg
    agtttccatgaa
    16 203438_at STC2 gtccacattcctgcaagcattgattgagacatttgcacaatctaaaatgtaagcaaagtagt
    cattaaaaatacaccctctacttgggctttatactgcatacaaatttactcatgagccttcc
    tttgaggaaggatgtggatctccaaataaagatttagtgtttattttgagctctgcatctta
    acaagatgatctgaacacctctcctttgtatcaataaatagccctgttattctgaagtgaga
    ggaccaagtatagtaaaatgctgacatctaaaactaaataaatagaaaacaccaggccagaa
    ctatagtcatactcacacaaagggagaaatttaaactcgaaccaagcaaaaggcttcacgga
    aatagcatggaaaaacaatgcttccagtggccacttcctaaggaggaacaaccccgtctgat
    ctcagaattggcaccacgtgagcttgctaagtgataatatctgtttctactacggatttagg
    caacaggacctgtacattgtcacattgcat
    17 203444_s_at MTA2 cacaaaggataccagggccctacggaaggctctgacccatctggaaatgcggcgagctgctc
    gccgacccaacttgcccctgaaggtgaagccaacgctgattgcagtgcggccccctgtccct
    ctacctgcaccctcacatcctgccagcaccaatgagcctattgtcctggaggactgagcacc
    tgtggggaagggaggtgggctgagaggtagagggtggatgcccagggcacccaaacctccct
    tccctttcgtgtcgaagggagtgaggagtgaattaaggaagagagcaagtgagtgtgtgtcc
    ctggaggggttgggcgccctctggtgttaccacctcgagacttgtctcatgcctccatgctt
    gccgatggaggacagactgcaggaacttggcccatgtgggaacctagcctgttttggggggt
    aggacccacagatgtcttggac
    18 203475_at CYP19A1 gaaattctttcccagtctgtcgatttatgcctcagccacttgcctgtgctacaattcattgt
    gttacctgtagattcaggtaatacaaaccatatataatcatcaagtaatacaaactaattta
    gtaatagcctgggttaagtattattagggccctgtgtctgcatgtagaaaaaaaaattcaca
    tgatgcacttcaaattcaaataaaaatccttttggcatgttcccatttttgcttagctcaat
    tagtgtggctaaccaagagataactgtaaatgtgacattgatttgctcttactacagctaca
    gtgattgggggaggaaaagtcccaacccaatgggctcaaacttctaaggggtactcctctca
    tccccttatccttctccctcgacattttctccctctttcttcccatgaccccaaagccaagg
    gcaacagatcagtaaagaacgtggtcagagtagaacccctg
    19 203509_at SORL1 gaatatcacagcttaccttgggaatactactgacaatttctttaaaatttccaacctgaaga
    tgggtcataattacacgttcaccgtccaagcaagatgcctttttggcaaccagatctgtggg
    gagcctgccatcctgctgtacgatgagctggggtctggtgcagatgcatctgcaacgcaggc
    tgccagatctacggatgttgctgctgtggtggtgcccatcttattcctgatactgctgagcc
    tgggggtggggtttgccatcctgtacacgaagcaccggaggctgcagagcagcttcaccgcc
    ttcgccaacagccactacagctccaggctggggtccgcaatcttctcctctggggatgacct
    gggggaagatgatgaagatgcccctatgataactggattttcagatgacgtccccatggtga
    tagcctgaaagagctttcctcactagaaacca
    20 203928_x_at MAPT gagtccagtcgaagattgggtccctggacaatatcacccacgtccctggcggaggaaataaa
    aagattgaaacccacaagctgaccttccgcgagaacgccaaagccaagacagaccacggggc
    ggagatcgtgtacaagtcgccagtggtgtctggggacacgtctccacggcatctcagcaatg
    tctcctccaccggcagcatcgacatggtagactcgccccagctcgccacgctagctgacgag
    gtgtctgcctccctggccaagcagggtttgtgatcaggcccctggggcggtcaataatngtg
    gagaggagagaatgagagagtgtggaaaaaaaaagaataatgacccggcccccgccctctgc
    ccccagctgctcctcgcagttcggttaattggttaatcacttaacctgcttttgtcactc
    21 203973_s_at CEBPD aagcggcgcaaccaggagatgcagcagaagttggtggagctgtcggctgagaacgagaagct
    gcaccagcgcgtggagcagctcacgcgggacctggccggcctccggcagttcttcaagcagc
    tgcccagcccgcccttcctgccggccgccgggacagcagactgccggtaacgcgcggccggg
    gcgggagagactcagcaacgacccatacctcagacccgacggcccggagcggagcgcgccct
    gccctggcgcagccagagccgccgggtgcccgctgcagtttcttgggacataggagcgcaaa
    gaagctacagcctggacttaccaccactaaactgcgagagaagctaaacgtgtttattttcc
    cttaaattattttgtaatggtagctttttctacatcttactcctgttgatgcagctaaggta
    catttgtaaaaagaaaaaaaaccagacttttcagacaaaccctttgtattgtagataagagg
    aaaagactgagcatgctcacttttttatattaa
    22 204179_at MB tgttccggaaggacatggcctccaactacaaggagctgggcttccagggctaggcccctgcc
    gctcccacccccacccatctgggccccgggttcaagagagagcggggtctgatctcgtgtag
    ccatatagagtttgcttctgagtgtctgctttgtttagtagaggtgggcaggaggagctgag
    gggctggggctggggtgttgaagttggctttgcatgcccagcgatgcgcctccctgtgggat
    gtcatcaccctgggaaccgggagtgcccttggctcactgtgttctgcatggtttggatctga
    attaattgtcctttcttctaaatcccaaccgaacttcttccaacctccaaactggctgtaac
    cccaaatccaagccattaactacacctgacagtagcaattgtctgattaatcactggcccct
    tgaagacagcagaatgtccctttgcaatgaggaggagatctgggctgggcgggccagctggg
    gaagcatttgactatctggaacttgtgtgtgcctcctcaggtatggca
    23 204267_x_at PKMYT1 ctgtggtgcatggcagcggaggccctgagccgagggtgggccctgtggcaggccctgcttgc
    cctgctctgctggctctggcatgggctggctcaccctgccagctggctacagcccctgggcc
    cgccagccaccccgcctggctcaccaccctgcagtttgctcctggacagcagcctctccagc
    aactgggatgacgacagcctagggccttcactctcccctgaggctgtcctggcccggactgt
    ggggagcacctccaccccccggagcaggtgcacacccagggatgccctggacctaagtgaca
    tcaactcagagcctcctcggggctccttcccctcctttgagcctcggaacctcctcagcctg
    tttgaggacaccctagacccaacctgagccccagactctgcctctgcacttttaacctttta
    tcctgtgtctctcccgtcgcccttgaaagctggggcccctcgggaactcccatggtcttctc
    tgcctggccgtgtctaataa
    24 204338_s_at RGS4 gaaacatcggctaggtttcctgctgcaaaaatctgattcctgtgaacacaattcttcccaca
    acaagaaggacaaagtggttatttccagagagtgagccaagaggaagtcaagaaatgggctg
    aatcactggaaaacctgattagtcatgaatgtgggctggcagctttcaaagctttcttgaag
    tctgaatatagtgaggagaatattgacttctggatcagctgtgaagagtacaagaaaatcaa
    atcaccatctaaactaagtcccaaggccaaaaagatctataatgaattcatctcagtccagg
    caaccaaagaggtgaacctggattcttgcaccagggaagagacaagccggaacatgctagag
    cctacaataacctgctttgatgaggcccagaagaagattttcaacctgatggagaaggattc
    ctaccgccgcttcctcaagtctcgattctatcttgatttggtcaacccgtcca
    25 204531_s_at BRCA1 ttcaagaaccggtttccaaagacagtcttctaattcctcattagtaataagtaaaatgttta
    ttgttgtagctctggtatataatccattcctcttaaaatataagacctctggcatgaatatt
    tcatatctataaaatgacagatcccaccaggaaggaagctgttgctttctttgaggtgattt
    ttttcctttgctccctgttgctgaaaccatacagcttcataaataattttgcttgctgaagg
    aagaaaaagtgtttttcataaacccattatccaggactgtttatagctgttggaaggactag
    gtcttccctagcccccccagtgtgcaagggcagtgaagacttgattgtaca
    26 204584_at L1CAM cctccctatcgtctgaacagttgtcttcctcagcctcctcccgcccccaccttgggaatgta
    aatacaccgtgactttgaaagtttgtacccctgtccttccctttacgccactagtgtgtagg
    cagatgtctgagtccctaggtggtttctaggattgatagcaattagctttgatgaacccatc
    ccaggaaaaataaaaacagacaaaaaaaaaggaaagattggttctcccagcactgctcagca
    gccacagcctccctgtatgcctgtgcttggtctactgataagccctctacaaaa
    27 204684_at NPTX1 ttccttttgtagattcccagtttattttctaagactgcaaagatcactttgtcaccagccct
    gggacctgagaccaagggggtgtcttgtgggcagtgagggggtgaggagaggctggcatgag
    gttcagtcattccagtgagctccaaagaggggccacctgttctcaaaagcatgttggggacc
    aggaggtaaaactggccatttatggtgaacctgtgtcttggagctgacttactaagtggaat
    gagccgaggatttgaatatcagttctaaccttgatagaagaaccttgggttacatgtggttc
    acattaagaggatagaatcctttggaatcttatggcaaccaaatgtggcttgacgaagtcgt
    ggtttcatctctt
    28 204810_s_at CKM gcaagcaccccaagttcgaggagatcctcacccgcctgcgtctgcagaagaggggtacaggt
    gcggtggacacagctgccgtgggctcagtatttgacgtgtccaacgctgatcggctgggctc
    gtccgaagtagaacaggtgcagctggtggtggatggtgtgaagctcatggtggaaatggaga
    agaagttggagaaaggccagtccatcgacgacatgatccccgcccagaagtaggcgcctgcc
    cacctgccaccgactgctggaaccccagccagtgggagggcctggcccaccagagtcctgct
    ccctcactcctcgccccgccccctgtcccagagtccacctgggggctctctccacccttctc
    agagttccagtttcaaccagagttccaaccaatgggctccatcctctggattctggccaatg
    aaatatctccctggcagggtcctcttcttttcccagagctcctccccaaccaggagctctag
    ttaatg
    29 204817_at ESPL1 tgtttggctgtagcagtgcggccctggctgtgcatggaaacctggagggggctggcatcgtg
    ctcaagtacatcatggctggttgccccttgtttctgggtaatctctgggatgtgactgaccg
    cgacattgaccgctacacggaagctctgctgcaaggctggcttggagcaggcccaggggccc
    cccttctctactatgtaaaccaggcccgccaagctccccgactcaagtatcttattggggct
    gcacctatagcctatggcttgcctgtctctctgcggtaaccccatggagctgtcttattgat
    gctagaagcctcataactgttctacctc
    30 204933_s_at TNFRSF gataaaacggcaacacagctcacaagaacagactttccagctgctgaagttatggaaacatc
    11B aaaacaaagcccaagatatagtcaagaagatcatccaagatattgacctctgtgaaaacagc
    gtgcagcggcacattggacatgctaacctcaccttcgagcagcttcgtagcttgatggaaag
    cttaccgggaaagaaagtgggagcagaagacattgaaaaaacaataaaggcatgcaaaccca
    gtgaccagatcctgaagctgctcagtttgtggcgaataaaaaatggcgaccaagacaccttg
    aagggcctaatgcacgcactaaagcactcaaagacgtaccactttcccaaaactgtcactca
    gagtctaaagaagaccatcaggttccttcacagc
    31 204953_at SNAP91 agagaggtgctattcaagtgattctgaaggcaccccaaggtatatctgtaatttaaagatta
    ctgcaaatatctttactttactgtgggtttttagtacatctgttaatttagtgtttctttgt
    gtgttttgtagactagtgttcttccatccttcaactgagctcaaagtaggttttgttgtaac
    attgtgattaggatttaaactaattcagagaattgtatcttttactgtacatactgtattct
    ttaagttttaatttgttgtcatactgtctgtgctgatggcttggcttaagattttgatgcat
    aaatgaggtcactgttgatcagtgttgctagtagcttggcagctcttcataaaagcatattg
    ggttggaaaggtgtttgcctatttttca
    32 205046_at CENPE aatcagcatctttccaatgaggtcaaaacttggaaggaaagaacccttaaaagagaggctca
    caaacaagtaacttgtgagaattctccaaagtctcctaaagtgactggaacagcttctaaaa
    agaaacaaattacaccctctcaatgcaaggaacggaatttacaagatcctgtgccaaaggaa
    tcaccaaaatcttgtttttttgatagccgatcaaagtctttaccatcacctcatccagttcg
    ctattttgataactcaagtttaggcctttgtccagaggtgcaaaatgcaggagcagagagtg
    tggattctcagccaggtccttggcacgcctcctcaggcaaggatgtgcctgagtgcaaaact
    cagtagactcctctttgtcacttctctggagatccagcattccttatttggaaatgactttg
    tttatgtgtctatccctggtaatgatgttgtagtgcagcttaatttcaattcagtctttact
    ttgccactag
    33 205189_s_at FANCC ttccctccacctccaagacaggtggcggccgggcaggcactcttaagcccacctccccctct
    tgttgccttcgatttcggcaaagcctgggcaggtgccaccgggaaggaatggcatcgagatg
    ctgggcggggacgcggcgtggcgagggggcttgacggcgttggcggggctgggcacaggggc
    agccgcagggaggcagggatggcaaggcgtgaagccaccctggaaggaactggaccaaggtc
    ttcagaggtgcgacagggtctggaatctgaccttactctagcaggagtttttgtagactctc
    cctgatagtttagtttttgataaagcatgctggtaaaaccactaccctcagagagagccaaa
    aatacagaagaggcggagagcgcccctccaaccaggctgttattcccctggactc
    34 205217_at TIMM8A gtacatgggactatgcttttctcaaagccccattaactgcttcctataattttgatagtggg
    accacatacgtaaaaatctctcatttgtgtggagtcatttctgatttcaggggagatccttg
    tgtttatcagaaagggcagaagtaggggaagaataatttggtatccttatctagtgtttgat
    tgtcaatgctggagaaaaatatctgtaagagtgtttatacagtacacttcagttatcttgat
    ctccctttcctatatgatgatttgcttaaatatccatattaagtaagtctcaaggtagggta
    ggcagcctgagagtctagaggcctttagttataaaggaatctagccagtgaacataattctt
    attactagactgccacaaggaagaaattaacttaccctgtatatcagggtacaaaaaattca
    gtgatgtgcctaaataagttataaagatttaggccaatcagaagctaacagcagtttcaggt
    agaggtgcatgcctaatgttagttagtgtagattccatttactgcattctt
    35 205386_s_at MDM2 tttcccctagttgacctgtctataagagaattatatatttctaactatataaccctaggaat
    ttagacaacctgaaatttattcacatatatcaaagtgagaaaatgcctcaattcacatagat
    ttcttctctttagtataattgacctactttggtagtggaatagtgaatacttactataattt
    gacttgaatatgtagctcatcctttacaccaactcctaattttaaataatttctactctgtc
    ttaaatgagaagtacttggttttttttttcttaaatatgtatatgacatttaaatgtaactt
    attattttttttgagaccgagtcttgctctgttacccaggctggagtgcagtgggtgatctt
    ggctcactgcaagctctgccctccccgggttcgcaccattctcctgcctcagcctcccaatt
    agcttggcctacagtcatctgcc
    36 205433_at BCHE ggaaagcaggattccatcgctggaacaattacatgatggactggaaaaatcaatttaacgat
    tacactagcaagaaagaaagttgtgtgggtctctaattaatagatttaccctttatagaaca
    tattttcctttagatcaaggcaaaaatatcaggagcttttttacacacctactaaaaaagtt
    attatgtagctgaaacaaaaatgccagaaggataatattgattcctcacatctttaacttag
    tattttacctagcatttcaaaacccaaatggctagaacatgtttaattaaatttcacaatat
    aaagttctacagttaattatgtgcatattaaaacaatggcctggttcaatttctttctttcc
    ttaataaatttaagttttttccccccaaaattatcagtgctctgcttttagtcacgtgtatt
    ttcattaccactcgtaaaaaggtatcttttttaaatgaattaaatattgaaacactgtacac
    catagtttaca
    37 205481_at ADORA1 gaggagaacactagacatgccaactcgggagcattctgcctgcctgggaacggggtggacga
    gggagtgtctgtaaggactcagtgttgactgtaggcgcccctggggtgggtttagcaggctg
    cagcaggcagaggaggagtacccccctgagagcatgtgggggaaggccttgctgtcatgtga
    atccctcaatacccctagtatctggctgggttttcaggggctttggaagctctgttgcaggt
    gtccgggggtctaggactttagggatctgggatctggggaaggaccaacccatgccctgcca
    agcctggagcccctgtgttggggggcaaggtgggggagcctggagcccctgtgtgggagggc
    gaggcgggggagcctggagcccctgtgtgggagggcgaggcgggggatcctggagcccctgt
    gtcggggggcgagggaggggaggtggccgtcggttgaccttctgaacatgagtgtcaactcc
    aggacttgcttccaagcccttccctctgttggaaattgggtgtgccctggctcc
    38 205491_s_at GJB3 tgcttccagccttcgtaattagacttcaccctgagtacacacacaatcactgccactctcac
    tatagacaaaccacactccctcctctgtcacccagtcactgccatctcaacacacatcccca
    ccctgtgtacacacaatctctgttattcatactctcactccttatgcgcactctcaacaggg
    catgtagtctgcactcaagcatgccatcccagcctcaccctgcattttattcggctcatccc
    attttccctgaacattttcgctgaactagggccctggcaggatgctgggactgtgcaaggag
    gtaggacctatgcccacggagctaagagacaggaacacaggctcatctcccgcactaaccaa
    cccctgggatggctcacagcctgctcccagtgctgtgtcatgacctgaa
    39 205501_at PDE10A atgcttgcccaacacactgtgaaatagttaccaaaatttgtacaaatgcagcatcttcattc
    tttctgagaagacaagatggttttctttacatgaacaaatgaacaaaagagatcctagatcc
    ataacgtagctaaggcatctaagagtttgctgttgataatcttgctgaccaaaaactactgg
    agagtaacacaggttatatgccatcacaaatacaatgctcatgaagaactgatttgtagagt
    caatgaacctgtgtccagaattttaataggctctctattggaaggagaaagaatttcaagtt
    aacagtatctaactttatcatagttgatgttagtaaattttaaaaaatgattttatatgtat
    gacaaaaatctttgtaaaatgcgcaagtgcaataatttaaagaggtcttaactttgcattta
    taaattataaatattgtacatgtgtgtaattttttcatgtattcatttgcagtctttgtatt
    taaaa
    40 205825_at PCSK1 tttccattcccaatctagtgctagatgtataaatctttcttttgattcttcctaacaaaata
    ttttctgggttaaaaccccagccaactcattgggttgtagccaaaggttcactctcaagaag
    ctttaatatttaaataaaatcatattgaatgtttccaacctggagtataatattcagatata
    aaacagttttgtcagtctttcttagtgcctgtgtggatttttgtgaaaatgtcaaagagaaa
    acttatatactatttcccttgaaattttaaactatattttctttacaggtatttataatata
    ccaatgcttttatcaaacagaattttaaagagcataataaattatattaaagaaccaaaagt
    tttcctgagaataagaaagtttcacccaataaaatatttttgaaaggcatgttcctctgtca
    atgaaaaaaagtacatgtatgtgttgtgatattaaaagtgacatttgtctaatagcctaata
    caacatgtagctgagtttaacatgtgtggtcttg
    41 205893_at NLGN1 gaacctaggagagtcaacatctggaggattttagtctttcttacacatatgtgtgattttaa
    acgaatattctcagaccacaggaaactcttcatccccctgttgtttaccagtaacagtatat
    cacagacctttccaaatgtttgtatatgtaatcagatgtacatttatattgaaaaacaaatg
    agatggacttaaagagcacatcctgataaatactttctctctcacctgtactatatttctat
    tagactaaagttatgtgattttttttttacattttttcagatgactagcaattttgatagtt
    tataagataatgcaaagaactttctctgacaaactaactgcagtaacagaaacctttctttt
    cagttactctttttcaagaatgaaagattattatacaaaaaattgtatactacttgatggaa
    ccaactttgtacatcttggccatgtcactggtcattg
    42 205938_at PPM1E catgctaggctttctcagtggggaaaaaaatggctggatagaactgggacaaacacagaccc
    atctttaggggtctggattttgtaggtccgactacacagcagtgttaactcatttctcatgc
    cattagctctctacaaaataaagcaaagtagttctagtgtggtcgttataaaccaatattgt
    gaaaaatagcaactattcatttgttcacaacatgcgtatttatagagtagttaggtaccatt
    tgtaaggtaaatcctttaaaattctataatacatactaaaatagtggttattggtctgatat
    atgctgctcttggttctataaactagataaaagcagtgctttgtgaaatgcagtgttctctc
    ttaacgccactggtgataggaagtagttcccttcagttcaaatc
    43 205946_at VIPR2 ttcctcccctgtagggtttggacagacccacccccagccttgcccagctttcaaaggacaaa
    agggagcatcccccacctactctcaggtttttgaggaaacaaagatttgtggtaactgaagg
    tgttgggtcagtggccaggtgccgacactgagctgtgacccagaggggacgctgaggaagtg
    ggcgtgagtggacntgtcaggtggttaccaggcactggttgttgatggtcggtggttgggtg
    tgggcagtcatcagtcatcaggtgtgctcaggggacaatctcccctcaaccgcacatgtgcc
    actgttcagcggagctgactggtttcncctggtagagggnccggctgtttcctgacagatgc
    ctggtgagcaggggaagcaggacccagtggtcancaggtgtctttaactgtcattgtgtgtg
    gaatgtcgcagactcctccacgtggcgggaatgagct
    44 206043_s_at ATP2C2 gcaccacgacgatgacgttcacttgttttgtgtttttcgatctcttcaacgccttgacctgc
    cgctctcagaccaagctgatatttgagatcggctttctcaggaaccacatgttcctctactc
    cgtcctggggtccatcctggggcagctggcggtcatttacatccccccgctgcagagggtct
    tccagacggagaacctgggagcgcttgatttgctgtttttaactggattggcctcatccgtc
    ttcattttgtcagagctcctcaaactatgtgaaaaatactgttgcagccccaagagagtcca
    gatgcaccctgaagatgtgtagtggaccgcactccgcggcaccttccctaatcatctcgatc
    tggttgtgactgtggcccctgccgtgtctcctcgtcaggggagacttttaggaggccgcagc
    cttccatcaccggatcagtttttcctcttaggaaagctgcaggaacctcgtgggc
    45 206096_at ZNF35 gtggctttcctaggaatgggtcgtacaaagctaagtggtaatgatgctatttggggaaaggt
    cttttttgcttaantttgttttttaaaactctgatgattncttgagcaacaggcaggttatc
    tgcctggttgaattctggttgaaccgtgtattctaatatttctggttaagtggtgactgggt
    aaggaaaccacttggggtagcagttcaacaattcacttacgaatgtttataagctttccatt
    tcctaggtaattttttaaaagccagtcaaaacaaaaactttactgaaaatggacagaaatag
    gaaatggactttttccttactgtctatacctcctgaaccttggtattgtaaagatctgggga
    cctctgggtctgttctgaccattccctagtctccatggccaagcactcaaggattgatggac
    accacacaccagctatattcatttgccaagatcaacagctccttctccaaacaactcaagcc
    cccaattccnatcgcattcnnttngggtgagatgcaactaacagcccctt
    46 206228_at PAX2 gcaggctagatccgaggtggcagctccagcccccgggctcgccccctngcgggcgtgccccg
    cgcgccccgggcggccgaaggccgggccgccccgtcccgccccgtagttgctctttcggtag
    tggcgatgcgccctgcatgtctcctcacccgtggatcgtgacgactcgaaataacagaaaca
    aagtcaataaagtgaaaataaataaaaatccttgaacaaatccgaaaaggcttggagtcctc
    gcccagatctctctcccctgcgagccctttttatttgagaaggaaaaagagaaaagagaatc
    gtttaagggaacccggcgcccagccaggctccagtggcccgaacggggcggcgagggcggcg
    agggcgccgaggtccggcccatcccagtcctgtggggctggccgggcagagaccccggaccc
    aggcccaggcctaacctgctaaatgtccccggacggttctggtctcctcggccactttcagt
    gcgtcggttcgttttgattctttt
    47 206232_s_at B4GALT6 tgcagttttgcatgtaatcggttatacctttattggacttttatagacattttttatttgca
    tgaaaaaaactcactaaatttacatcactaaacaaaggttaacccttgtgtgaaatgaagga
    actgtcaataattgacagccaactaatacagtaaactgttatactagttttgagctttagac
    ctcagccttttgtgtggaagaagtcacagctttcttaggctttaaaggaaaagaaggaagga
    cttaaatagcttttcttcctaccgggattacctatgtttttccttgcttgcaatctcatctg
    attttgctagaaatcacaaccatattgtttatgcatattgcatgagtattaccaagaaaaaa
    atctttaaaagttgtgatgtgacatgatataaaggatctctttatgttaaatgtctttccat
    gtacctctggtgtgtcagggattttgtgcctcaaaaaatgtttccaaggttgtgtgtttata
    ctgtgtattttttttaaattcacggtgaacagcacttttattatttcca
    48 206401_s_at MAPT aggtggcagtggtccgtactccacccaagtcgccgtcttccgccaagagccgcctgcagaca
    gcccccgtgcccatgccagacctgaagaatgtcaagtccaagatcggctccactgagaacct
    gaagcaccagccgggaggcgggaaggtgcaaatagtctacaaaccagttgacctgagcaagg
    tgacctccaagtgtggctcattaggcaacatccatcataaaccaggaggtggccaggtggaa
    gtaaaatctgagaagcttgacttcaaggacagagtccagtcgaagattgggtccctggacaa
    tatcacccacgtccctggcggaggaaataaaaagattgaaacccacaagctgaccttccgcg
    agaacgccaaagccaagacagaccacggggcggagatcgtgtacaagtcgccagtggtgtct
    ggggacacgtctccacggcatctcagcaatgtctcctccaccggcagcatcgacatggtaga
    ctcgccccagctcgccacgctagctgacgaggtgtctgcctcc
    49 206426_at MLANA gtaaagatcctatagctctttttttttgagatggagtttcgcttttgttgcccaggctggag
    tgcaatggcgcgatcttggctcaccataacctccgcctcccaggttcaagcaattctcctgc
    cttagcctcctgagtagctgggattacaggcgtgcgccactatgcctgactaattttgtagt
    tttagtagagacggggtttctccatgttggtcaggctggtctcaaactcctgacctcaggtg
    atctgcccgcctcagcctcccaaagtgctggaattacaggcgtgagccaccacgcctggctg
    gatcctatatcttaggtaagacatataacgcagtctaattacatttcacttcaaggctcaat
    gctattctaactaatgacaagtattttctactaaaccagaaattggtagaaggatttaaata
    agtaaaagctactatgtactgccttagtgctgatgcctgtgtactgccttaaatgtacctat
    ggcaatttagctctcttgggttcccaaatccctctcacaagaatgt
    50 206496_at FMO3 aaagcccaacatcccatggctgtttctcacagatcccaaattggccatggaagtttattttg
    gcccttgtagtccctaccagtttaggctggtgggcccagguagtggccaggagccagaaatg
    ccatgctgacccagtgggaccggtcgttgaaacccatgcagacacgagtggtcgggagactt
    cagaagccttgcttctttttccattggctgaagctctttgcaattcctattctgttaatcgc
    tgttttccttgtgttgacctaatcatcattttctctaggatttctgaaagttactgacaata
    cccagacaggggctttgc
    51 206505_at UGT2B4 taattacgtctgaggctggaagctgggaaacccaataaatgaactcctttagtttattacaa
    caagaagacgttgtgatacaagagattcctttcttcttgtgacaaaacatctttcaaaactt
    accttgtcaagtcaaaatttgttttagtacctgtttaaccattagaaatatttcatgtcaag
    gaggaaaacattagggaaaacaaaaatgatataaagccatatgaggttatattgaaatgtat
    tgagcttatattgaaatttattgttccaattcacaggttacatgaaaaaaaatttactaagc
    ttaactacatgtcacacattgtacatggaaacaagaacattaagaagtccgactgacagtat
    cagtactgttttgcaaatactcagcatactttggatccatttcatgcaggattgtgttgttt
    taac
    52 206524_at T agcagtggaggagcacacggacctttccccagagcccccagcatcccttgctcacacctgca
    gtagcggtgctgtccaggtggcttacagatgaacccaactgtggagatgatgcagttggccc
    aacctcactgacggtgaaaaaatgtttgccagggtccagaaactttttttggtttatttctc
    atacagtgtattggcaactttggcacaccagaatttgtaaactccaccagtcctactttagt
    gagataaaaagcacactcttaatcttcttccttgttgctttcaagtagttagagttgagctg
    ttaaggacagaataaaatcatagttgaggacagcaggttttagttgaattgaaaatttgact
    gctctgccccctagaatgtgtgtattttaagcatatgtagctaatctcttgtgtt
    53 206552_s_at TAC1 ttcagcttcatttgtgtcaatgggcaatgacaggtaaattaagacatgcactatgaggaata
    attatttatttaataacaattgtttggggttgaaaattcaaaaagtgtttatttttcatatt
    gtgccaatatgtattgtaaacatgtgttttaattccaatatgatgactcccttaaaatagaa
    ataagtggttatttctcaacaaagcacagtgttaaatgaaattgtaaaacctgtcaatgata
    cagtccctaaagaaaaaaaatcattgctttgaagcagttgtgtcagctactgcggaaaagga
    aggaaactcctgacagtcttgtgcttttcctatttgttttcatggtgaaaatgtactgagat
    tttggtattacactgtatttgtatctctgaagcatgtttcatgttttgtgactatatagaga
    tgtttttaaaagtttcaatgtgattctaatgtcttcatttcattgtatgatg
    54 206619_at DKK4 ctgtctgacacggactgcaataccagaaagttctgcctccagccccgcgatgagaagccgtt
    ctgtgctacatgtcgtgggttgcggaggaggtgccagcgagatgccatgtgctgccctggga
    cactctgtgtgaacgatgtttgtactacgatggaagatgcaaccccaatattagaaaggcag
    cttgatgagcaagatggcacacatgcagaaggaacaactgggcacccagtccaggaaaacca
    acccaaaaggaagccaagtattaagaaatcacaaggcaggaagggacaagagggagaaagtt
    gtctgagaacttttgactgtggccctggactttgctgtgctcgtcatttttggacgaaaatt
    tgtaagccagtccttttggagggacaggtctgctccagaagagggcataaagacactgctca
    agctccagaaatcttccagcgttgcgactgtggccctggactactgtgtcgaagccaattga
    ccagcaatcggcagcatgctcgat
    55 206622_at TRH gccctcttcctttaggcatgtgagaaaatcagcctagcagtttaaaccccactttcctccac
    ttagcaccataggcaagggggcagatcccagagcccctctcaccccccccaccacaggcctg
    ctccttccttagccttggctaagatggtccttctgtgtcttgcaaagactccccaagtggac
    agggagcccctgggagggcagccagtgagggtggggtgggactgaagcgttgtgtgcaaatc
    cagcttccatcccctccccaacctggcaggattctccatgtgtaaacttcacccccaggacc
    caggatcttctcctttctgggcatccctttgtgggtgggcagagccctgacccacagctgtg
    ttactgcttggagaagcatatgtaggggcataccctgtggtgttgtgctgtgtctggctgtg
    ggataaatgtgtgtgggaatattgaaacatcgcctaggaattgtggtttgtatataaccctc
    taagcccctatcccttgtcgatgacagtca
    56 206661_at DBF4B accaggagtgtcagcttttagaaggatcatggtcatgtgagcttctggtcaccggaagccag
    aaatactcagctgccatgttgatccacaaaggtgggaggatgtggggaagggggaaagcggt
    gaggacgcagagtgcaggctgtggcctcggcatcccgcaggaggtccctagaacatgccgtt
    tcatgtcacctgctacagctctcccccagctagtatgatgatccgttttacaaatgcagaaa
    tgatcttaatattcatgaccactggccaggcgaggtggctcacacctgtaatcccagcactt
    tgggaggccaaggcgggtggatcacaaggtcaagagttcgagaccagcctgaccaacgtggt
    gaaaccccgtctctactaaaaatagaagcattagccgagcctggtgg
    57 206672_at AQP2 gcgcagagtagctgcttcctggacgtgcgcgcccaggccagtgctgtgagcaggcggggagg
    aggctgccggaggagcctgagcctggcaggttcccctgccctgaggctgtgagcagctagtg
    gtggcttctcctgcctttttcagggaactgggaaacttaggggactgagctggggagggagg
    caggtgggtggtaagagggaaactctggagagcctgcacccaggtactgagtggggagtgta
    cagaccctgccttgggggttctgggaatgatgcaactggttttactagtgtgcaagtgtgtt
    catccccaagttctcttttgtcctcacatgcagagttgtgcatgcccctgagtgtgaacagg
    tttgcctacgttggtgca
    58 206678_at GABRA1 tggtttattgccgtgtgctatgcctttgtgttctcagctctgattgagtttgccacagtaaa
    ctatttcactaagagaggttatgcatgggatggcaaaagtgtggttccagaaaagccaaaga
    aagtaaaggatcctcttattaagaaaaacaacacttacgctccaacagcaaccagctacacc
    cctaatttggccaggggcgacccgggcttagccaccattgctaaaagtgcaaccatagaacc
    taaagaggtcaagcccgaaacaaaaccaccagaacccaagaaaacctttaacagtgtcagca
    aaattgaccgactgtcaagaatagccttcccgctgctatttggaatctttaacttagtctac
    tgggctacgtatttaaacagagagcctcagctaaaagcccccacaccacatcaatagatctt
    ttactcacattctgttgttcagttcctctgcactgggaatttatttatgttctcaacgcagt
    aattccca
    59 206799_at SCGB1D2 tagaagtccaaatcactcattgffigtgaaagctgagctcacagcaaaacaagccaccatga
    agctgtcggtgtgtctcctgctggtcacgctggccctctgctgctaccaggccaatgccgag
    ttctgcccagctcttgtttctgagctgttagacttcttcttcattagtgaacctctgttcaa
    gttaagtcttgccaaatttgatgcccctccggaagctgttgcagccaagttaggagtgaaga
    gatgcacggatcagatgtcccttcagaaacgaagcctcattgcggaagtcctggtgaaaata
    ttgaagaaatgtagtgtgtgacatgtaaaaactttcatcctggtttccactgtctttcaatg
    acaccctgatctt
    60 206835_at STATH aagcttcacttcaacttcactacttctgtagtctcatcttgagtaaaagagaacccagccaa
    ctatgaagttccttgtctttgccttcatcttggctctcatggtttccatgattggagctgat
    tcatctgaagagaaatttttgcgtagaattggaagattcggttatgggtatggcccttatca
    gccagttccagaacaaccactatacccacaaccataccaaccacaataccaacaatatacct
    tttaatatcatcagtaactgcaggacatgattattgaggcttgattggcaaatacgacttct
    acatccatattctcatctttcataccatatcacactactaccactttttgaagaatcatcaa
    agagcaatgcaaatgaaaaacactataatttactgtatactctttgtttcaggatacttgcc
    ttttcaattgtcacttgatgatataattgcaatttaaactgttaagctgtgttcagtactgt
    ttc
    61 206940_s_at LOC100 ggtttgttaccatcctttaatcataactaaaacattgaaaacagaacaaatgagaaaagaaa
    131317 aaaaacctgccgattaacaatgacgaaaatcatgcatgatctgaaaggtgtggaaagaaaca
    /// caattaggtctcactctggttaggcattatttatttaattatgttgtatatcattgtttgca
    POU4F1 gggcaacattctatgcattgaactgagcactaactgggctagcttctggtagacgtttgtgg
    ctagtgcgattcacagtctactgcctgttccactgaaacattttgtcatattcttgtattca
    aagaaaaaaggaaaaaaagattattgtaaatattttatttaatgcacacattcacacagtgg
    taacagactgccagtgttcatcctgaaatgtctcacggattgatctacctgtccatgtatgt
    ctgctgagctttctccttggttatgttttt
    62 206984_s_at RIT2 taaagagctcatttttcaggtccgccacacctatgaaattcccctggtgctggtgggtaaca
    aaattgatctggaacagttccgccaggtttctacagaagaaggcttgagtcttgcccaagaa
    tataattgtggtttttttgagacctctgcagccctcagattctgtattgatgatgcttttca
    tggcttagtgagggaaattcgcaagaaggagtccatgccatccttgatggaaaagaaactga
    agagaaaagacagcctgtggaagaagctcaaaggttctttgaagaagaagagagaaaatatg
    acatgatatctttgcttttgagttcctcacgctctctgaattttattagttggacaattcca
    tatgtagcattctgcttcaatattatctctctatgtgtctctctctctttaaatatctgcct
    gtaggtaaaagcaagctctgcatatctgtacctcttgagatagttttgttttgcctttaaca
    gttggatgga
    63 207003_at GUCA2A gaggggtcaccgtgcaggatggaaatttctccttttctctggagtcagtgaagaagctcaaa
    gacctccaggagccccaggagcccagggttgggaaactcaggaactttgcacccatccctgg
    tgaacctgtggttcccatcctctgtagcaacccgaactttccagaagaactcaagcctctct
    gcaaggagcccaatgcccaggagatacttcagaggctggaggaaatcgctgaggacccgggc
    acatgtgaaatctgtgcctacgctgcctgtaccggatgctaggggggcttgcccactgcctg
    cctcccctccgcagcagggaagctcttttctcctgcagaaagggccacccatgatactccac
    tcccagcagctcaacctaccctggtccagtcgggaggagcagcccggggaggaactgggtga
    ct
    64 207028_at LOC100 ctccccccgagagaaggctgcaaagctgggaagcccagggtgtgctcctcccgcccttttgg
    129296 acccccgggcttgcaccggctgcactctgagaaccagctgcgcgcggagcggtgcaatgcag
    /// cacccaccctgcgagcctggcaattgcttgtcattaaaagaaaaaaaaattacggagggctc
    MYCNO cgggggtgtgtgttggggaggggagaccgatgcttctaacccagcccccgctttgactgcgt
    S gttgtgcagctgagcgcgaggccaacgttgagcaaggccttgcagggaggttgctcctgtgt
    aattacgaaagaaggctagtccgaaggtgcaaaatagcagggagaggacgcgcccccttagg
    aacaagacctctggatgtttccagtttcaaattgaaagaagaggggcgccccccttg
    65 207208_at RBMXL2 acagcagcagttatggccggagcgaccgctactcgaggggccgacaccgggtgggcagacca
    gatcgtgggctctctctgtccatggaaaggggctgccctccccagcgtgattcttacagccg
    gtcaggctgcagggtgcccaggggcggaggccgtctaggaggccgcttggagagaggaggag
    gccggagcagatactaagcaggaacagacttgggaccaaaaatcccttttcaacgaaactaa
    caaaaagaagaacctgttgtatggtaactacccaaggactagtacaaggaagagttgttttt
    accttttaagaatttcctgttaagatcgtctccatttttatgcttttgggagaaaaaactta
    aaattcgtttagtttagttttggaattgttaacgtttctttcaacaagctcctgttaaaagt
    atatgaacctgagtactagtcttcttacatttacaagtagaaattcgattaatggcttcttc
    ccttgtaaattttcttg
    66 207219_at ZNF643 cagccagagcattggactgatccagcatttgagaactcatgttagagagaaaccttttacat
    gcaaagactgtggaaaagcgtttttccagattagacaccttaggcaacatgagattattcat
    actggtgtgaaaccctatatttgtaatgtatgtagtaaaaccttcagccatagtacatacct
    aactcaacaccagagaactcatactggagaaagaccatataaatgtaaggaatgtgggaaag
    cctttagccagagaatacatctttctatccatcagagagtccatactggagtaaaaccttat
    gaatgcagtcattgtgggaaagcctttaggcatgattcatcctttgctaaacatcagagaat
    tcatactggagaaaaaccttatgattgtaatgagtgtggaaaagccttcagctgtagttcat
    cccttattagacactgcaaaacacatttaagaaataccttcagcaatgttgtgtgaaatata
    ctaaacatcaaagaatctatgttggagcacaagattctaaatcagtggttccctg
    67 207529_at DEFA5 gagtcactccaggaaagagctgatgaggctacaacccagaagcagtctggggaagacaacca
    ggaccttgctatctcctttgcaggaaatggactctctgdatagaacctcaggttctcaggca
    agagccacctgctattgccgaaccggccgttgtgctacccgtgagtccctctccggggtgtg
    tgaaatcagtggccgcctctacagactctgctgtcgctgagcttcctagatagaaaccaaag
    cagtgcaagattcagttcaaggtcctgaaaaaagaaaaacattttactctgtgtaccttgtg
    tctt
    68 207597_at ADAM1 gtgacgctcaatctacagtttattcatatattcaagaccatgtatgtgtatctatagccact
    8 ggttcctccatgagatcagatggaacagacaatgcctatgtggctgatggcaccatgtgtgg
    tccagaaatgtactgtgtaaataaaacctgcagaaaagttcatttaatgggatataactgta
    atgccaccacaaaatgcaaagggaaagggatatgtaataattttggtaattgtcaatgcttc
    cctggacatagacctccagattgtaaattccagtttggttccccagggggtagtattgatga
    tggaaattttcagaaatctggtgacttttatactgaaaaaggctacaatacacactggaaca
    actggtttattctgagtttctgcatttttctgccgtttttcatagttttcaccactgtgatc
    tttaaaagaaatgaaataagtaaatcatgtaacagagagaatgcagagtataatcgtaattc
    atccgttgtatcag
    69 207814_at DEFA6 gagccactccaagctgaggatgatccactgcaggcaaaagcttatgaggctgatgcccagga
    gcagcgtggggcaaatgaccaggactttgccgtctcctttgcagaggatgcaagctcaagtc
    ttagagctttgggctcaacaagggctttcacttgccattgcagaaggtcctgttattcaaca
    gaatattcctatgggacctgcactgtcatgggtattaaccacagattctgctgcctctgagg
    gatgagaacagagagaaatatattcataatttactttatgacctagaaggaaactgtcgtgt
    gtcccatacattgccatcaactttgtttcctcat
    70 207843_x_at CYB5A gctggaggtgacgctactgagaactttgaggatgtcgggcactctacagatgccagggaaat
    gtccaaaacattcatcattggggagctccatccagatgacagaccaaagttaaacaagcctc
    cagaaccttaaaggcggtgtttcaaggaaactcttatcactactattgattctagttccagt
    tggtggaccaactgggtgatccctgccatctctgcagtggccgtcgccttgatgtatcgcct
    atacatggcagaggactgaacacctcctcagaagtcagcgcaggaagagcctgctttggaca
    cgggagaaaagaagccattgctaactacttcaactgacagaaaccttcacttgaaaacaatg
    attttaatatatctctttctttttcttccgacattagaaacaaaacaaaaagaactgtcctt
    tctgcgctcaaattMcgagtgtgcctttttattcatctacttt
    71 207878_at KRT76 gagctcaagccagcatagctccaccaagtgatctactgttccaaatctctataaccacctgc
    ttcccactcagcctgcaatagtgtttcccactctctgcttggcatcaatagatgcataaggg
    tcaaccacatttttcctcaagttccctggagaagaagctgaactcctggtttctccatcccc
    atgaccttcccagggccatggaggtcctgctgctggtctgggatgatgatgcccctggaaac
    cttcctgcaatggccccttactttggacagcaacccctgagcccaagccagttttggccttc
    acagcctggccggttcccactctggcccatctcccattcttactgggagttggagatttgaa
    gccagtcatctcagcactgtctgaggagggcagagccatgggttctgtgctggagggtgcac
    ggccaagatctccagactgctggttcccagggaaccctccctacatctgggcttcagatcct
    gactcccttctgtcccctaattccctgagctgtagatcctctggt
    72 207937_x_at FGFR1 cgcacccgcatcacaggggaggaggtggaggtgcaggactccgtgcccgcagactccggcct
    ctatgcttgcgtaaccagcagcccctcgggcagtgacaccacctacttctccgtcaatgttt
    cagcttgcccagatctccaggaggctaagtggtgctcggccagcttccactccatcactccc
    ttgccatttggacttggtactcggcttagtgattagaggccctgaacaggtggtggtatccc
    tgctctgctggagaggaacccagatgctctcccctcctcggaggatgatgatgatgatgatg
    actcctcttcagaggagaaagaaacagataacaccaaaccaaaccccgtagctccatattgg
    acatccccagaaaagatggaaaagaaattgcatgcagtgccggctgccaagacagtgaagtt
    caaatgcccttccagtgggaccccaaaccccacactgcgctggttgaaaaatggcaaagaat
    tcaaacctgaccacagaattggaggctacaaggtccgttatgccacctgga
    73 208157_at SIM2 ctgccctgtacatgctagttcaacagaaaggaatggcctttcaccttctcctggtggcaggc
    aagcagatgtcctctgcggagataccgccagctccccaggacgcagactgactcctgtttgc
    tcgctggaccaaccccaggcagaaggtggaaggtgggaacagaggtttagctgcaggacatg
    tattcccattgcaccgagacctaactgccgctcagagtgtagaccgagatggtgcagatgcc
    tgcagtgccattaaaatgtgggtgaaggtgacatcaggattatgtgccccaggccgggctca
    gtggctcacacctgtaatcccagcactttgggaggccaaggtgggcggatcacctgaggtca
    ggagtttgcgacaagcctgccaacaagctgaaacc
    74 208233_at PDPN gaaatctctgatataagctgggtgtggtggctcgtgcctgtagtctcagctgctgggcaact
    gcagaccagcctgggcaacatagtaagaccctgtctcaaaaaaataatctctggtacaatgg
    tcatgttccaaagttccttacttgggcctcttgagtgcagtggctcacacctggaatcccag
    tgctttgagaggctgaggaggcaggaggttcacttgtgcccaggaatttgaggctgcagtga
    gctatgattgtgccactgcactccagcctgggtgacagagcaagactgtgctctcttaaaaa
    taagaaagagcctcttcatcttcaaaaggactacatctgaagtttccccagaaggacaaatg
    tctacttagaccttataaatttccaaaataagagagtcagagccagaggtggcttgtaagtt
    gacttctgttgagatctgaccacatttgatctcttgttttaattttccaactaactgaactt
    ggaagaaaacccaaaccaagttttaatctgatgccta
    75 208292_at BMP10 ccatgagcaacttccagagctggacaacttgggcctggatagcttttccagtggacctgggg
    aagaggctttgttgcagatgagatcaaacatcatctatgactccactgcccgaatcagaagg
    aacgccaaaggaaactactgtaagaggaccccgctctacatcgacttcaaggagattgggtg
    ggactcctggatcatcgctccgcctggatacgaagcctatgaatgccgtggtgtttgtaact
    accccctggcagagcatctcacacccacaaagcatgcaattatccaggccttggtccacctc
    aagaattcccagaaagcttccaaagcctgctgtgtgcccacaaagctagagcccatctccat
    cctctatttagacaaaggcgtcgtcacctacaagtttaaatacgaaggcatggccgtctccg
    aatgtggctgtagatagaagaagagtcctatggcttatttaataactgtaaatgtgtatatt
    tggtgttcctatttaatgagattatttaataagggtgtacagtaatagaggcttgctgcctt
    caggaa
    76 208314_at RRH atgatctgcatgtttctggtggcatggtccccttattccatcgtgtgcttatgggcttcttt
    tggtgacccaaagaagattcctccccccatggccatcatagctccactgtttgcaaaatctt
    ctacattctataacccctgcatttatgtggttgctaataaaaagtttcggagggcaatgctt
    gccatgttcaaatgtcagactcaccaaacaatgcctgtgacaagtattttacccatggatgt
    atctcaaaacccattggcttctggaagaatctgaaataagagaaaaggacacgctatcaaaa
    cactttagttttttgacaatgcttttcttttaaatatgagcccatttagatcaagtgcagac
    atggatcattgtcctatgagagtgtaagctcctcaagcacagctcgtgcttccgtttgtgca
    ctctggctgctgtagtgtatgcttctctgtgtcctgatatatcaacttattgctcatctcct
    ttgatgaattaggcatcagaggttaaggtcccctttc
    77 208368_s_at BRCA2 gaacaggagagttcccaggccagtacggaagaatgtgagaaaaataagcaggacacaattac
    aactaaaaaatatatctaagcatttgcaaaggcgacaataaattattgacgcttaacctttc
    cagtttataagactggaatataatttcaaaccacacattagtacttatgttgcacaatgaga
    aaagaaattagtttcaaatttacctcagcgtttgtgtatcgggcaaaaatcgttttgcccga
    ttccgtattggtatacttttgcttcagttgcatatcttaaaactaaatgtaatttattaact
    aatcaagaaaaacatctttggctgagctcggtggctcatgcctgtaatcccaacactttgag
    aagctgaggtgggaggagtgcttgaggccaggagttcaagaccagcctgggcaacataggga
    gacccccatctttacgaagaaaaaaaaaaaggggaaaagaaaatcttttaaatctttggatt
    tgatcactacaagt
    78 208399_s_at EDN3 ccgagccgagcttactgtgagtgtggagatgttatcccaccatgtaaagtcgcctgcgcagg
    ggagggctgcccatctccccaacccagtcacagagagataggaaacggcatttgagtgggtg
    tccagggccccgtagagagacatttaagatggtgtatgacagagcattggccttgaccaaat
    gttaaatcctctgtgtgtatttcataagttattacaggtataaaagtgatgacctatcatga
    ggaaatgaaagtggctgatttgctggtaggattttgtacagtttagagaagcgattatttat
    tgtgaaactgttctccactccaactcctttatgtggatctgttcaaagtagtcactgtatat
    acgtatagagaggtagataggtaggtagattttaaattgcattctgaatacaaactcatact
    ccttagagcttgaattacatttttaaaatgcatatgtgctgtttggcaccgtggcaagatgg
    tatcagagagaaacccatcaattgctcaaatactc
    79 208511_at PTTG3 ttgtggctacaaaggatgggctgaagctggggtctggaccttcaatcaaagccttagatggg
    agatctcaagtttcaatatcatgttttggcaaaacattcgatgctcccacatccttacctaa
    agctaccagaaaggctttgggaactgtcaacagagctacagaaaagtcagtaaagaccaatg
    gacccctcaaacaaaaacagccaagcttttctgccaaaaagatgactgagaagactgttaaa
    gcaaaaaactctgttcctgcctcagatgatggctatccagaaatagaaaaattatttccctt
    caatcctctaggcttcgagagttttgacctgcctgaagagcaccagattgcacatctcccct
    tgagtgaagtgcctctcatgatacttgatgaggagagagagcttgaaaagctgtttcagctg
    ggccccccttcacctttgaagatgccctctccaccatggaaatccaatctgttgcagtctcc
    tttaagcattctgttgaccctggatg
    80 208684_at COPA ggtttaaggatcagtcctctgcagtttcgctaaggccccctttgtgtgcatgggtcagtcac
    catatgttccccccagagaatgtgtctatatcctccttctaacagcaccttccccctgcagc
    tactcttcagatctggctctctgtaccctaaaacctagtatctttttctcttctatggaaaa
    tccgaaggtctaaacttgacttttttgaggtcttctcaacttgactacagttgtgctcataa
    ttgtccttgcctttccagcttaattattttaaggaacaaatgaaaactctgggctgggtgga
    gtggctcatacctgtaatcccagcactttgggaggctacggtgggcagatcatctgaggcca
    ggagttcgagacctgcctggccaacatggcaacaccccgtctctaataaaaatataaaaatt
    agcctggcatggtagcatgcgcctatagtcccagctgctcaggaggctgaggcatgagaatc
    gcttgaacctaggaggtggaggttgcattcaactgagatcatacc
    81 208992_s_at STAT3 actggtctatctctatcctgacattcccaaggaggaggcattcggaaagtattgtcggccag
    agagccaggagcatcctgaagctgacccaggcgctgccccatacctgaagaccaagtttatc
    tgtgtgacaccaacgacctgcagcaataccattgacctgccgatgtccccccgcactttaga
    ttcattgatgcagtttggaaataatggtgaaggtgctgaaccctcagcaggagggcagtttg
    agtccctcacctttgacatggagttgacctcggagtgcgctacctcccccatgtgaggagct
    gagaacggaagctgcagaaagatacgactgaggcgcctacctgcattctgccacccctcaca
    cagccaaaccccagatcatctgaaactactaactttgtggttccagattttttttaatctcc
    tacttctgctatctttgagc
    82 209434_s_at PPAT ttgacagctctttaagcccacatgcagcagtgggtcagataaccctgtggcagtgacacggg
    caaattggcatttgaataaagccctgggaccacctcaacatgcgtagcctcttgtcttaaat
    gtactccccatggcagcatggaggaggcaagacctgtgggtcaattttgaactggccttact
    ttgatttttaaaacaagagactcagggaaagtactaaaccaaaatctctgattttactttgc
    gttttctgtagtttttgttttactgagatgcttttgtaaaggaaaataatactgtgacagtt
    tagtaattctacagattcttaatatttctccatcatggccttttacttcacaattttctgaa
    gtctgaattcaattacaattttttttttttaccaatttaatctcaaatgttgtttaactgct
    ttaaattcatatacgtagagtattataaactgcagagatgaaaaatgtgttttcacgggatt
    tatattgtgaactaaactaagcctactttttgtgact
    83 209839_at DNM3 gagacttctcacttctggttggaggtttcacatatggctcaactcaagtcattaatctcttt
    ttaatttttactcttgaattccttaaacttcgctcattatgaaatgttttaaaattatgaca
    aaaattactctgtctaaccacttgccttgtctgctaccagtttgttaaaaattattcccccc
    aaccagtaattccaccagtactacttgatttgtgttatatttcctatgtacatgtacagcct
    ttgttttgcttgcttgtctatttttactttcccttttttgggtcaaatttttcttttgcttt
    gtttgaagaaggaatatacagaagtaaaatcttgtcttctctgctgattctttaattaatat
    gagccggatactttccactgtcttcttggcactttcaggatttcttaatgctgatatatgga
    ctcttagaatggaatttttgaagaaaaatctcaaagcctgtatcgttct
    84 209859_at TRIM9 ataggttacccttgaaattcattagtttgtcataaagttttaggaaaggtaggacccggaaa
    gaagttctaattagttgtctaaatatttttcagtgagccaagaaattcaccatgaaaaaaca
    agaataacaaatagaagggaagagataggatgggaaagctaacaaattaaagttttggcaaa
    aaggaatatatgtaaatagctaattatttacttttgtgcttactttatttagattatttcta
    tcagttacaatctttttctagttaagtgtacctaatttatggaatgggtgctatcctgttta
    tgtgtgtcttggtttttcttggctacagaaaaactgttgcagggcaacactagtttgatatt
    tgatttactctccaatgagactcaatggctgggccgtggtagactcatagttcctcttgttc
    tttattaaattcatcctgctaattagatttctagtgacttgtaacatgtagtttacactgaa
    ttgcaattacagatgcatacaactactatacta
    85 210016_at LOC100 ataacagcatatgcatttccccaccgcgttgtgtctgcagcttctttgccaatatagtaatg
    134306 cttttagtagagtactagatagtatcagttttggattcttattgttatcacctatgtacaat
    /// ggaaagggattttaagcacaaacctgctgctcatctaacgttggtacataatctcaaatcaa
    MYT1L aagttatctgtgactattatatagggatcacaaaagtgtcacatattagaatgctgaccttt
    catatggattattgtgagtcatcagagtttattataacttattgttcatattcatttctaag
    ttaatttaagtaatcatttattaagacagaattttgtataaactatttattgtgctctctgt
    ggaactgaagtttgatttatttttgtactacacggcatgggtttgttgacactttaattttg
    ctataaatgtgtggaatcacaagttgctgtgatacttcatttttaaattgtgaactttgtac
    aaattttgtcatgctggatgttaacacat
    86 210247_at SYN2 tcatgtcttattcttccctgtgaaaccaggattaatcgtggactcctggcagcttaacctag
    ctcagttgcagtgctaagcatgccccgcccccattcagtgatacctgtttgggaagtatata
    cttccccaaaagtactcttggccctaagttttaggaactttccccgacctggatcccttgtc
    atacctgtgttactgtttaaagcacacccacccaacttacaagatcttaggctgctgtggtg
    gtgaagcaccttgagtctgctgatattcgggagaacaaggatctgcagtttccccttttctc
    ccctctgaagagtggttcttatgtgcaatctgcagtaaccttgaactccagagctgcactat
    agaggagaatgcatgccactatgacagcagtatgccaagctttgtgttcatctcctaata
    87 210302_s_at MAB21L atttcgttttgcttttggttgcctgaatgttgtcaccaagtgaaaaaattatttaactatat
    2 gtaaaatttctcttttaaaaaaaagttttactgatgttaaacgttctcagtgccaatgtcag
    actgtgctcctccctctcctgaacctctaccctcaccctgagctgtcttgttgaaaacagt
    88 210315_at SYN2 tattctcgactgtaatggcattgcagtagggccaaaacaagtccaagcttcttaaaatgatt
    ggtggttaatttttcaaagcagaaattttaagccaaaaacaaacgaaaggaaagcggggagg
    ggaaaacagaccctcccactggtgccgttgctgcgttctttcaatgctgactggactgtgtt
    tttcctatgcagtgtcagctcctctgtctggttgtttacctgttcctgttcgtgcttgtaat
    gctcacttatgttttctctgtataacttgtgattccagggctgtttgtcaacagtatacaaa
    agaattgtgcctctcccaagtccagtgtgactttatcttctgggtggtttg
    89 210455_at C10orf2 gaaatcagcgaggctcaagttccaagcaaaccattccaaaatgtggaattctgtgacttcag
    8 taggcatgaacctgatggggaagcatttgaagacaaagatttggaaggcagaattgaaactg
    ataccaaggttttggagatactatatgagtttcctagagtttttagttctgtcatgaaacct
    gagaatatgattgtaccaataaaactaagctctgattctgaaattgtacaacaaagcatgca
    aacatcagatggaatattgaatcccagcagcggaggcatcaccactacttctgttcctggaa
    gtccagatggtgtctttgatcaaacttgcgtagattttgaagttgagagtgtaggtggtata
    gccaatagtacaggtttcatcttagatcaaaagatacagattccattcctgcaactatgggt
    cacatctctctgtcagagagcacaaatgacactgttagtccagtaatgattagagaatgtga
    gaagaatgacagcactgctgatgagttacatgtaaagcacgaacctcctgatacag
    90 210758_at PSIP1 gggctcaaagcattaatccagttactgaaaagagaatacaagtggagcaaacaagagatgaa
    gatcttgatacagactcattggactgaatttcccccttccccccatgatggaagaatgttca
    gattctaaattgaggacttcattattaatggcattactgtgttatgattaacaaatttcttg
    taaggtacacactacatactaaggtcggccatcattccgttttttttttttttttttttttt
    aaccaagcttaaaatgaagcttaaaatgaagctttgtgtttgaaagtaataacaagctcaga
    cgaagatggtggttgtacattattcatctagaaaatataaaaattcattttgttttgaagct
    agttattaaactggaatagcagttatatccctgagaatggggccctt
    91 210918_at — gctgctgttttcttctaactgcagggaaaatgctgtctaaaagaaaataataaatttgtatc
    tgctgagttctcttagcataaggcaccaacaaaacaaccttcaggaagggagaagaaaccat
    cctcccactcatccttcagaggatttagataaagtgaagggaagaatcgttctccagctcct
    tcggaatttacgccggcatcagggcaggcttgttactgctggatccattgtctgctcaaggt
    tacttattccactaagacgtacatcctaccacggaccacggctttgtagctagccaggctct
    gagtgtgtgtgtagatgaaccatttctctctccagtaaatgaatgacagtctttctagggct
    cttgtcttctgctgggaggcag
    92 211204_at ME1 agtcactctcccagatggacggactctgtttcctggccaaggcaacaattcctacgtgttcc
    ctggagttgctcttggggtggtggcctgcggactgagacacatcgatgataaggtcttcctc
    accactgctgaggtcatatctcagcaagtgtcagataaacacctgcaagaaggccggctcta
    tcctcctttgaataccattcgagacgtttcgttgaaaattgcagtaaagattgtgcaagatg
    catacaaagaaaagatggccactgtttatcctgaaccccaaaacaaagaagaatttgtctcc
    tcccagatgtacagcactaattatgaccagatcctacctgattgttatccgtggcctgcaga
    agtccagaaaatacagaccaaagtcaaccagtaacgcaacagcta
    93 211264_at GAD2 gttccacttctctaggtagacaattaagttgtcacaaactgtgtgaatgtatttgtagtttg
    ttccaaagtaaatctatttctatattgtggtgtcaaagtagagtttaaaaattaaacaaaaa
    agacattgctccttttaaaagtcctttcttaagtttagaatacctctctaagaattcgtgac
    aaaaggctatgttctaatcaataaggaaaagcttaaaattgttataaatacttcccttactt
    ttaatatagtgtgcaaagcaaactttattttcacttcagactagtaggactgaatagtgcca
    aattgcccctgaatcataaaaggttctttggggtgcagtaaaaaggacaaagtaaatataaa
    atatatgttgacaataaaaactcttgcctttttcatagtattagaaaaaaatttctaattta
    cctatagcaacatttcaaat
    94 211341_at LOC100 gcatttgaaactgagcactaaactgggctagctttctggtagaccgttttgtggctagtgcg
    131317 atttcacagtctactgcctgtttccactgaaaacatttttgtcatattcttgtattcaaaga
    /// aaacaggaaaaaagttattgtaaatattttatttaatgcacacattcacacagtggtaacag
    POU4F1 actgccagtgttcatcctgaaatgtctcacggattgatctacctgtctatgtatgtctgctg
    agctttctccttggttatgttttttctcttttacctttctcctcccttacttctatcagaac
    caattctatgcgccaaatacaacagggggatgtgtcccagtacacttacaaaataaaacata
    actgaaagaagagcagttttatgatttgggtgcgtttttgtgtttatactgggccaggtcct
    g
    95 211516_at IL5RA ggcagccttccttgtgatcaaaaaaggtaatcccagaaacgtacccgttcactcgtgggtct
    taaaatggtttcatatctctattgtgactaattttctctcggtctactgccttttcaatcag
    gaatagatttgccatgaagccagtgaagtttttaagtgtctaggcttctcattagtgccaac
    tctcctagacctggtgcctgttttttttccaagttttgtttctacttctatccattttttaa
    attaaactttttattttgaaataattatcacactcacaagctgtgggaagaaataatagaga
    tcctgtgtctctttcatccagttttcctcaagggtaacatct
    96 211772_x_at CHRNA3 tgctcaacgtgcactacagaaccccgacgacacacacaatgccctcatgggtgaagactgta
    ttcttgaacctgctccccagggtcatgttcatgaccaggccaacaagcaacgagggcaacgc
    tcagaagccgaggcccctctacggtgccgagctctcaaatctgaattgcttcagccgcgcag
    agtccaaaggctgcaaggagggctacccctgccaggacgggatgtgtggttactgccaccac
    cgcaggataaaaatctccaatttcagtgctaacctcacgagaagctctagttctgaatctgt
    tgatgctgtgctgtccctctctgctttgtcaccagaaatcaaagaagccatccaaagtgtca
    agtatattgctgaaaatatgaaagcacaaaatgaagccaaagaggaacaaaaagcccaagag
    atccaacaattgaaacgaaaagaaaagtccacagaaacatccgatcaagaacctgggctatg
    aatttccaatcttcaacaacctgtt
    97 212359_s_at KIAA091 cagcgctgccagcaggcatacatgcagtacatccaccaccgcttgattcacctgactcctgc
    3 ggactacgacgactttgtgaatgcgatccggagtgcccgcagcgccttctgcctgacgccca
    tgggcatgatgcagttcaacgacatcctacagaacctcaagcgcagcaaacagaccaaggag
    ctgtggcagcgggtctcactcgagatggccaccttctccccctgagtctttcacccttaggg
    tcctatacagggacccaggcctgtggctatgggggcccctcacacagggggagtgaaacttg
    gctggacagatcatcctcactcagttccctggtagcacagactgacagctgctcttgggcta
    tagcttggggccaagatgtctcacaccctagaagcctagggctgggggagacagccctgtct
    gggagggggcgttgggtggcctctggtatttattt
    98 212528_at — gtcactcatttccttgaacagcacccccctttatactagcagccatttgtgccattgcctgt
    gccctagggtttgtggggagagagcgagggatcactgagcagttttcccagagctccatggg
    aaggcaagctctccctcccaatgggagccccactgtcactaactgtaaactcaggctcaggc
    ttcaactgcctacccccatcctcatatttctgtctgtcccagcacctcaggagcattctcat
    tgtggccggctaactccgcctggatgtgaacaggcaagcacagtgggaaatgagtcacgtac
    ttgtattgcacagtggacacctctagaggtccattggtttaaagggatagggaaggaggagg
    gatgagaccatcaccccctcccagaagtaaatctagtatctgagttttctttat
    99 212531_at LCN2 caagagctacaatgtcacctccgtcctgtttaggaaaaagaagtgtgactactggatcagga
    cttttgttccaggttgccagcccggcgagttcacgctgggcaacattaagagttaccctgga
    ttaacgagttacctcgtccgagtggtgagcaccaactacaaccagcatgctatggtgttctt
    caagaaagtttctcaaaacagggagtacttcaagatcaccctctacgggagaaccaaggagc
    tgacttcggaactaaaggagaacttcatccgcttctccaaatctctgggcctccctgaaaac
    cacatcgtcttccctgtcccaatcgaccagtgtatcgacggctgagtgcacaggtgccgcca
    gntgccgcaccagcccgaacaccattgaggga
    100 213197_at ASTN1 tttccccttggaagacactattgatctcaacctgctgacttttcctaatgcttacctgaagg
    aacccatcctggctagaaagggtgatggtactggaccggtattcaaccttgagttttcaagc
    tgccaaacaggtcttaagggaggtgcttatatcccaccaacactctcccagctcccatgtcc
    ccaagacctctggagtttcctcttgaatgtacatgaaccactgtaatagcattagactttta
    attgagtgtgcaatcgttttccatggagtttggtccgttcattattttttagttaactacac
    ttcttgatattcaaatgttctattaaaaaaactgagtatgaagaaaaacactttactactgc
    agaa
    101 213260_at FOXC1 tcccccatttacaatccttcatgtattacatagaaggattgcttttttaaaaatatactgcg
    ggttggaaagggatatttaatctttgngaaactattttagaaaatatgtttgtagaacaatt
    atttttgaaaaagatttaaagcaataacaagaaggaaggcgagaggagcagaacattttggt
    ctagggtggtttctttttaaaccattttttcttgttaatttacagttaaacctaggggacaa
    tccggattggccctcccccttttgtaaataacccaggaaatgtaataaattcattatcttag
    ggtgatctgccctguaatcagactttggggagatggcgatttgattacagacgttcgggggg
    gtggggggcttgcagtttgttttggagataatacagtttcctgctatctgccgctcctatct
    agaggcaacacttaagcagtaattgctgttgcttgttgtca
    102 213458_at FAM149 agcctgaaacaggaactcacatgagactcagggccaccaggaaatgcttaaaatacatactc
    tttcccaaaagcaaatctataattctgtttcaattttatgaatatatgaatagacaaaatga
    atcgaattacataactatgtcattcattaaatggcaacaatgctgacagcaagcagtagatc
    ctctgattccaattaccatttgttttttacccaattctatttgctagaggtagtaagtactc
    tggcactcataaatcacatgatgataaaaaggaacatgaggccgggtatggtggctcacaac
    tgtaatccccataccttggg
    103 213482_at DOCK3 tatgggtcagttacagcagccctcacctcaaagggctggcctgcttctcagcctacattcat
    ttgcaagcttcaatctctggaccatctggtgttcacaggtgttagagggttaggggttaggg
    gctagttttggatttgattcataggtaggagggcttagattttaaggcacttctgaaagtca
    atccctggacaaggcagtcatcacataagaacagctaccttctccacttggtggcacaagag
    gtagggaggggagtatgggttcatttgncttcgcattatgcaaggtgaaaccgtttgttttc
    cctctccattttccctaactaaatgaaaaggacacattctgaaatcccttttgttggagaat
    aagtcagtctgaggggaaatgggaggccagagatgagaaccctttgaaaagattgtaaaata
    ctgattttcattctttcaagcttatttgtaaatacctatttgaatgctgtgtatttgtacag
    gaatttgagcaaaaaatgtatagagtgtgatgtccaattggtattcagcactat
    104 213603_s_at RAC2 gagcttcgttgatggtcttttctgtactggaggcctcctgaggcnnnnnnagccccaggacc
    cattaagccacccccgtgttcctgccgtcagtgccaactnnnnnatgtggaagcatctaccc
    gttcactccagtcccaccccacgcctgactcccctctggaaactgcaggccagatggttgct
    gccacaacttgtgtaccttcagggatggggctcttactccctcctgaggccagctgctctaa
    tatcgatggtcctgcttgccagagagttcctctacccagcaaaaatgagtgtctcagaagtg
    tgctcctctggcctcagttctcctcttttggaacaacataaaacaaatttaattttctacgc
    ctctggggatatctgctcagccaatggaaaatctgggttcaaccagcccctgccatttctta
    agactttctgctccactcacaggatcctgagctgcacttacctgtgagagtcttcaaacttt
    taaaccttgccagtcaggacttttgctattgcaaatagaaaacccaactcaacctgctt
    105 213917_at PAX8 ctgcctggttaccgtggcgatgtgcttaatgcagcgttgaaaatacagaatactgactcctc
    tgtccctcctggccccggactccctccctccctcccttcctcttctggagcgtgaaatgaga
    ttggtcaagataaaaaaggaaaagattcggttatttttttaagagtgtggataatggggcct
    ctcaatcaaaatcccagtctccagtcggttccccuattuccttccaacccctccaccttccc
    ctgccgcctgcttagaggaggaggaagaaacataaagcacaaggcttttctcttaattatga
    atcattccctgagggcaggcccagggcaaggggttcctggggcccagagtctgacctgtgag
    gtagctagaaggcttgagcctctcatcaaagtcc
    106 214457_at HOXA2 ctttgcaggactttagcgttttctccacagattcctgcctgcagctttcagatgcagtttca
    cccagtttgccaggttccctcgacagtcccgtagatatttcagctgacagcttagacttttt
    tacagacacactcaccacaatcgacttgcagcatctgaattactaaaaacattaaagcaaaa
    caaagcatcaccaaacaaaaactcctttgaccaggtggttttgccttcttttatttgggagt
    ttattttttattttcttcttgacctaccccttccctcctttaagtgttgaggattttctgtt
    tagtgattccctgacccagtttcaaacagagccatcttttacagattattttggagttttag
    ttgttttaaacctaactcaacaaccctttatgtgattcctgagagc
    107 214608_s_at EYA1 gtcaccctgaggaaggttcattgccattgtcatcaccatggaaacaacgttcctctccacct
    gcattatgtactacatgacaggcatcaatctggggaaataataaaattatcacctttgtcag
    accataagagtttctccaaaagtggtcagtttggctgggcaatatttnctctcatctaacaa
    acacaatccattgtcatgaaattacccttaggatgagtcttctttaatcaatcatatattgg
    gcggaaaaaacaccagctttgacccgaagtagttgaagagctacttcattcttttctgaagt
    tgtgtgttgctgctagaaatagtcatttgtgaattatccaaattgtttaaattcacaattga
    attagttttttcttcctttttgcttgaagcaaacagttgacaatttttaaccttttcatttt
    atgtttttgtactctgcagactgaaaagacaaagtttatcttggccttactgtataaaggtg
    tgctgtgtccaccgttgtgtacaga
    108 214665_s_at CHP gaggtctggcactagtagcacaacctaaggtggcattacagatctttgagcgagccacagca
    acttttctgccaagtcagcttnagttnagacttcagtgaatcaggntattgctatcctaatg
    tatgtctctatgagtgtatntagccacanantctgcccttggttgantttctgactcattgc
    ttgcttgcttgtttccttgctttggaaaactatnnaagattgctaaaaaataccactgcaaa
    gtgatggaaaagggtggagaacaggggagtagccaggctggatggctcaaatataaatgaat
    gaggaattctttatgaagtatcagtcagattttatgattaagtgatgtaatataggaattat
    gtaaaagggaagaatgtctgatactgatctattagagaggtactttagaggcttcttgattg
    gcataaagttcctaaggttatagattttccccccttttggctgtatagcaaagtgttttaat
    ccacggttgtgccttattgttccattaaaa
    109 214822_at FAM5B caatgggaggggtcggagctcttccttcccctctgtggagtcacttttgtattctttttaac
    cagatttcttaaaatgttgttgttttgtgaatcctgacattggttcttacttttgtatgctg
    cctcctctgtgccctcccagacgctgactgggaaacacaagaagtacaaccaacaggaacca
    gcgccaagggcaggcagcggcctccttgctcccctcccttactcctccctctgctgcctcct
    ccccccaccaagtttcagggccctggattgttcccagttcccattgtggtcccttcagagct
    cctttccaacagcatctctctgtcgaagaaagaagctctgtcaagttagagagagacaatgt
    gtaggaaatgttcttttttaaaaaaaaataacaaaaacaaaacaaaactatnnannntgtga
    ttgttttccttgttaatctgctccaaccacctgaacatctaagta
    110 215102_at DPY19L gagacgggagtttaccccgatcacagaaaccataccaactgaaagacaaatcagcatcttgc
    1P1 tggacgacccctcacagagctcctagatccttgaagtgtgaacttcagcagctgagagagat
    ggggtctcactatgttgcccaggctggtcttgaactcctggactcaagcaatcctctcacct
    cagcctcccaaagtgctgggattacagattttataaatattgttgatctttttgaaaaacca
    actgttggcttcattttntttattgtgtaatactaccttagaggacagcagttcctaatacc
    tacttttattatgagtctctgccatttataaagaactgtggacagcacagggaatgggggaa
    gaaaactctggtgcagcttgaatcttggtagcaaaacagtgacttcatcagaaaattttgtc
    actctctattagatataatggagtttgaccatttggaatttggaatttttcaaatgaatatg
    acaaaaatttaaaaaactcttgtattactatgtgataacacagatctttacaacttta
    111 215180_at — aagccttcaccagatggtcaagcagatgctggtgccatgcccttgancntcncnccaccatc
    ccccacctagccactatatgggttgttagatattttgaccacctcctcttcnctcactccac
    tattcaactcactgcatcatcaatgtacttattacaaacctgtcacaagccaggtcttatgc
    taggtgctcctctcaacaggttcttgagctggcaggggagagagagacattcaaacaccaag
    gattaatataccattacaggtttaaagacagaggcctataagggtcccctggcagtgccatg
    gaggtagggcatggtcggctgtacctgtagaggtgtctaaagggaggcttgcaagctgcccc
    ttgaaggacgagcagaaaattgtacatgaggacaagtaggaaaggaattccaggaggaggga
    tcagcatgtgca
    112 215289_at HLA-DRB1 ggactaaatcgagccttattatacatcagcagtctcacactggagaaagtccttttaagtta
    /// aggganngnnnnnnannntnnancaaatgtaatactggtcagcgccaaaaaactcacactgg
    HLA-DRB2 agaaaggtcttatgagtgtggtgaatccagcaaagtgtttaaatacaactccagcctcatta
    /// aacatcagataattcatactggaaaaaggccttagtggagtgaatgcaggaaagtcaccaaa
    HLA/DRB3 actgtcacctcattcagcaccaaaaggttcacatcggaccaagaacctattaatatatgtaa
    /// atctaatgttgaaagagttcagatggaaatctgcgaggatttcctgctgggaactacatta
    HLA-DRB4
    ///
    HLA-DRB5
    ///
    LOC100
    133484
    ///
    LOC100
    133661
    ///
    LOC100
    133811
    ///
    LOC730
    415
    ///
    RNASE2
    ///
    ZNF749
    113 215356_at TDRD12 aattgggcaggctcttgggaagtagaaagttctggtgtttttgctggtgaaggttttgactg
    tggagctcttctaacacccatatcagtgtctgtttctctgcatgtggctgctgccctgttgg
    tggagctctgggggcagagaccaggccgccgtccagtggcgcnccgtgcgcaccagctgcct
    gctgtttacacccaggtgcgccgagtctctttcatacagcacagcaaatgataatagctagt
    gacaatgtgtttcctgtgcactcgtgaaaatgcagggaggacaactgcatgcttagatctgt
    ttcttttttcagacattcaaatgttctaatatctgaagctaacattttgtaggatataggat
    gctgattatgtgaacaattagtcattggttttctgtactgctatgaatatgtctgatttcaa
    gttttggtcaaatatctaaaatgcaaggtgaaagtgcctttgtctctatgcttctaaaatcg
    ctcatgcttagttgtggtatggatgtcttccgcagtg
    114 215476_at — cttggtaagccttgcctgtagcggctccgctgccgagtgctttgacaccaggcgctcccaga
    gctctgcccccactgccaagcggcagctgctccggagggcacggggggctggatttggctgt
    ggcttctccagctctgcacaagagccccccttccctggccctgctgcagcatgactgcctcc
    tggctcgtgtcacccactctgtctctgtctctcttcatacgtttccagctgagctgggatcc
    atagtctgtttccctctccacgaccaatctatttatcttctctggaacttcttgtaatgccg
    ggagtgcagagcttacaagttggggcaggaagctttagaagcccaggnagccctgagaggct
    ctttccttgtaagtgggtctctccccaggagcctcttggaatatttagcagggacttttacc
    catgctgggtctagagaccctcccgcccctctgtttcctgccctcctacttagactgggatc
    tggtttccctcagctggttcccttgctagcgtgtgactctgtgtgtct
    115 215705_at PPP5C gttcacagcagtgggtaggcccagcagtggttcttgacatcacacgatgaggcgngcatctc
    ccgtcatccagggagaccagaggacccttgtctcactcccagttggctnttagtcacagccc
    cgctttgtctttgacatggacgtttgtgatgatcacgttcctcccgctccccgtgtntgaag
    agtgctccctgactggctgccgtctcctccctgtcgggtctggctgggttctccanagggag
    tgctgcggaggggacacagcanaggccccatgctcgtgatgtatgttgcagatcattttccc
    ccattctgtccttttttgttaaattgtggtaaaaagcacataacataaactgtaccncctta
    accatttgaaagtatatatcccagactgtcttttatctttagacttcacttgtggtttgttg
    cc
    116 215715_at SLC6A2 tcccctggaagttgtcctttctgatcctctcttcttttcccatttacaaatgatttcgtgac
    tgtagtttttgttcaccttctgtgcatctggcctgggggctgttagctcagaggagaggagc
    aaacaggaaaatgacttctgttctgtccccgctgttttgggggaagtctctcccactttggg
    atcctgctgaagctaggttcatgaggtcggaaatccccaccacatttgcctagactttgggc
    acaggagttcttagtccaccaaatcaga
    117 215850_s_at NDUFA5 cattttctctaactttatctcctatgcatttccttatgtgtcctgtacagcagtatattcca
    aaatccccagtggatgtctgaaaaccacatatagtaccaaactgtatatatgctatgttttg
    tttcatacatacctataataaagtttaatttatgaattaggcacaataagagataagcaggc
    tggacgtgctggctcacgcctgtaatcccagcactttgggaggctgaggcgggtggattgct
    ttagcccaggagtttaagaccagcctggccaacatggcaaaaccccgtctctataaaaaatg
    tggaaattaatcaggtgtggt
    118 215944_at — gagatgaccgaaaacttcaacccctgcagtcagcaatggtcaacagaaagggcccaattctc
    cacgacaatgcatgatcgcacattacacaactaaagcttcaaaagttgaactaactgggcta
    cgaagttttgcctcatccaccatattcacctgacctcccgccaaccgactaccacttcttca
    atcatctcgacaactttttgcaaggaaaacacttccacaaccagtagaatgcaaaaagtgct
    ttccaagagttcactgaatcctgaagcacggatttttatgctacaggaataaacaaacttat
    ttttcattggtaaaaatgtgttgattgtaatggatcctattttgattaatgaagatgtgttt
    gagcctagttataatgatttaaaattcacgatccaaaaccgcaattacttttgcatcagcct
    aatatgaggaagtaatagttgaacagaataattctttcctggaagtct
    119 215953_at DKFZP5 ttggtttggtctggtttggctacctgattcctgctgtctttttctacgccaggtgaagaggc
    64C196 actttcaagatccttctctgagacctgcaccaataagactataccaatgttcagttgaaaca
    tcaggtataagtttagcggaaacgaaagtacaacctgctttgaaataaattccaaggacaga
    ttgtcattaacgaaatagaaagtggactatgcccctcatgctgccagcgcctggtatgatgc
    ggcgtgacacgcagcgcttgcggcagtacaatgcccccaatcacccgccccgccccgacgcg
    ccgcccactcacggcaaagagagccacctagtgagggattattctcatttccgcggtggggt
    tctgcttttctttctaccatgagcgcccaaggatagacactcctactacctattacctcaaa
    tagcctacatttctttccgaa
    120 215973_at HCG4P6 agaacactgagcgaggctctgtagatggatgtaataaaaatctataaaacaatgtgtttaaa
    cctaagaattctactgctttccaattccttccctctgctccttttcctaacctcctgcttct
    ccagcccttccctctgtccctttcanccctcaggccctcctctccccttagtccccaccacc
    ctgtcacttctaaattgtggctctagcattgtcccattacctgctangtgactgttctctcc
    acagtggtcctgctcctgtgagtcagagtgtgtcatttcctcacctaaaacactccagtggc
    tccacctcggtcttgtgaagcttctagaatgtcaggcacgtgagcatatgagggcatacctg
    gttcatcttaggcactaaattnnnntttgttgactgaatgaatgaaatatgaatgtattaaa
    ttgcatcacagaaagttataaaatgtaaaacactgaaaaattaagaaatattttatnttatg
    taactagtgtgcatatcaattcattccgagtctgttgagcctgtgtat
    121 216050_at — aatgattcaactcatgtgatccagtgttacattcagtgtggtaatgaagaacagtcaaaaca
    ggcttttgaagaattgggagataatttggttgaattaagtaaagccaaatactccagaaata
    ttttaaagaaatgtctcacgttgtgaacatgtaccctagaacttaaagtataataaaaaaaa
    aaaaaannggaaagtatcttgcacaagctcacgtagctggtaagttacatagttgggatctg
    aattcagttgtggcttcatgcctgagcttttaactactactactaaactgagaaggcacttg
    cttgagtaaattatgtcatcctcttaat
    122 216066_at ABCA1 gatgtggcatgtgatgacattgcacatggncagttaantgngccaagaagngcagcagtagc
    agcaacnggagatgcaaagcccaacatgatggggagagaaantnttctttcaatatgtgctt
    ctgtaccaaaagtggaatttcacgagagacatattttggaacatttttccttttgtgtgtgc
    gtgagtgtttccctgtttccagccaagggtattgtgagtttctcctgggcctccttcagaat
    ctgggtgctctggaaagcagtgttttggcaacatggggaaagtatggcagtgtgggagggtc
    agctgggtctgggtttgaatattgcatttgaatattttaccagcattgatgtcggataaatt
    atttagtccctgtaagcctcagttttntcttnttctacatacacataatatatttgactctt
    tgttgtgat
    123 216240_at PVT1 tttcctaactttctgatcccttggaggtgataatcaaatattctagtctgaggcattgggat
    acatggtgctaggttctgagactctgcgtcaggcctgaaccctgcattttgtggaggtgggt
    gggagaatgtncccctggggaacatgcctagacacgggggacaacagttgccctcatgggga
    ggtacctgtttactcgctgttatgggaccgctttcacaaaaccactgcaggtgagtgagttc
    ctgctgaatatcaggcctggtgtctctagactcattattncccccacccaacccctatgtta
    gttcatctcgagccacatttttattgccataatccaggcctggacaggccaagatcttttaa
    caattttaattactgaaaataataactgcattttttttnaaagcccaacttttnggtanagt
    cagcccaaaatacagtctttgtgttgccatctgggaactggatttggaattgttcttccatg
    agactgcagagcag
    124 216881_x_at PRB1 ccacctcctccaggaaagccagaaagaccacccccacaaggaggtaaccagtcccaaggtcc
    /// cccacctcatccaggaaagccagaaggaccacccccacaggaaggaaacaagtcccgaagtg
    PRB4 cccgatctcctccaggaaagccacaaggaccaccccaacaagaaggcaacaagcctcaaggt
    /// cccccacctcctggaaagccacaaggcccacccccagcaggaggcaatccccagcagcctca
    PRH1 ggcacctcctgctggaaagccccaggggccacctccacctcctcaagggggcaggccaccca
    /// gacctgcccagggacaacagcctccccagtaatctaggattcaatgacaggaagtgaataag
    PRH2 aagatatcagtgaattcaaataattcaattgctacaaatgccgtgacattggaacaaggtca
    /// tcatagctctaac
    PRR4
    125 216989_at SPAM1 gtttgatgtctattatctcacttcatcctcaccaggaccccatccgagccttaatttcagtt
    gacagtaactattggatccccaggaatatgtttgcatatttggggagaaaatactattggag
    gggaacagaaatgctactaagggtctcactgtgtcacccaggctggagtccatcaaagctca
    ctgcagccttaaccttctgtgctcaagggatcctcccacttaagcctcctgagtagctggaa
    ctacaggcatatgccaccgagcctggctaatctttgatttttttgtacagattgtgtctcct
    tatgttgctcaggctggactcaaacttctggtctcaagcgatctttccatcttagcttccca
    aattgttggaattatggacatgagccagtgtgcttggcctgattttttttttttttttaatg
    agaaaaacgttccttaagaaaagtttcattgtaagacgaggacttgctatgttgccagtttg
    gtcttgaactcggtctcaagtgattctcctgccttgggttcccaaagcgtttgggccggcag
    atgt
    126 217004_s_at MCF2 ctgaattggaacacaccagcactgtggtggaggtctgtgaggcaattgcgtcagttcaggca
    gaagcaaatacagtttggactgaggcatcacaatctgcagaaatctctgaagaacctgcgga
    atggtcaagcaactatttctaccctacttatgatgaaaatgaagaagaaaataggcccctca
    tgagacctgtgtcggagatggctctcctatattgatgaagctactatgtcaaatggcaagta
    gctctttcctgcctgcttctcagctcatttggaaaaatactgcgcaaaagacattgagctca
    aatgatgcagatgttgttttcaggttaatggacacgcaaagaaaccacagcacatacttctt
    ttctttcatttaataaagcttttaattatggtacgctgtctttttaaaatcatgtatttaat
    gtgtcagatattgtgcttgaaagattctcatctcagaatacttttggact
    127 217253_at SH3BP2 gagtgtcttgactattctggctctttgtattttcatgtaaggtttttctcccatataagttt
    taaaatcagcttgtcaattccaacaacaatgatgcacttgatagtttgggaatttattatag
    ctatcaatcagttttgggaaaattgacgtctttacaatattgagttttctgattcatgaaca
    tggtttacctctcttcccatgggggtctcctttaaggtttaccaataggattttatatttgg
    ggccattgnggtcttgcttatcttaagtnnnnnnnnnnnnnnnaaatctcttgaccncatga
    tctgcccgccttgtcctcccaaagtgctgggattacaggcgtgagccaccgcacctggcctg
    caatacagtattgttaaccgtcttcaccatgttgtacgttagagctccagaaattatttanc
    atgcataactgaaactttatactctttgaacaccacctccccatttccctctcccggcagcc
    atttgtgcctctcggttctctttattagcttccattttgtgggtcagt
    128 217995_at SQRDL tacgtcaaagaccgctgctgcagtagctgcccagtcaggaatacttgataggacaatttctg
    taattatgaagaatcaaacaccaacaaagaagtatgatggctacacatcatgtccactggtg
    accggctacaaccgtgtgattcttgctgagtttgactacaaagcagagccgctagaaacctt
    cccctttgatcaaagcaaagagcgcctttccatgtatctcatgaaagctgacctgatgcctt
    tcctgtattggaatatgatgctaaggggttactggggaggaccagcgtttctgcgcaagttg
    tttcatctaggtatgagttaaggatggctcagcacttgctcatcttggatggcttctgggcc
    aaaactgcagtcactgaatgaccaagagcagcacgaaggacttggaacctatccttgtaaag
    agttccttgatgggtaatggtgaccaaatgcctcccttttcagtacctttgaacagcaacca
    tgtgggctactcatgatgggcttgat
    129 218768_at NUP107 ttggatgccctaactgctgatgtgaaggagaaaatgtataacgtcttgttgtttgttgatgg
    agggtggatggtggatgttagagaggatgccaaagaagaccatgaaagaacacatcaaatgg
    tcttactgagaaagctttgtctgccaatgttgtgttttctgcttcatacgatattgcacagt
    actggtcagtatcaggaatgcctacagttagcagatatggtatcctctgagcgccacaaact
    gtacctggtattttctaaggaagagctaaggaagttgctgcagaagctcagagagtcctctc
    taatgctcctagaccagggacttgacccattagggtatgaaattcagttatagtttaatctt
    tgtaatctcactaattttcatgataaatgaagtttttaataaaatatacttgttattagtaa
    ttttttcttttgcattaccatgtaaaatttagacatttgaattttgtacttttcagaatatt
    atcgtgacactttcaacatgtagggatatcagcgtttctctgtgtgct
    130 218881_s_at FOSL2 aggtcacagtatcctcgtttgaaagataattaagatcccccgtggagaaagcagtgacacat
    tcacacagctgttccctcgcatgttatttcatgaacatgacctgttttcgtgcactagacac
    acagagtggaacagccgtatgcttaaagtacatgggccagtgggactggaagtgacctgtac
    aagtgatgcagaaaggagggtttcaaagaaaaaggattttgtttaaaatactttaaaaatgt
    tatttcctgcatcccttggctgtgatgcccctctcccgatttcccaggggctctgggaggga
    cccttctaagaagattgggcagttgggtttctggcttgagatgaatccaagcagcagaatga
    gccaggagtagcaggagatgggcaaagaaaactggggtgcactcagctctcacaggggtaat
    ca
    131 218980_at FHOD3 gcacctcggagttgcagctgtgacactcataggttactcccaggagtgtgctgagcagaagg
    caagctcttgctggatgaaacccctccaggtggggttggggagacttgatattcacatccaa
    cagtttgaaaagggagagctcaattcccagcgtcaccccatggcttgtgttgcctgctacgc
    attgacttggatctccaggagtcccctgcacataccttctccatcgtgtcagctgtgtttct
    cttgattccgtgacacccggtttattagttcaaaagtgtgacaccttttctgggcaaggaac
    agcccctttaaggagcaaatcacttctgtcacagttattatggtaatatgaggcaatctgat
    tagcttcacagactgagtctccacaacacc
    132 219000_s_at DSCC1 tcaagtgagtgagttcccctctacttttagccttccacccaaactggaagcctctaggtgct
    atcaattatttatatccatcgtttacatccatgaaattggctgaataattactcctctgcct
    ggcgtagacatgtgctttgggaaaaaaacgagtttataatcctataatgaagaatactggca
    caggcaatgctcactcgaaaacttcaagtaatttctagttggttttggaatgcttgataaag
    ttcctttacagctttattttcctgatttgttttggtttagatcaaagttcaaattaatttta
    acttagctaatgaactcatcaccaggacagttggagggggtaggccgaggttaaatggtcca
    cgtttcaaaaatgttaat
    133 219171_s_at ZNF236 cttttgttcttgctgggttatttattttgattttagcattaaatgtcatctcaggatatctc
    taaaaggggttgtttaattcctaattgtatagaaagctagtttggtgaattgtattggttaa
    ttgactgtttaaggccttaacaggtgaatctagagcctacttttattttggttaaagaaaaa
    gaaaatatcaataattcaattttgtgtcttttctcaatttattagcaaacacaagacatttt
    atgtattatttcgatttacttcctaattataaaagctgcttttttgcagaacattccttgaa
    aatataaggttttgaaaagacataattttacttgaatctttgtggggtacaggttgatcttt
    atattttactggttgttttaaaaattctagaaaagagatttctaggcctcatgtataaccag
    ggttttgaggataaagaactgtatttttagaactatctcatcatagcatatctgctttggaa
    taactat
    134 219182_at FLJ2216 ttaccctcgtggctaagcaagtgtctgcaggagcagagatggctggaaggggcctctgcaca
    7 cggaagatggcttgttcagcccattcacctcctgaggatgtgggcagtctcctccaagaaca
    catggagctgcttcctgatcccaagcaggtcattgccactggaaggacatggccccggtgat
    ccatgcttcatgcccacccagaaacacacccctcagtgtgtgcctcagtttactttggagat
    cagttgtcgtttttagtgctcctttaggcttactaaaacagttttggaaacaaagctatttt
    gaagtattcaagcagaggaattccctaacactgacc
    135 219425_at SULT4A gaccattttgcgagtgtagccctgtttcactcggatcaggttggcacggccgcctgcgtgtc
    1 tgtccacctcatccctccgtgtatctgagggagtaaaggtgaggtctttattgcttcactgc
    ctaattttctcacccacattcgctgaagcgatggagagtcgggggccagtagccagccaacc
    ccgtggggaccggggttgtctgtcatttatgtggctggaaagcacccaaagtggtggtcagg
    agggtcgctgctgtggaaggggtctccgttcttggtgctgtatttgaaacgggtgtagagag
    aagcttgtgtttttgtttgtaatggggagaagcgtggccaggcagtggcacgtggcatcgca
    tggtgggctcggcagcaccttgcctgtgtttctgtgagggaggctgctttctgtgaaatttc
    tttatatttttctatttttagtactgtatggatgttactgagcactacacatgatccttctg
    tgcttgcttg
    136 219520_s_at WWC3 aaggaaggccagagagccgcgcagttctctgcaggtgcagatgcaggcagtggaggtggcct
    gagcaggcagaaggacaccaagcgccctatgttgcttgtcattcatgacgtggtcttggagc
    ttctgactagttcagactgccacgccaaccccagaaaataccccacatgccagaaaagtgaa
    gtcctaggtgtttccatctatgtttcaatctgtccatctaccaggcctcgcgataaaaacaa
    aacaaaaaaacgctgccaggttttagaagcagttctggtctcaaaaccatcaggatcctgcc
    accagggttcttttgaaatagtaccacatgtaaaagggaatttggctttcacttcatctaat
    cactga
    137 219537_x_at DLL3 tcccggctacatgggagcgcggtgtgagttcccagtgcaccccgacggcgcaagcgccttgc
    ccgcggccccgccgggcctcaggcccggggaccctcagcgctaccttttgcctccggctctg
    ggactgctcgtggccgcgggcgtggccggcgctgcgctcttgctggtccacgtgcgccgccg
    tggccactcccaggatgctgggtctcgcttgctggctgggaccccggagccgtcagtccacg
    cactcccggatgcactcaacaacctaaggacgcaggagggttccggggatggtccgagctcg
    tccgtagattggaatcgccctgaagatgtagaccctcaagggatttatgtcatatctgctcc
    ttccatctacgctcgggaggtagcgacgccccttttccccccgctacacactgggcgcgctg
    ggcagaggcagcacctgctttttccctacccttcctcgattctgtccgtgaaatgaattggg
    tagagtctctggaaggttttaagcccattttcagttctaacttactttcatcctattttgca
    tccc
    138 219617_at C2orf34 tgaagaaaaccttcattacccgcttctgcttattttgaccaaacatggatagaagattaagc
    ttctcaaagacgaagaaacgtatcaagtgcatagggaatatttttacaaaaacggaaatctg
    taaggggtataatcgcctgcctgcgccctttgcagcatttcacgtgtgggctatggactcca
    cctgtcctcacccacgttattccccagctgccctctccagctccctccccgcctctttttac
    actctgcttgttgctcgtcctgccctaaacctttgtttgtctttaaatgtgtataagctgcc
    tgtctgtgacttgaatttgactggtgaacaaactaaatatttttccctgtaattgagacaga
    atttcttttgatgatacccatccctccttcattttttttttttttttggtctttgttctgtt
    ttggtggtggtagtttttaatcagtaaacccagcaaatatcatgattctttcctggttagaa
    aaataaataaagtgtatctttttatctccctc
    139 219643_at LRP1B tattcacaagttttggagggctttttgttcctctgatagacatgactgacttttagctgtca
    taatgtattaacctaacagatgaaatatgttaaatatgtggttgctctttatccctttgtac
    aagcattaaaaaaactgctgttttataagaagactttttgttgtactatgtgcatgcatact
    acctatttctaaactttgccatattgaggcctttataaactattgatttatgtaatactagt
    gcaattttgcttgaacaatgttatgcatatcataaactttttcaggttcttgtttaagtaca
    ttttttaaattgaacagtatttttcattttggttataatatagtcattttgcctatgtttc
    140 219704_at YBX2 ctcagcccctgtcaacagtggggaccccaccaccaccatcctggagtgattccaactcaact
    caaaggacacccagagctgccatctggtatctgccagtttttccaaatgacctgtaccctac
    ccagtaccctgctccccctttcccataattcatgacatcaaaacaccagcttttcacctttt
    ccttgagactcaggaggaccaaagcagcagccttttgctttttcttttttcttccctcccct
    tatcaagggttgaaggaagggagccatccttactgttcagagacagcaactccctcccgtaa
    ctcaggctgagaag
    141 219882_at TTLL7 gtttctgtgattcaggatcctcttgggagagtatattcaataaaagcccggaggtggtgact
    cctttgcagctccagtgttgccagcgcctagtggagctttgtaaacagtgcctgctagtggt
    ttacaaatatgcaactgacaaaagaggatcactttcaggcattggtcctgactggggtaatt
    ccaggtatttactaccagggagcacccaattcttcttgagaacaccaacctacaacttgaag
    tacaattcacctggaatgactcgctccaatgttttgtttacatccagatatggccatctgtg
    aaacagaagggaagatcgccattggttat
    142 219937_at TRHDE ggaggtcccaaatatgtggtctatcaccactgaattcatgtaatagataagaaaaaaattag
    aggtggatgtcttgttttgtgtcatgaattactaaaatctcttagtagttgtggtatatttt
    tgagtaaaattaccatttccagatttgagtttgaagggcttttatagttgtattttcctcct
    cactgttaataatcataatcctttttcagtattttagtggccttgaacaactggtttatcta
    caatctcaaatcctaagtgtataattatgtgcaatgttcaatacctcatataatacttgctc
    aacagtatagtggtaccaatggcattaagatggtgtttttgttctacatatttttcaataat
    ttattctttctaatgttgaaattatatcaggctttaccggtt
    143 219955_at L1TD1 gaagttgcaacattcgtttgataggaattccagaaaaggagagttatgagaatagggcagag
    gacataattaaagaaataattgatgaaaactttgcagaactaaagaaaggttcaagtcttga
    gattgtcagtgcttgtcgagtacctagtaaaattgatgaaaagagactgactcctagacaca
    tcttggtgaaattttggaattctagtgataaagagaaaataataagggcttctagagagaga
    agagaaattacctaccaaggaacaagaatcaggttgacagcagacttatcactggacacact
    ggatgctagaagtaaatggagcaatgtcttcaaagttctgctggaaaaaggctttaatccta
    gaatcctatatccagccaaaatggcatttgattttaggggcaaaacaaaggtatttcttagt
    attgaagaatttagagattatgttttgcatatgcccaccttgagagaattactggggaataa
    tataccttagcacgccagggtgactaca
    144 220029_at ELOVL2 gttatacagatgccatgctccacaccacgagcagtgtacaaatctggctgcccgtttacttt
    ctgagcaagcactggagtccactccgacctttttctttgaacatgcatgctgctggaatatg
    tataaatcagaactagcagaagtagcagagtgatgggagcaaaataggcactgaattcgtca
    actcttttttgtgagcctacttgtgaatattacctcagatacctgttgtcactcttcacagg
    ttatttaagttcttgaagctgggaggaaaaagatggagtagcttggaaagattccagcactg
    agccgtgagccggtcatgagccacgataaaaaatgccagtttggcaaactcagcactcctgt
    tccctgctcaggtatatgcgatctctactgagaagcaagcacaaaagtagaccaaagtatta
    atgagtatttcctttctccataagtgcaggactgttactcactactaaactct
    145 220076_at ANKH gaacgtcgtatgagatcctacaatggaagaataaaatcacctcattcttcatttcagatctg
    aacattagcagtgatctagatttttttttttttaaacaaaattaagtgtgcttagagtcatc
    cctctacatgggctgtggctgtcagcccataggtttgtcagtttcacatcaaaactgtgggt
    ataaactgttgaaaccaatcacattaaaatatttagctgggcacagtggtgtgcatctgtag
    tcccagctacttgggaggctgaggcaggaggatcgcttaagcacaggagttggaatccagcc
    tgagcaacagagcaaaaccccgtctctaaaatacaaataaaatatttgtgtagtttttgatt
    aaaattgactacagcggtcagtataaaatacatgtcgcttttaaggaagtgctctttatgta
    tctaacagatggaagtttttgcattggtaagagcatttatatatgctttgtttcagggttta
    tggatttgtattcatatattgtcaaataggtttcatactctaattttactt
    146 220294_at KCNV1 agattatatccctatcttctttttcatgtaaaccactggtcacaaatgaactgatctctgta
    tcccattattactataagaggtgggaatcccaaaactgcttagattgcagtacatgagttta
    cacaaagacttcaacaattgcacatcttcattctcccaactgagtgtagtatgtggagcata
    aaacagcatattcttagtatttcatgaatatcagatggtctttaaatgtctctttatggatg
    tattgttcacattatggctttaaaataatgaatatgtaaaagtgaggtagtgaacatcctaa
    atttctacactggaattactaaataatcttatttcataaaatgggaaatatatgttaaatga
    catcactggatgaacttgaagatcttttacttgttaacaaaaaaatactatggacagctttc
    tgattgttggggtaaatagcaaatgttcaaactttgcaggcattttgacattcatcataaca
    acacaattcctagacatt
    147 220366_at ELSPBP1 ttaggcagtctgtggtgctcagtcacctctgtcttcgatgagaaacagcagtggaaattctg
    tgaaacgaatgagtatgggggaaattctctcaggaagccctgcatcttcccctccatctaca
    gaaataatgtggtctctgattgcatggaggatgaaagcaacaagctctggtgcccaaccaca
    gagaacatggataaggatggaaagtggagtttctgtgccgacaccagaatttccgcgttggt
    ccctggctttccttgtcactttccgttcaactataaaaacaagaattattttaactgcacta
    acaaaggatcaaaggagaaccttgtgtggtgtgcaacttcttacaactacgaccaagaccac
    acctgggtgtattgctgatgctgaggaaaggagaaatatcttcagaggaagactgccgccat
    actgaggctgagcacagatttgtctttttcattgcatctgtcaa
    148 220394_at FGF20 gtgtggcagtgggactggtcagtattagaggtgtggacagtggtctctatcttggaatgaat
    gacaaaggagaactctatggatcagagaaacttacttccgaatgcatctttagggagcagtt
    tgaagagaactggtataacacctattcatctaacatatataaacatggagacactggccgca
    ggtattttgtggcacttaacaaagacggaactccaagagatggcgccaggtccaagaggcat
    cagaaatttacacatttcttacctagaccagtggatccagaaagagttccagaattgtacaa
    ggacctactgatgtacacttgaagtgcgatagtgacattatggaagagtcaaaccacaacca
    ttctttcttgtcatagttcccatcataaaataatgacccaagcagacgttcaaa
    149 220397_at MDM1 tatgcattttttaccacaatttttaaaaagtttgaatagaaatttttaatgtctttgagtgg
    attttgttttttgaacagttggatagacttctgcgtaagaaagctggattgactgttgttcc
    ttcatataatgccttgagaaattctgaatatcaaaggcagtttgtttggaagacttctaaag
    aaactgctccagcttttgcagccaatcaggtagcttaatggatgtaatacatttctgagtac
    cattatcttatctagtaatgtagatttacatagaattaagagttgaaagaaattaagtactt
    aagtagcctggaggtaggttctagaaaaccaaaatgagagttttgctaaaatcatcctatta
    cttatgatttatggtagtaatattatactgtcctaggcttctgatgatcattgttgccagat
    gcagcacatatactaaatatgagacagggtaatgaaaacttggggaactggtaagtttttgc
    atgctac
    150 220541_at MMP26 tgacccctttgatattccagcaagtgcagaatggagatgcagacatcaaggtttctttctgg
    cagtgggcccatgaagatggttggccctttgatgggccaggtggtatcttaggccatgcctt
    tttaccaaattctggaaatcctggagttgtccattttgacaagaatgaacactggtcagctt
    cagacactggatataatctgttcctggttgcaactcatgagattgggcattctttgggcctg
    cagcactctgggaatcagagctccataatgtaccccacttactggtatcacgaccctagaac
    cttccagctcagtgccgatgatatccaaaggatccagcatttgtatggagaaaaatgttcat
    ctgacataccttaatgttagcacagaggacttattcaacctgtcctttcagggagtttattg
    gaggatcaaagaactgaaagcactagagcagccttggggactgctaggatgaagccctaaag
    aatgcaacctagtcaggttagctgaaccgacactcaaaacgctac
    151 220653_at PEG3 aaggtagaaagccttccgtccagtgtgcgaatctctgtgaacgtgtaagaattcacagtcag
    /// gaggactactttgaatgttttcagtgcggcaaagcttttctccagaatgtgcatcttcttca
    ZIM2 acatctcaaagcccatgaggcagcaagagtccttcctcctgggttgtcccacagcaagacat
    acttaattcgttatcagcggaaacatgactacgttggagagagagcctgccagtgttgtgac
    tgtggcagagtcttcagtcggaattcatatctcattcagcattatagaactcacactcaaga
    gaggccttaccagtgtcagctatgtgggaaatgtttcggccgaccctcatacctcactcaac
    attatcaactccattctcaagagaaaactgttgagtgcgatcactgttgagaaacctttagt
    cacagcacacacttttctcaacattattggcttcctcctagagtgttgtgagtgtgagaagg
    cctttcactagcccc
    152 220700_at — atgttactacaaacttgattaaacttctggtggaaattccatcacattttatgcaattttca
    atttatttctccaatttatttttaatgccacatggacattatattccttaaccattcttttg
    catgtgattaacatttgtgaaattaaccacttaagcaagtgtttttgctttgatgaaagaaa
    aatgtttaaaatcctactggatatgaaactgaaagtaatgttttgtgttttttgtttcaaat
    gaaagtgtaaattaagaatttgttggcagggcgtggtggctcatgcctgtaatcccagcact
    ttgggaggccgaggtgggcagatcacctgaggtcagcagtccaagaccaccctggccaacat
    ggtgaagtcccgtctctactaaaaatacaaaaatcagctgggcatggtggcgggcacttgta
    gtcccagctactcaggaggctgaagcaggagaatcacttgaactcaggaggcagaagttgcg
    gttagccga
    153 220703_at C10orf1 cctctctccactctctagaaatattaaggctaggctgctgctgtatgtcagggctagtcccc
    10 tcttctatgaatccagaataactctgaagaagccgagtaacaggcatgaagtgaagagaaat
    cgctgtaacaggaagacagcaaagcagatgctaatgaccacactatttaacgaactggaacc
    aacgagaaaatacggtattactgaagactgcacttccttgaacagagtgctcttctcagcaa
    atcggaaatgcctacacaaatcgctttacaagaaagactgtttcaaagcagcacctttctca
    atgttctcgttcaggtgacaattcttcttggtctcagctccaattttattgtcattttcatc
    aataaggatacacatctctgccaggagttgaacctgttgcttgtcgaggtggttagtgttta
    tttcaggcatcattacaaaatgtctgatctgttctagaaccct
    154 220771_at LOC511 aagtatctccatacaaaatacggttgaattacaaaaagaaaattgtaacattagcatggaca
    52 aacctggcaggtactccttaactctcctaagtaataaaaactgtaaaatgcaaataagcctt
    cgatgacatttactaacctttactaaagtatcaatgatgacttggttgtttaaacagctgac
    atttgggcaatttgagtatgtcaaactcaataatactggttttcatttgcaagatccactta
    aaacttaaggaggccaaaaaacatcatttaaaataccctataaattataatcatacatatga
    tacgaaaaatatcctacttcag
    155 220817_at TRPC4 catacacatacgtattttccgtagtgctctgggtgggggaaaatgtttaaattgtattagca
    aatgctaacttacactttatagcatttatcagctgtggcatattacctgtaacatgtttaaa
    ttaaggcaaaggcaatcaaaaacctttttgttttgtagcctgcttttgctttcacaatttgt
    cttacaatt
    156 220834_at MS4A12 gctggccaagactactgggccgtgctttctggaaaaggcatttcagccacgctgatgatctt
    ctccctcttggagttcttcgtagcttgtgccacagcccattttgccaaccaagcaaacacca
    caaccaatatgtctgtcctggttattccaaatatgtatgaaagcaaccctgtgacaccagcg
    tcttcttcagctcctcccagatgcaacaactactcagctaatgcccctaaatagtaaaagaa
    aaaggggtatcagtctaatctcatggagaaaaactacttgcaaaaacttcttaagaagatgt
    cttttattgtctacaatgatttctagtctttaaaaactgtgtttgagatttgtttttaggtt
    ggtcgctaatgatggctgtatctcccttcactgtctcttcctacattaccactactacatgc
    tggcaaaggtgaaggatcagaggactgaaaaatgattctgcaactctcttaaa
    157 220847_x_at ZNF221 tgacatgcaccagagggtccacaggggagagcgaccctataattgtaaggaatgtggaaaga
    gctttggctgggcttcatgtcttttgaaacatcagagactccacagtggagaaaagccattg
    aaatctggagtgtgggaagagatctactcagaattcacagcttcatttacatcagtaagtct
    atgtgggagaaaagccatataaatgtgagaagtgtgggaagggctttggctgggcctcaact
    catctgacccatcaattctccacagcagagaaaaaccattcaaatatgagaactgtgggaag
    agctttgtacatagatcatatctttttttttttttttgagacagagtctcactctttcaccc
    aagcctgactgcagtggcg
    158 220852_at PRO176 gaaaagcgccctgtgctgagtaaagcagccagtcttctcttgtcacagtaaaaggctgggag
    8 taaaatttcccataaacacaggggaaacctacatttactcacatgccaaggaaaatggcacg
    gaagacccacgtgtagccacagcagagtctatgcagagggcctgcaaatgcctggggtgcga
    gtgaatgcctggaggggcggagtttccaagataacagctattgtgttttctttttcacactt
    cagaagagaatcctaaggactagactccgctcagtgcattcctttttcatacactgatctca
    agtacaatcacataattttgaaaatccatgtagtcctccctaaataaaattataaggatagg
    tttctatttccttccgattacctagatacctccgtcttctggaaaaccccaaaaagaccagt
    agacgaatcaggaaggtcctaggagtgattcctccaat
    159 220970_s_at KAP2.1B tgcccccacagagcaatacactgaagcctaaacatctatctggtgtttttaaaaagttaaaa
    /// gaaaaatagattttttttcacaaggtgacaatagtgatttttaccatctggatacagcctgg
    KRTAP2- tgtaagcagacgtccattaccaccctcacccacattttcaggtgtctacatcagccttagtc
    4 /// attatggatagtaaatcgacctttaagaattcctggggtggactttgcaaacacattctaca
    LOC644 acctgatggtttttactgctcaaactgtcaccatcatcttttgcaatgtgttgctcactgtt
    350 /// gtcaata
    LOC728
    285 ///
    LOC728
    934 ///
    LOC730
    755
    160 220981_x_at LOC650 ggacagtctcagggttctgttctcgccttcacccggaccttcattgctacccctggcagcag
    686 /// ttccagtctgtgcatcgtgaatgacgagctgtttgtgagggatgccagcccccaagagactc
    NXF2 agagtgccttctccatcccagtgtccacactctcctccagctctgagccctccctctcccag
    /// gagcagcaggaaatggtgcaggctttctctgcccagtctgggatgaaactggagtggtctca
    NXF2B gaagtgccttcaggacaatgagtggaactacactagagctggccaggccttcactatgctcc
    agaccgagggcaagatccccgcagaggccttcaagcaaatctcctaaaaggagccctccgat
    gtcttctttgtcttcgttcacatcctctttgtttcctcttttcaccagcctaaggcctggct
    gaccaggaagccaacgttaacttgcaggccacgtgacataac
    161 220993_s_at GPR63 aagtctgcattgaatccgctgatctactactggaggattaagaaattccatgatgcttgcct
    ggacatgatgcctaagtccttcaagtttttgccgcagctccctggtcacacaaagcgacgga
    tacgtcctagtgctgtctatgtgtgtggggaacatcggacggtggtgtgaatattggaactg
    gctgacattttgggtgatgcttgttctttattgacattgaattctctttctcatagcctctc
    cactttatttttttttatagggtttgtgtatgtatgtgtgtgagcagtgtaaagaaagaatg
    gtaattatagttctgttaccaagaataaataataggaaagtgattacaaatattacctccag
    ggttcaatagaaatcctcaatttagggtgaggagacttttttttggttttggggtttttcct
    tgattgattttgttttcatagtgggaatcaggattgtgctttattgagcctgcagttacatt
    gaattgtaggtgtttcgtgtgctgctaaggta
    162 221018_s_at TDRD1 gggactgtcgatgtagctgataagctagtgacatttggtctggcaaaaaacatcacacctca
    aaggcagagtgctttaaatacagaaaagatgtataggacgaattgctgctgcacagagttac
    agaaacaagttgaaaaacatgaacatattcttctcttcctcttaaacaattcaaccaatcaa
    aataaatttattgaaatgaaaaaactggtaaaaagttaagtaagttaaatcgtatgttttcg
    cctcttctgtgatcaccaataggacatcttcaggcatattggcaggatagagctaatggagt
    gaaacctattgtaaggctgtactttcgtgatttaatgacctgaggtttggtcataatgcttc
    tgctgtttttgtaggtttatctgatcgttttcctttgctactgctaatggaactgaaccccc
    aggggtattccagttgtaatagcctttccttactgttgtttgg
    163 221077_at ARMC4 gttgagttgaaattctgccgcttactcaatggccttgggtgatgatgctgtaccctaattct
    aaaggaagcaatgaacccccttttcagctaccttactgataagcacttatgttctgccttct
    gctatcctgatggttcgggttgtctgtcttactatctacttcttgagtagagagaccacatt
    aaatttattgctgtatctcacagggcatcttgctagtgtgcacaggctcgcctccctacctc
    tgccccgatggtgtgaaggggagagggcgaggttccttagtggcagggctttgctgttcttc
    actctcagccccctgaaagcagttcttcctgcctctgagcctgtctttccttctgctgttaa
    cttctttcctacttttcttgcatccctctcccttccttttcctgccgtctttcttgtagaca
    t
    164 221137_at — aaaaggactaactcacatggctgcagtaagtgctggctgttagctggaagcacaaccaaggc
    tgttaacaggtgtgccttggttctcttccatatggcttctcttttgttttcagtactctgca
    gtttaattatgatgcatgcaggtgtgaatttctgtttattctgcttgggatgtgttttcctt
    ctgggatctgtgaatcggtttctcattatttttgtaaaacctgaagccagttatctcttaaa
    ataccagctctccttg
    165 221168_at PRDM1 ctggacttcttggatgagctcaccctgaaccgcccaggcggtctgctcttggtgttcagaat
    3 cacatcaatgcgaacgtcacagcgccttcgagggcgcagattttaactgccacgtattttta
    agttgtacttttctgtggaggaaattgtgccttttgaaacgacgttttgtgtgtgtatttca
    cgttagcatttcattgcataggcaaaacactagtcacaattgggtagatgtgacatccatat
    acttgtttacattttatctgttctcatgtcaaagactactccttgccccattgaatatatag
    tggtagcaggtgtacaaattggtcaagttgcaattatttatgagagaataatgataaatgta
    aaatatctaaagcatgaatctaagagcacgcaatatataattttaaagaaaatattctattt
    ggtagaatacaaatgtggtgtgtgttgttttataatgactgctgtacagtgggtatagtatt
    ttggttttggttccagattgtgcaatc
    166 221258_s_at KIF18A gtgaagacatcaagagctcgaagtgtaaattacccgaacaagaatcactaccaaatgataac
    aaagacattttacaacggcttgatccttcttcattctcaactaagcattctatgcctgtacc
    aagcatggtgccatcctacatggcaatgactactgctgccaaaaggaaacggaaattaacaa
    gttctacatcaaacagttcgttaactgcagacgtaaattctggatttgccaaacgtgttcga
    caagataattcaagtgagaagcacttacaagaaaacaaaccaacaatggaacataaaagaaa
    catctgtaaaataaatccaagcatggttagaaaatttggaagaaatatttcaaaaggaaatc
    taagataaatcacttcaaaaccaagcaaaatgaagttgatcaaatctgcttttcaaagttta
    tcaataccctttcaaaaatatatttaaaatctttgaaagaagacccatcttaaagctaagtt
    tacccaagtactttcagcaagc
    167 221319_at PCDHB8 cgggagcctgtctcagaactatcagtacgaggtgtgcctggcaggaggctcagggacgaatg
    agttccagttcctgaaaccagtattacctaatattcagggccattcttttgggccagaaatg
    gaacaaaactctaactttaggaatggctttggtttcagccttcagttaaagta
    168 221393_at TAAR3 gaactccaccataaagcaactgctggcattttgctggtcagttcctgctcttttttcttttg
    gtttagttctatctgaggccgatgtttccggtatgcagagctataagatacttgttgcttgc
    ttcaatttctgtgcccttactttcaacaaattctgggggacaatattgttcactacatgttt
    ctttacccctggctccatcatggttggtatttatggcaaaatctttatcgtttccaaacagc
    atgctcgagtcatcagccatgtgcctgaaaacacaaagggggcagtgaaaaaacacctatcc
    aagaaaaaggacaggaaagcagcgaagacactgggtatagtaatgggggtgtttctggcttg
    ctggttgccttgttttcttgctgttctgattgacccatacctagactactccactcccatac
    taatattggatcttttagtgtggctccggtacttcaactctacttgcaaccctcttattcat
    ggcttttttaatccatggtttcagaaagcattcaagtacatagtgtcaggaaaaatatttag
    ctcccattcagaaactgc
    169 221591_s_at FAM64A cacatctggacccatcagtgactgcctgccatagcctgagagtgtcttggggagaccttgca
    gagggggagaattgttccttctgctttcctaggggactcttgagcttagaaactcatcgtac
    acttgaccttgagccttctatttgcctcatctataacatgaagtgctagcatcagatatttg
    agagctcttagctctgtacccgggtgcctggtttttggggagtcatccgcagagtcactcac
    ccactgtgtttctggtgccaaggctcttgagggccccactctcatccctcctttccctacca
    gggactcggaggaaggcataggagatatttccaggcttacgaccctgggctcacgggtacct
    atttatatgctcagtgcagagcactgtggatgtgccaggaggggtagccctgttcaagagca
    atttctgccctttgtaaattatttaagaaacctgctttgtcattttattagaaagaaaccag
    cgtgtgactttcctagataacactgctttc
    170 221609_s_at WNT6 ccgccaggagagcgtgcagctcgaagagaactgcctgtgccgcttccactggtgctgcgtag
    tacagtgccaccgttgccgtgtgcgcaaggagctcagcctctgcctgtgacccgccgcccgg
    ccgctagactgacttcgcgcagcggtggctcgcacctgtgggacctcagggcaccggcaccg
    ggcgcctctcgccgctcgagcccagcctctccctgccaaagcccaactcccagggctctgga
    aatggtgaggcgaggggcttgagaggaacgcccacccacgaaggcccagggcgccagacggc
    cccgaaaaggcgctcggggagcgtttaaaggacactgtacaggccctccctccccttggcct
    ctaggaggaaacagttttttagactggaaaaaagccagtctaaaggcctctggatactgggc
    tccccagaactgc
    171 221718_s_at AKAP13 gcgatgcagaaatgaaccaccggagttcaatgcgagttcttggggatgttgtcaggagacct
    cccattcataggagaagtttcagtctagaaggcttgacaggaggagctggtgtcggaaacaa
    gccatcctcatctctagaagtaagctctgcaaatgccgaagagctcagacacccattcagtg
    gtgaggaacgggttgactctttggtgtcactttcagaagaggatctggagtcagaccagaga
    gaacataggatgtttgatcagcagatatgtcacagatctaagcagcagggatttaattactg
    tacatcagccatttcctctccattgacaaaatccatctcattaatgacaatcagccatcctg
    gattggacaattcacggccctt
    172 221950_at EMX2 gtaggctcagcgatagtggtcctcttacagagaaacggggagcaggacgacgggggngctgg
    ggntggcgggggagggtgcccacaaaaagaatcaggacttgtactgggaaaaaaacccctaa
    attaattatatttcttggacattccctttcctaacatcctgaggcttaaaaccctgatgcaa
    acttctcctttcagtggttggagaaattggccgagttcaaccattcactgcaatgcctattc
    caaactttaaatctatctattgcaaaacctgaaggactgtagttagcggggatgatgttaag
    tgtggccaagcgcacggcggcaagttttcaagcactgagtttctattccaagatcatagact
    tactaaagagagtgacaaatgcttccttaatgtcttctataccagaatgtaaatatttttgt
    gttttgtgttaatttgttagaattctaacacactatatacttccaa
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Claims (18)

1-44. (canceled)
45. A method for prognosing or classifying a subject with non-small cell lung cancer (NSCLC) comprising:
determining relative expression levels of at least 15 biomarkers in a tumor sample from the subject, wherein the at least 15 biomarkers comprise FAM64A, MB, EDN3, ZNF236, FOSL2, MYT1L, MLANA, L1CAM, TRIM14, STMN2, UMPS, ATP1B1, HEXIM1, IKBKAP, and MDM2;
calculating a combined score from the relative expression levels, including the relative expression levels of FAM64A, MB, EDN3, ZNF236, FOSL2, MYT1L, MLANA, L1CAM, TRIM14, STMN2, UMPS, ATP1B1, HEXIM1, IKBKAP, and MDM2, and classifying the subject into a high or low risk group based on the combined score.
46. The method of claim 45, further comprising selecting adjuvant chemotherapy if the subject is in the high risk group.
47. The method of claim 45, further comprising selecting resection alone for the low risk group.
48. The method of claim 45, wherein the combined score is calculated from the relative expression levels of 16, 17, or 18 biomarkers.
49. The method of claim 48, wherein the one, two or three additional biomarkers are selected from Table 3.
50. The method of claim 45, wherein the additional one, two, or three biomarkers are selected from RGS4, UGT2B4, and MCF2.
51. The method of claim 45, wherein the combined score is calculated according to Formula I:

Combined score=0.557×PC1+0.328×PC2+0.43×PC3+0.335×PC4  (Formula I),
wherein PC1 is the sum of the relative expression level for each biomarker multiplied by a first principal component for each biomarker, PC2 is the sum of the relative expression level for each biomarker multiplied by a second principal component for each biomarker, PC3 is the sum of the relative expression level for each biomarker multiplied by a third principal component for each biomarker, and PC4 is the sum of the relative expression level of each biomarker multiplied by a fourth principal component for each biomarker.
52. The method of claim 45, and wherein the subject has stage I or stage II NSCLC.
53. The method of claim 45, wherein the relative expression levels are determined by microarray.
54. The method of claim 45, wherein the relative expression levels are determined by quantitative PCR.
55. A method for prognosing or classifying a subject with stage I or stage II non-small cell lung cancer (NSCLC) comprising:
determining relative expression levels of at least 15 biomarkers in a tumor sample from the subject, wherein the at least 15 biomarkers comprise FAM64A, MB, EDN3, ZNF236, FOSL2, MYT1L, MLANA, L1CAM, TRIM14, STMN2, UMPS, ATP1B1, HEXIM1, IKBKAP, and MDM2;
calculating a combined score from the relative expression levels, including the relative expression levels of FAM64A, MB, EDN3, ZNF236, FOSL2, MYT1L, MLANA, L1CAM, TRIM14, STMN2, UMPS, ATP1B1, HEXIM1, IKBKAP, and MDM2, and
classifying the subject into a high or low risk group based on the combined score, and
selecting adjuvant chemotherapy if the subject is in the high risk group, and selecting resection alone for the low risk group.
56. The method of claim 55, wherein the combined score is calculated from the relative expression levels of 16, 17, or 18 biomarkers.
57. The method of claim 56, wherein the one, two or three additional biomarkers are selected from Table 3.
58. The method of claim 57, wherein the additional one, two, or three biomarkers are selected from RGS4, UGT2B4, and MCF2.
59. The method of claim 55, wherein the combined score is calculated according to Formula I:

Combined score=0.557×PC1+0.328'PC2+0.43×PC3+0.335×PC4  (Formula I),
wherein PC1 is the sum of the relative expression level for each biomarker multiplied by a first principal component for each biomarker, PC2 is the sum of the relative expression level for each biomarker multiplied by a second principal component for each biomarker, PC3 is the sum of the relative expression level for each biomarker multiplied by a third principal component for each biomarker, and PC4 is the sum of the relative expression level of each biomarker multiplied by a fourth principal component for each biomarker.
60. The method of claim 55, wherein the relative expression levels are determined by microarray.
61. The method of claim 55, wherein the relative expression levels are determined by quantitative PCR.
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