JP2016185142A - Method for assisting diagnosis of recurrence risk of colon cancer, program, and computer system - Google Patents

Abstract

PROBLEM TO BE SOLVED: To provide a highly reliable method for assisting diagnosis of recurrence risk of colon cancer regarding various cases of colon cancer.SOLUTION: A method for assisting diagnosis of recurrence risk of colon cancer includes: a measuring process of severally measuring an expression amount of a plurality of genes selected from a first gene group which exists in a region from 18q21 to 18q23 on a long chain of an 18-th chromosome, a plurality of genes selected from a second gene group which exists in a region from 20q11 to 20q13 on a long chain of a 20-th chromosome, and a plurality of genes selected from a third gene group including ANGPTL2, AXL, C1R, C1S, CALHM2, CTSK, DCN, EMP3, GREM1, ITGAV, KLHL5, MMP2, RAB34, SELM, SRGAP2 P1 and VIM, in a biological sample collected from a colon cancer patient; and a process of determining recurrence risk of colon cancer in the patient based on the expression amounts measured in the measuring process.SELECTED DRAWING: None

Description

  The present invention relates to a method for assisting diagnosis of recurrence risk of colorectal cancer. In particular, with respect to nucleic acids obtained from tissues of colorectal cancer patients, the expression level data of genes belonging to a predetermined gene group is acquired, and diagnosis of recurrence risk of colorectal cancer of the patient is assisted based on the acquired expression level data The present invention relates to a method, a program, and a computer system.

  Colorectal cancer is a general term for carcinomas that occur in the cecum, colon, and rectum. Like many cancers, early detection is important for the treatment of colorectal cancer. In the treatment of cancer, an anticancer agent having a strong side effect may be used, and in this case, the patient is forced to bear a great burden. In order to reduce such a burden on the patient, it is important for the doctor to select the most appropriate treatment method for the patient, and for this purpose, the doctor accurately determines the progression, malignancy, symptoms, etc. of the patient's cancer. It is necessary to grasp.

  In addition, accurately predicting the prognosis of a patient is important for improving the quality of life (QOL) in the prognosis of the patient. Dukes classification, which is a histopathological technique, is known as a method for predicting the prognosis of colorectal cancer. Dukes classification is widely used internationally, and is a method of classifying into Dukes A, B, C, and D according to the degree of cancer invasion. The Dukes classification is a classification method performed by the doctor with the naked eye, and there is a problem that errors are likely to occur by the doctor. In addition, there is also a problem that a difference in diagnosis is likely to occur due to a difference in facilities where colon cancer tissues are acquired.

  In recent years, studies for predicting the prognosis of cancer using genetic markers have been conducted, focusing on the increase or decrease in the expression level of a specific gene. For example, Patent Document 1 discloses a molecular analysis for predicting recurrence of colorectal cancer in a patient diagnosed with colorectal cancer or treated for colorectal cancer. The technique disclosed in Patent Document 1 predicts the prognosis of recurrence for a specific colorectal cancer and has a problem that it cannot be applied to all colorectal cancers.

US Patent Application Publication No. 2008/058432

  An object of the present invention is to provide a highly reliable diagnosis assisting method, program, and computer system for recurrence risk of colorectal cancer in various cases of colorectal cancer.

  As a result of intensive studies to solve the above problems, the present inventors have found that colorectal cancer can be classified into three types by cluster analysis. The present inventors have found that these three types are related to the prognosis of colorectal cancer and that the obtained results are sufficiently stable, thereby completing the present invention.

  According to the present invention, in a biological sample collected from a colorectal cancer patient, a plurality of genes selected from the first gene group present in the region from 18q21 to 18q23 on the long chromosome 18 chain, long chromosome 20 chain A plurality of genes selected from the second gene group existing in the region from 20q11 to 20q13 above, and ANGPTL2, AXL, C1R, C1S, CALHM2, CTSK, DCN, EMP3, GREM1, ITGAV, KLHL5, MMP2, RAB34 , SELM, SRGAP2P1, and a measurement process for measuring the expression levels of a plurality of genes selected from the third gene group including VIM, and recurrence of colorectal cancer in the patient based on the expression levels measured in the measurement process Determining the risk, and a method for assisting diagnosis of recurrence risk of colorectal cancer is provided.

  According to the present invention, it is possible to provide a highly reliable method for assisting diagnosis of colorectal cancer recurrence risk.

It is the schematic which showed an example of the diagnostic assistance apparatus used for the diagnostic assistance method of this invention. It is a block diagram which shows the function structure of the software of a diagnostic assistance apparatus. It is a block diagram which shows the structure of the hardware of a diagnostic assistance apparatus. It is an example of the flowchart which shows operation | movement of a diagnostic assistance apparatus. It is an example of the flowchart which shows operation | movement of a diagnostic assistance apparatus. It is a figure which shows the result of the recurrence risk group classification | category in the case of a training set. It is a Kaplan-Meier curve which shows the recurrence risk of each risk group. It is a figure which shows the result of the recurrence risk group classification | category in the case of a training set and the validation set 1. FIG. It is a figure which shows the result of the recurrence risk group classification | category in the case of the validation set 2. FIG. It is a figure which shows the result of the recurrence risk group classification | category in the case of the validation set 3. FIG. It is a Kaplan-Meier curve by the recurrence risk group classification in the case of validation set 3. It is a figure which shows the result of the recurrence risk group classification | category in the case of the validation set 4. FIG. It is a Kaplan-Meier curve by the recurrence risk group classification | category in the case of the validation set 4. FIG. It is a Kaplan-Meier curve by the Dukes classification in the case of a training set. It is a Kaplan-Meier curve of the recurrence risk classification result in which the middle risk group was stratified according to the presence or absence of KRAS gene mutation in Example 4. It is a Kaplan-Meier curve of the recurrence risk classification result in which the middle risk group is stratified according to the presence or absence of KRAS gene mutation in Example 5. It is a figure which shows the result of the recurrence risk group classification | category in 18 FFPE tissue specimens of Example 8. It is a Kaplan-Meier curve of the recurrence risk classification result in which the middle risk group in 18 FFPE tissue specimens of Example 8 was stratified according to the presence or absence of KRAS gene mutation. 2 is a Kaplan-Meier curve obtained in Example 10.

  In the diagnosis assistance method for recurrence risk of colorectal cancer of the present embodiment (hereinafter sometimes referred to as “diagnosis assistance method”), first, 18q21 on the long chain of chromosome 18 in a biological sample collected from a patient with colorectal cancer. A plurality of genes selected from the first gene group existing in the region from 20 to 18q23, a plurality of genes selected from the second gene group present in the region from 20q11 to 20q13 on the long chain of chromosome 20, and Measure the expression level of multiple genes selected from the third gene group including ANGPTL2, AXL, C1R, C1S, CALHM2, CTSK, DCN, EMP3, GREM1, ITGAV, KLHL5, MMP2, RAB34, SELM, SRGAP2P1 and VIM To do.

  The “biological sample” is not particularly limited as long as it contains a nucleic acid (eg, mRNA) derived from a tumor cell of a colorectal cancer patient. For example, a clinical sample can be used. Specific examples of clinical specimens include blood, serum, and tissues collected by surgery or biopsy. Further, a formalin-fixed paraffin-embedded (FFPE) sample of tissue collected from a subject may be used as a biological sample.

  The method of this embodiment may include a step of extracting DNA from a biological sample before the measurement step. A method for extracting DNA from a biological sample can be performed by a method known in the art. For example, a DNA extract can be obtained by centrifuging a biological sample to precipitate cells containing DNA, destroying the cells by a physical technique or a chemical technique, and removing cell debris. This operation can also be performed using a commercially available DNA extraction kit or the like.

  In the present specification, the “first gene group” is a general term for genes existing in the region from 18q21 to 18q23 on the long chain of chromosome 18. Specifically, the first gene group includes C18orf22 (chromosome 18 open reading frame 22), C18orf55 (chromosome 18 open reading frame 55), CCDC68 (coiled-coil domain containing 68), CNDP2 (CNDP dipeptidase 2). (metallopeptidase M20 family)), CYB5A (cytochrome b5 type A (microsomal)), LOC400657 (hypothetical LOC400657), LOC440498 (heat shock factor binding protein 1-like), MBD2 (methyl-CpG binding domain protein 2), MBP (myelin basic protein), MYO5B (myosin VB), NARS (asparaginyl-tRNA synthetase), PQLC1 (PQ loop repeat containing 1), RTTN (Rotatin), SEC11C (SEC11 homolog C (S. cerevisiae)), SOCS6 (suppressor of cytokine signaling 6), TNFRSF11A (tumor necrosis factor receptor superfamily, member 11a, NFKB activator), TXNL1 (thioredoxin-like 1), TXNL4A (thioredoxin-like 4A), VPS4B (vacuolar protein sorting 4 homolog B (S. cerevisiae)) and ZNF407 (Zinc finger protein 407) and table It is containing the gene.

  In the present specification, the “second gene group” is a general term for genes existing in the region from 20q11 to 20q13 on the long chain of chromosome 20. Specifically, the second gene group consists of ASXL1 (additional sex combs like 1 (Drosophila)), C20orf112 (chromosome 20 open reading frame 112), C20orf177 (chromosome 20 open reading frame 177), CHMP4B (chromatin) modifying protein 4B), COMMD7 (COMM domain containing 7), CPNE1 (copine I), DIDO1 (death inducer-obliterator 1), DNAJC5 (DnaJ (Hsp40) homolog, subfamily C, member 5), KIF3B (kinesin family member 3B) , NCOA6 (nuclear receptor coactivator 6), PHF20 (PHD finger protein 20), PIGU (phosphatidylinositol glycan anchor biosynthesis, class U), PLAGL2 (pleiomorphic adenoma gene-like 2), POFUT1 (protein O-fucosyltransferase 1), PPP1R3D (protein phosphatase 1, regulatory (inhibitor) subunit 3D), PTPN1 (protein tyrosine phosphatase, non-receptor type 1), RBM39 (RNA binding motif protein 39), TAF4 (TAF4 RNA polymerase II, TATA box binding protein (TBP) -associated factor , 135kDa) and TCFL 5 (transcription factor-like 5 (basic helix-loop-helix)) is included.

  In the present specification, the “third gene group” is a generic term for genes including those called biologically stroma-related genes, EMT-related genes, and the like. Specifically, the third gene group consists of ANGPTL2 (angiopoietin-like 2), AXL (AXL receptor tyrosine kinase), C1R (complement component 1, r subcomponent), C1S (complement component 1, s subcomponent), depending on the gene symbol. , CALHM2 (calcium homeostasis modulator 2), CTSK (cathepsin K), DCN (Decorin), EMP3 (epithelial membrane protein 3), GREM1 (gremlin 1, cysteine knot superfamily, homolog (Xenopus laevis)), ITGAV (integrin, alpha V (vitronectin receptor, alpha polypeptide, antigen CD51)), KLHL5 (kelch-like 5 (Drosophila)), MMP2 (matrix metallopeptidase 2 (gelatinase A, 72kDa gelatinase, 72kDa type IV collagenase)), RAB34 (RAB34, member RAS oncogene family ), SELM (selenoprotein M), SRGAP2P1 (SLIT-ROBO Rho GTPase activating protein 2 pseudogene 1) and VIM (Vimentin). In the present specification, the third gene group may be referred to as a “stroma-related gene group”.

  In the diagnosis assistance method of this embodiment, the risk of recurrence of colorectal cancer is determined using these three gene groups.

In the present specification, the “gene transcription product” is a product obtained by transcription of a gene, and is ribonucleic acid (RNA), specifically, messenger RNA (mRNA).
In the present specification, the “gene expression level” refers to the abundance of a transcription product of a gene in the biological sample or the amount of a substance reflecting the abundance. Therefore, in the diagnosis assisting method of this embodiment, the amount of a gene transcription product (mRNA) or the amount of complementary deoxyribonucleic acid (cDNA) or complementary RNA (cRNA) obtained from mRNA can be measured. Usually, since the amount of mRNA in a biological sample is very small, it is preferable to measure the amount of cDNA or cRNA obtained therefrom by reverse transcription and in vitro transcription (IVT).

  A method for extracting a gene transcription product from a biological sample can be performed using an RNA extraction method known in the art. For example, an RNA extract can be obtained by centrifuging a biological sample to precipitate cells containing RNA, destroying the cells by a physical method or an enzymatic method, and removing cell debris. RNA extraction can also be performed using a commercially available RNA extraction kit or the like.

  From the extract of the transcription product of the gene obtained as described above, it is preferable that the contaminated component derived from the biological sample is not mixed at the time of measuring the expression level of the gene, for example, when the biological sample is blood, A treatment for removing mRNA and the like can also be performed.

  About the extract of the transcription product of the gene obtained as mentioned above, the expression level of a plurality of genes, preferably at least 5 genes selected from each of the first to third gene groups is measured. By measuring the expression level of five or more genes, it is possible to reduce biological variations and measurement errors that occur when the expression of a given gene is accidentally high or low. Can assist in diagnosis of recurrence risk.

The gene expression level can be measured according to a method known per se, but in the method for assisting diagnosis of recurrence risk according to this embodiment, a measurement method using a nucleic acid chip, a method using a so-called microarray is preferable.
When measuring the expression level of a gene using a microarray, for example, a nucleic acid probe of about 20 to 25 mer immobilized on a substrate is used to extract a gene transcript or a cDNA or cRNA prepared from a gene transcript. The expression level of the target gene can be measured by contacting it and measuring the change in indicators such as fluorescence, color development, and current for the presence or absence of hybrid formation.
At least one nucleic acid probe may be used for one gene transcript, and a plurality of probes may be used depending on the length of the gene transcript. The sequence of the probe can be appropriately determined by those skilled in the art according to the sequence of the transcription product of the gene to be measured.
As a method for measuring the expression level of a gene using a nucleic acid chip, for example, a GeneChip system provided by Affymetrix can be used.

  When using a nucleic acid chip, the gene transcript or its cDNA or cRNA may be fragmented to facilitate hybridization with the nucleic acid probe. Fragmentation can be performed by a method known in the art, for example, using a nucleolytic enzyme such as ribonuclease or deoxyribonuclease.

  In the measurement step, the expression levels of a plurality of genes may be measured separately, or some genes or all genes may be measured simultaneously. For example, when a nucleic acid chip is used, the expression level of a plurality of genes can be measured simultaneously with a single nucleic acid chip.

  The gene transcription product or its cDNA or cRNA to be contacted with the nucleic acid probe in the nucleic acid chip is usually about 5 to 20 μg. The contact conditions are usually about 16 hours at 45 ° C.

The transcription product of the gene or its cDNA or cRNA that has hybridized with the nucleic acid probe, whether it is hybridized or not, and the amount of hybridization, the amount of current flowing on the nucleic acid chip due to the fluorescent substance, dye, or hybridization It is possible to detect based on the change of.
When the formation of a hybrid is measured by detecting a fluorescent substance or a dye, it is preferable that the gene transcription product or its cDNA or cRNA is labeled with a labeling substance for detecting the fluorescent substance or the dye. As such a labeling substance, those usually used in the art can be used. Usually, by mixing biotinylated nucleotides or biotinylated ribonucleotides as a nucleotide or ribonucleotide substrate when synthesizing cDNA or cRNA, the resulting cDNA or cRNA can be labeled with biotin. When the cDNA or cRNA is labeled with biotin, avidin or streptavidin, which is a binding partner for biotin, can bind on the nucleic acid chip. Hybrid formation can be detected by avidin or streptavidin binding to an appropriate fluorescent substance or dye. Examples of the fluorescent substance include fluorescein isothiocyanate (FITC), green fluorescent protein (GFP), luciferin, phycoerythrin, and the like. Usually, since a phycoerythrin-streptavidin conjugate is commercially available, it is convenient to use it.
Alternatively, a labeled antibody against avidin or streptavidin can be contacted with avidin or streptavidin to detect the fluorescent substance or dye of the labeled antibody.

The expression level of the gene obtained in this step is not particularly limited as long as it is a value that relatively represents the abundance of the transcription product of each gene in the biological sample. When measurement is performed using the nucleic acid chip, the expression level can be a signal obtained from the nucleic acid chip based on fluorescence intensity, color intensity, current amount, and the like.
These signals can be measured using a measuring device for a nucleic acid chip.

Next, in the determination step of the present embodiment, the recurrence risk of colorectal cancer is determined based on the gene expression level data obtained in the measurement step. Specifically, the recurrence risk of colorectal cancer is determined as follows.
When the third gene group is highly expressed, it is determined that the risk of recurrence is high.
When the first gene group is underexpressed and the third gene group is underexpressed, the risk of recurrence is determined to be moderate.
When the first gene group is highly expressed, the second gene group is highly expressed, and the third gene group is lowly expressed, the risk of recurrence is determined to be moderate.
When the first gene group is highly expressed, the second gene group is low expressed, and the third gene group is low expressed, it is determined that the risk of recurrence is low.

  In the determination step, various analysis methods can be used. For example, a method of performing correlation analysis (for example, correlation coefficient comparison and clustering) based on the gene expression pattern of the biological sample to be judged and the gene expression pattern of each patient group, expression of the gene of the biological sample to be judged Examples include a method of comparing the amount with a reference value.

  According to one preferred embodiment, the risk of recurrence is determined by performing a correlation analysis based on the gene expression pattern of the biological sample and the gene expression pattern of each patient group. In this method, a plurality of “gene groups” are not set, but a plurality of patient groups are set according to the risk of recurrence. Specifically, first, a group of patients determined to have a high risk of recurrence (hereinafter also referred to as a “high risk group”), a group of patients determined to have a moderate risk of recurrence (hereinafter referred to as “medium”). Risk group) and patient group judged to have low risk of recurrence (hereinafter also referred to as “low risk group”). Here, the above patient groups can be classified into three groups by clustering analysis or the like in advance. In the clustering analysis, the expression level of each gene group can be used. For example, a group in which the third gene group is highly expressed is regarded as a high risk group. A group in which the first gene group has a low expression and the third gene group has a low expression is regarded as a medium risk group. A group in which the first gene group is highly expressed, the second gene group is highly expressed, and the third gene group is lowly expressed is considered to be a medium risk group. When the first gene group is highly expressed, the second gene group is lowly expressed, and the third gene group is lowly expressed, it is regarded as a low risk group.

  The expression level of the gene to be analyzed is obtained from the samples of each patient group, and the average value is calculated. For example, when 100 patients are included in the high-risk group and the average expression level of C18orf22 is calculated, the value obtained by dividing the sum of the C18orf22 expression levels of 100 patients by 100 is C18orf22 of the high-risk group. It becomes the average value of the expression level. Similarly, the average value of the expression level of C18orf22 is also calculated in the medium risk group and the low risk group. In this embodiment, since a plurality of genes are to be analyzed, the average value of the expression levels of the plurality of genes is calculated. Here, the data set of the average expression level in the high risk group thus obtained is referred to as the high risk group expression pattern, and the data set of the average expression level in the medium risk group is referred to as the medium risk group expression pattern. The data set of the average expression level in the low risk group is called the low risk group expression pattern. When analyzing the expression level of 55 genes, the high risk group expression pattern includes 55 values.

  The expression pattern of each risk group is acquired in advance before measuring the gene expression of the biological sample and determining the risk of recurrence.

  Next, the expression level of each gene in the biological sample is measured. Here, the data set of the expression level of each gene measured in the measurement process is referred to as the expression pattern of the biological sample. When measuring the expression level of 55 genes, the expression pattern of the biological sample includes 55 values.

  The correlation between the expression pattern of each gene in the biological sample and the expression pattern of each risk group is analyzed. The risk group that shows the highest correlation with the expression pattern of the biological sample is identified. The recurrence risk corresponding to the identified risk group is determined as the recurrence risk of the biological sample. For example, when the expression level of each gene in the biological sample shows the highest correlation with the high risk group, it is determined that the biological sample has a high recurrence risk.

In the correlation analysis, various methods can be used.
In one preferred embodiment, for example, the correlation coefficient between the expression pattern of the biological sample and the high-risk group expression pattern, the correlation coefficient between the expression pattern of the biological sample and the medium-risk group expression pattern, and the expression pattern of the biological sample The correlation coefficient with the low risk group expression pattern is calculated. The interlaminar coefficients are compared, the biological samples are classified into risk groups that exhibit the highest correlation coefficient, and recurrence risk can be determined. For example, when the correlation coefficient with the high-risk group expression pattern is the highest, the biological sample is classified into the high-risk group, and the recurrence risk is determined to be high.

  The calculation of the correlation coefficient can be performed by a known method. For example, the correlation coefficient can be calculated based on Spearman's rank correlation, Pearson's product-moment correlation, Kendall's rank correlation, and the like.

In another preferred embodiment, as a method of analyzing the correlation with each risk group, a clustering analysis by a clustering method such as a closest distance method may be used. For example, the analysis can be performed as follows.
The expression level of each gene is acquired in advance in a plurality of patients (note that at this time, the high-risk group, medium-risk group, and low-risk group are not classified). In the measurement step, the expression level of each gene in the biological sample is measured. The expression level of each gene of each patient and the expression level of each gene in the biological sample are classified into a high risk group, a medium risk group, and a low risk group by clustering analysis. Based on the risk group into which the biological sample is classified, the recurrence risk of the biological sample can be determined.

  In addition to the analysis method described above, linear discrimination, discrimination using a support vector machine, or the like can also be used.

  In another embodiment, for example, the expression level of a gene selected from the first gene group in the biological sample is compared with a reference value, and the expression level of the gene selected from the second gene group in the biological sample is compared with the reference value. The risk of recurrence is determined based on the comparison and the comparison between the expression level of the gene selected from the third gene group in the biological sample and the reference value.

In this embodiment, in the determination step, the expression level of the gene selected from the third gene group is not less than the reference value of the gene group, regardless of the expression level of the gene selected from each of the first and second gene groups. If it is, it is determined that the risk of recurrence is high.
That is, when the expression level of the gene selected from the third gene group is equal to or higher than the reference value of the gene group, the expression level of the gene selected from each of the first and second gene groups is set to each of these gene groups. Even if the reference value is equal to or greater than the reference value or smaller than the reference value, the risk of recurrence is determined to be high.

In this embodiment, in the determination step, regardless of the expression level of the gene selected from the second gene group, the expression level of the gene selected from the third gene group is smaller than the reference value of the gene group, When the gene selected from one gene group is smaller than the reference value of the gene group, it is determined that the risk of recurrence is moderate.
That is, when the expression level of the gene selected from the third gene group is smaller than the reference value of the gene group, and the expression level of the gene selected from the first gene group is smaller than the expression level of the gene group For example, even if the expression level of the gene selected from the second gene group is equal to or higher than the reference value of the gene group or smaller than the reference value, it is determined that the risk of recurrence is moderate.

  In this embodiment, in the determination step, the expression level of the gene selected from the third gene group is smaller than the reference value of the gene group, and the expression level of the gene selected from the first gene group is When the expression level is equal to or higher than the reference value and the expression level of the gene selected from the second gene group is equal to or higher than the reference value of the gene group, the recurrence risk is determined to be moderate.

  In this embodiment, in the determination step, the expression level of the gene selected from the third gene group is smaller than the reference value of the gene group, and the expression level of the gene selected from the first gene group is When the expression level is equal to or higher than the reference value and the expression level of the gene selected from the second gene group is smaller than the reference value of the gene group, it is determined that the risk of recurrence is low.

  In the above embodiment, the “reference value” of each gene group is set to a value that can determine whether or not each gene group is overexpressed. For example, the “reference value” of the first gene group is acquired as follows. First, the average value of the expression level of each gene is calculated in a specific patient group. For example, the average value of the C18orf22 expression level of the patient group can be obtained by measuring the C18orf22 expression level of each patient included in the patient group and dividing the sum of the expression levels by the number of patients. Similarly, average values are obtained for the other genes included in the first gene group. By dividing the sum of the average values of these genes by the number of genes, the average value of the first gene group in a specific patient group can be obtained. This average value can be used as a “reference value”. Similarly, the “reference value” can be obtained for the second gene group and the third gene group.

  Here, the “average value” is exemplified as the reference value, but the median value or the mode value may be used instead of the average value.

  This reference value is preferably acquired in advance before the measurement process and the determination process.

  In a preferred embodiment of this embodiment, the total expression level of each gene in the biological sample is divided by the number of genes to obtain an average value of the gene expression level of the biological sample, and the gene expression level of the biological sample is calculated. The average value is compared with the reference value described above.

  In a further preferred embodiment of the present invention, the risk of recurrence is high when the KRAS gene has a mutation, and the risk of recurrence is low when there is no mutation in the KRAS gene. Is determined.

  KRAS gene is located at 25.36-25.4 Mb on chromosome 12 and is a kind of ras oncogene. It transmits epidermal growth factor receptor (EGFR) signal to nucleus and promotes cell proliferation. It is said that it has a function to do. The nucleotide sequence of KRAS cDNA is represented as SEQ ID NO: 56. This nucleotide sequence is known under the accession number AF493917 in the human genome database GenBank.

  KRAS gene mutation is preferably a mutation occurring in the base sequence of GGTGGC corresponding to the 12th and 13th codons (bases 34 to 39) present in the exon 2 sequence of the gene or the 61st position present in the exon 3 sequence. Refers to a mutation that occurs in the base sequence of CAA corresponding to the codons (bases 182-184).

  The method for measuring the presence or absence of KRAS mutation is not particularly limited, and can be performed using methods known to those skilled in the art. In the present embodiment, the measurement of the presence or absence of a KRAS mutation can be performed using sequence analysis.

  The type of mutation in the KRAS gene is not particularly limited, and if any base in the codon is mutated, it can be determined that there is a mutation. In the present embodiment, preferably, nucleotide sequence mutations that cause mutations in the amino acid sequence of the KRAS protein (that is, mutations other than silent mutations such as missense mutations, nonsense mutations, frameshift mutations, etc.) are targeted. As the type of mutation, nucleotide substitution, deletion, deletion and addition can be considered. In the present embodiment, substitution is preferable. Specific examples of such substitution include 34th G substitution with A, 35th G substitution with A, C or T, 38th G substitution with A, 182nd C substitution with A, Examples include substitution of 184th A with C or T.

  As described above, by adding the presence or absence of a KRAS mutation to the criteria, the medium risk group can be classified into a high risk group and a low risk group, and the whole can be classified into two groups, high and low. As a result, the medium risk group can be classified as either high or low, and more useful information can be provided for more cases.

  The present invention also includes a computer program product for causing a computer to determine the recurrence risk of colorectal cancer in a patient. Examples of the computer program product include a program that can be downloaded via the Internet, a medium that records the program, and the like.

For example, the program for making a computer perform the following processes is illustrated.
In a biological sample collected from a colorectal cancer patient, it receives the expression level of a plurality of genes selected from the first gene group existing in the region from 18q21 to 18q23 on the long chain of chromosome 18, and on the long chain of chromosome 20. Receiving the expression level of a plurality of genes selected from the second gene group present in the region from 20q11 to 20q13, and ANGPTL2, AXL, C1R, C1S, CALHM2, CTSK, DCN, EMP3, GREM1, ITGAV, KLHL5 Receiving the expression level of a plurality of genes selected from the third gene group including MMP2, RAB34, SELM, SRGAP2P1 and VIM;
A step of determining a recurrence risk of colorectal cancer in the patient based on the received expression level.

  Hereinafter, an embodiment of an apparatus suitable for carrying out the method of this embodiment will be described with reference to the drawings. However, the present invention is not limited only to this embodiment. FIG. 1 is a schematic diagram showing an example of a diagnostic assistance device used for determining the recurrence risk of colorectal cancer in a patient. A diagnostic auxiliary device 1 shown in FIG. 1 includes a measuring device 2 and a computer system 3 connected to the measuring device 2.

  In the present embodiment, the measuring device 2 is a measuring device for a nucleic acid chip. This measuring device 2 acquires information related to the expression level of the gene itself, such as the expression level of the gene itself and the hue and fluorescence intensity of the colored fluorescence of the nucleic acid chip. When a biological sample collected from a colorectal cancer patient is set in the measuring device 2, the measuring device 2 acquires information related to the gene expression level in the biological sample and transmits the obtained information to the computer system 3.

In the case of further determining whether the colorectal cancer recurrence risk is high or low for the specimen determined to be a medium risk, the diagnostic auxiliary device 1 further includes the mutation measuring device 4 in addition to the measuring device 2 and the computer system 3 connected to the measuring device 2. including.
In the present embodiment, the mutation measuring device 4 acquires information relating to the presence or absence of mutations in the KRAS gene in the biological sample. When a biological sample collected from a colorectal cancer patient is set in the mutation measuring device 4, the mutation measuring device 4 acquires information on the presence or absence of a KRAS gene mutation in the biological sample and transmits the obtained information to the computer system 3. To do.

  The computer system 3 includes a computer main body 3a, an input unit 3b composed of a keyboard and a mouse, and a display unit 3c composed of an LCD and a CRT for displaying sample information and determination results. The computer system 3 receives information relating to the expression level of the gene and information relating to the presence or absence of mutation of the KRAS gene as necessary from the measuring device 2 and the mutation measuring device 4. Then, the computer system 3 executes a program for determining the colorectal cancer recurrence risk of the subject based on such information. Note that “second group classification is necessary” can be input from the input unit 3b.

  FIG. 2 is a block diagram showing software of the computer main body 3a of the diagnostic auxiliary device 1 as functional blocks. As illustrated in FIG. 2, the computer includes a reception unit 301, a storage unit 302, a calculation unit 303, a determination unit 304, and an output unit 305. The receiving unit 301 is communicably connected to the measuring device 2 and, if necessary, the mutation measuring device 4 via a network. The determination unit 304 measures the information necessary for carrying out the colorectal cancer recurrence risk determination via the input unit 3b, specifically, the presence or absence of mutation in the KRAS gene (classification of two groups) for the sample determined to be medium risk. Whether or not can be entered.

  The receiving unit 301 receives information transmitted from the measuring device 2 and the mutation measuring device 4. The storage unit 302 stores a reference value necessary for determination, an expression for calculating the gene expression level, a processing program, and the like. The calculating unit 303 uses the information acquired by the receiving unit 301 to calculate the gene expression level according to the stored formula. The determination unit 304 determines whether the gene expression level acquired by the reception unit 301 or calculated by the calculation unit 303 is equal to or greater than a reference value stored in the storage unit 302. The output unit 305 outputs the determination result of the determination unit 304 to the display unit 3c as the determination result of the colorectal cancer recurrence risk of the subject.

  When further determining whether the colorectal cancer recurrence risk is high or low for the specimen determined to be a medium risk, the receiving unit 301 further acquires information transmitted from the mutation measuring device 4 in addition to the information transmitted from the measuring device 2. . The storage unit 302 further stores a non-mutated sequence of the KRAS gene in addition to a reference value necessary for determination and an expression for calculating the gene expression level. The calculating unit 303 uses the information acquired by the receiving unit 301 to calculate the gene expression level according to the stored formula. The determination unit 304 determines whether the gene expression level acquired by the reception unit 301 or calculated by the calculation unit 303 is equal to or greater than a reference value stored in the storage unit 302. The presence or absence of a mutation in the KRAS gene is determined based on whether or not the KRAS gene sequence acquired by the receiving unit 301 matches the non-mutated sequence of the KRAS gene stored in the storage unit 302. The output unit 305 outputs the determination result of the determination unit 304 to the display unit 3c as the determination result of the colorectal cancer recurrence risk of the subject.

  FIG. 3 is a block diagram showing a hardware configuration of the computer main body 3a shown in FIG. As shown in FIG. 3, the computer main body 3a includes a CPU (Central Processing Unit) 30, a ROM (Read Only Memory) 31, a RAM 32, a hard disk 33, an input / output interface 34, a reading device 35, and a communication. An interface 36 and an image output interface 37 are provided. A CPU 30, a ROM 31, a RAM (Random Access Memory) 32, a hard disk 33, an input / output interface 34, a reading device 35, a communication interface 36 and an image output interface 37 are connected by a bus 38 so that data communication is possible.

  The CPU 30 can execute a computer program stored in the ROM 31 and a computer program loaded in the RAM 32. Each function shown in FIG. 2 is executed by the CPU 30 executing the computer program. Thereby, the computer system 3 functions as a diagnosis assisting device for determining a subject's risk of recurrence of colorectal cancer.

  The ROM 31 is configured by a mask ROM, PROM, EPROM, EEPROM, or the like. The ROM 31 stores a computer program executed by the CPU 30 and data used therefor as described above.

  The RAM 32 is configured by SRAM, DRAM, or the like. The RAM 32 is used for reading out computer programs recorded in the ROM 31 and the hard disk 33. The RAM 32 is also used as a work area for the CPU 30 when executing these computer programs.

  The hard disk 33 is installed with an operating system to be executed by the CPU 30, a computer program such as an application program (a computer program for determining a recurrence risk of colorectal cancer in a subject), and data used for executing the computer program. .

  The reading device 35 includes a flexible disk drive, a CD-ROM drive, a DVD-ROM drive, and the like. The reading device 35 can read the computer program or data recorded on the portable recording medium 40.

  The input / output interface 34 includes, for example, a serial interface such as USB, IEEE1394, RS-232C, a parallel interface such as SCSI, IDE, IEEE1284, and an analog interface including a D / A converter, an A / D converter, and the like. It is configured. An input unit 3 b such as a keyboard and a mouse is connected to the input / output interface 34. The operator can input various commands to the computer main body 3a through the input unit 3b.

  The communication interface 36 is, for example, an Ethernet (registered trademark) interface. The computer main body 3a can also transmit print data to a printer or the like via the communication interface 36.

  The image output interface 37 is connected to a display unit 3c configured with an LCD, a CRT, or the like. Thereby, the display part 3c can output the video signal according to the image data given from CPU30. The display unit 3c displays an image (screen) according to the input video signal.

  Next, the procedure for determining the recurrence risk of the subject's colorectal cancer by the diagnosis assisting apparatus 1 will be described. FIG. 4 is a flowchart of risk determination for recurrence of colorectal cancer. Here, the fluorescence intensity is calculated from the color fluorescence information obtained using the biological sample derived from the subject, the gene expression level is calculated from the obtained fluorescence intensity, and the obtained expression level is equal to or higher than the reference value. The case where it is determined whether or not will be described as an example. However, the present invention is not limited only to this embodiment.

  First, in step S <b> 1-1, the receiving unit 301 of the diagnostic auxiliary device 1 acquires information on the color fluorescence associated with the expression level of the gene selected from the third gene group from the measurement device 2. Next, in step S <b> 1-2, the calculation unit 303 calculates fluorescence intensity from the acquired information and transmits it to the storage unit 302. In step S <b> 1-3, the calculation unit 303 calculates the expression level of the gene according to the stored formula based on the stored fluorescence intensity.

  Thereafter, in step S1-4, the determination unit 304 determines whether or not the expression level calculated in step S1-3 is greater than or equal to the reference value stored in the storage unit 302. Here, when the expression level is equal to or higher than the reference value, the routine proceeds to step S1-5, and the determination unit 304 outputs a determination result indicating that the subject has a high risk of recurrence of colorectal cancer (high risk). Send to. On the other hand, when the expression level is lower than the reference value, the routine proceeds to step S1-6.

  In step S1-6, the receiving unit 301 of the diagnostic auxiliary device 1 acquires information on the color fluorescence associated with the expression level of the gene selected from the first gene group from the measurement device 2. Next, in step S <b> 1-7, the calculation unit 303 calculates the fluorescence intensity from the acquired information and transmits it to the storage unit 302. In step S1-8, the calculation unit 303 calculates the gene expression level according to the stored formula based on the stored fluorescence intensity.

  Thereafter, in step S <b> 1-9, the determination unit 304 determines whether or not the expression level calculated by the calculation unit 303 is greater than or equal to the reference value stored in the storage unit 302. Here, when the expression level is greater than or equal to the reference value, the routine proceeds to step S1-11. When the expression level is lower than the reference value, the routine proceeds to step S1-10, and the determination unit 304 determines that the colorectal cancer recurrence risk of the subject is intermediate (medium risk), and then the routine Proceed to step S1-17.

  In step S1-11, the receiving unit 301 of the diagnostic auxiliary device 1 acquires information on the color fluorescence associated with the expression level of the gene selected from the second gene group from the measurement device 2. Next, in step S1-12, the calculation unit 303 calculates the fluorescence intensity from the acquired information and transmits it to the storage unit 302. In step S1-13, the calculation unit 303 calculates the expression level of the gene according to the stored formula based on the stored fluorescence intensity.

  Thereafter, in step S <b> 1-14, the determination unit 304 determines whether the expression level calculated by the calculation unit 303 is greater than or equal to the reference value stored in the storage unit 302. Here, when the expression level is equal to or higher than the reference value, the routine proceeds to step S1-15, and the determination unit 304 determines that the subject's colorectal cancer recurrence risk is moderate (medium risk), and then the step Proceed to S1-17. On the other hand, when the expression level is lower than the reference value in step S1-14, the routine proceeds to step S1-16, and the determination unit 304 determines that the subject has a low risk of recurrence of colorectal cancer (low risk). ) To the output unit 305.

  For a sample that has been determined to have a moderate colorectal cancer recurrence risk through step S1-10 or S1-15, in step S1-17, “requires 2-group classification” is input from the input unit 3b. In these specimens, the risk of colorectal cancer recurrence is determined by measuring the KRAS gene mutation.

  When “2 group classification is necessary” is not input, the routine proceeds to step S1-18, and transmits a determination result indicating that the subject's risk of recurrence of colorectal cancer is moderate to the output unit 305.

  On the other hand, if the second group classification is necessary, the routine proceeds to step S1-19. In step S1-19, a process for determining whether the colorectal cancer recurrence risk is high or low based on the presence or absence of a mutation in the KRAS gene is performed for the specimen determined to be medium risk. The mutation measuring device 4 is used for this processing.

  In step S1-19, the receiving unit 301 acquires sequence information of the KRAS gene of the subject determined to be a medium risk. Next, in step S1-20, the determination unit 304 compares the acquired sequence of the KRAS gene with the non-mutated sequence of the KRAS gene stored in the storage unit 302, and determines the KRAS in the biological sample of the subject. Determine if there is a mutation in the gene. If there is a mutation in the KRAS gene, the routine proceeds to step S1-21, and the determination unit 304 transmits a determination result indicating that the subject has a high risk of recurrence of colorectal cancer (high risk) to the output unit 305. . On the other hand, if there is no mutation in the KRAS gene, the routine proceeds to step S1-22, and the determination unit 304 outputs a determination result indicating that the subject has a low risk of recurrence of colorectal cancer (low risk) to the output unit 305. Send.

  In step S1-23, the output unit 305 outputs the determination result of the colorectal cancer recurrence risk of the subject and displays the determination result on the display unit 3c. Thereby, the diagnostic assistance apparatus 1 can provide a doctor or the like with information that assists in determining whether the subject has a high, intermediate, or low risk of recurrence of colorectal cancer.

  According to another embodiment, the recurrence risk can also be determined by calculating a correlation coefficient using the diagnostic auxiliary device shown in FIG. A processing flow in this case will be described with reference to FIG. In addition, the memory | storage part of this apparatus has memorize | stored the high risk group expression pattern, the medium risk group expression pattern, and the low risk group expression pattern previously.

  In step S <b> 2-1, the reception unit 301 of the diagnostic assistance device 1 acquires fluorescence information indicating the expression level of each gene in the biological sample from the measurement device 2. Next, in step S <b> 2-2, the calculation unit 303 calculates fluorescence intensity from the acquired information and transmits the fluorescence intensity to the storage unit 302. In step S2-3, the calculation unit 303 calculates the expression level of each gene based on the stored fluorescence intensity (here, the expression pattern of the biological sample is acquired). Thereafter, in step S2-4, the determination unit 304 reads the high risk group expression pattern, the medium risk group expression pattern, and the low risk group expression pattern stored in the storage unit 302, and acquires them in step S2-3. Based on the expression pattern of the biological sample, the correlation coefficient between the expression pattern of the biological sample and the high-risk group expression pattern (hereinafter also referred to as “correlation coefficient H”), the expression pattern of the biological sample, and the medium-risk group expression pattern And a correlation coefficient (hereinafter also referred to as “correlation coefficient L”) between the expression pattern of the biological sample and the low risk group expression pattern (hereinafter also referred to as “correlation coefficient L”). .

  In step S2-5, it is determined whether or not the correlation coefficient H is the highest. That is, when the correlation coefficient H is higher than the correlation coefficient M and the correlation coefficient H is higher than the correlation coefficient L, it is determined that the correlation coefficient H is the highest. When the correlation coefficient H is the highest, the biological sample is classified into a high risk group in step S2-6, and it is determined that the recurrence risk of the biological sample is high.

  If it is determined in step S2-5 that the correlation coefficient H is not the highest correlation coefficient, it is determined in step S2-7 whether the correlation coefficient M is the highest. That is, when the correlation coefficient M is higher than the correlation coefficient H and the correlation coefficient M is higher than the correlation coefficient L, it is determined that the correlation coefficient M is the highest. When the correlation coefficient M is the highest, the biological sample is classified into a medium risk group in step S2-8, and the recurrence risk of the biological sample is determined to be moderate.

  If it is determined in step S2-7 that the correlation coefficient M is not the highest correlation coefficient, it is determined in step S2-9 that the correlation coefficient L is the highest. If the relationship number L is the highest, the biological sample is classified into the low risk group in step S2-9, and it is determined that the recurrence risk of the biological sample is low.

  In step S2-10, the output unit 305 outputs the determination result of the colorectal cancer recurrence risk of the subject and displays it on the display unit 3c. Thereby, the diagnostic assistance apparatus 1 can provide a doctor or the like with information that assists in determining whether the subject has a high, intermediate, or low risk of recurrence of colorectal cancer.

  Further, the flowchart of FIG. 5 may include a step of determining whether or not the correlation coefficient L is the highest instead of the step of determining whether or not the correlation coefficient M is the highest. Further, instead of the step of determining whether or not the correlation coefficient H is the highest, a step of determining whether or not the correlation coefficient L is the highest may be included. In any case, it can be determined which of the correlation coefficients H, M, and L is the highest. In any case, the execution order of the determination steps is not limited.

  In a more preferred embodiment, the presence or absence of a KRAS gene mutation may be further measured for the specimen determined to be in recurrence risk in step S2-8 in the flowchart of FIG. The flowchart for performing such two-group classification is the same as that shown in steps S1-17 to S1-22 in FIG. 4, for example. These steps are, for example, those in step S2-8 in the flowchart in FIG. Done later.

  The present invention also includes a system suitable for determining the risk of recurrence of colorectal cancer in a subject.

The storage unit 302 stores a computer program for causing the computer system 3 to execute the following steps:
In a biological sample collected from a colorectal cancer patient, it receives the expression level of a plurality of genes selected from the first gene group existing in the region from 18q21 to 18q23 on the long chain of chromosome 18, and on the long chain of chromosome 20. Receiving the expression level of a plurality of genes selected from the second gene group present in the region from 20q11 to 20q13, and ANGPTL2, AXL, C1R, C1S, CALHM2, CTSK, DCN, EMP3, GREM1, ITGAV, KLHL5 Receiving the expression level of a plurality of genes selected from the third gene group including MMP2, RAB34, SELM, SRGAP2P1 and VIM;
A step of determining a recurrence risk of colorectal cancer in the patient based on the received expression level.

  In the method of the present embodiment, the colorectal cancer recurrence risk of the subject is determined based on the analysis result obtained in the above analysis step. For example, it is possible to provide a determination result that the colorectal cancer of the subject is highly likely to recur, such a possibility is moderate, or such a possibility is low. By providing the determination result to a doctor or the like, diagnosis by the doctor or the like about the possibility of recurrence of colorectal cancer is assisted.

Example 1: Examination of classification according to prognosis of colorectal cancer patients
Among the Affymetrix GeneChip, Human Genome U133 plus 2.0 Array data set GSE14333 (obtained from NCBI Gene Expression Omnibus (URL; http://www.ncbi.nlm.nih.gov/geo/)), colorectal cancer (colon cancer) ) 72 patients were used as training set. Analysis software includes array data analysis software (Expression Console v1.1 (Affymetrix)), spreadsheet software (Office Excel 2002, 2007 (Microsoft)), cluster analysis software (Cluster3.0, Java ( (Trademark) Treeview (obtained from http://bonsai.hgc.jp/~mdehoon/software/cluster/software.htm)) and statistical analysis software (MedCalc (MedCalc))) It was.
MAS5 was used for data normalization. Among all probes on GeneChip, probes with unknown gene symbols and probes with an average expression signal value of less than 300 were excluded from the analysis. For probes with corresponding gene duplication, the probe with the maximum average expression signal value was represented, and the rest were excluded. After Z-transforming the signal values, unsupervised hierarchical clustering was performed using the closest distance method. The similarity measure was Pearson correlation coefficient.
Based on the results of cluster analysis, gene clusters that are presumed to satisfy the following two conditions: (1) reflecting important biological functions and (2) contributing to the generation of characteristic case clusters are defined as functional modules. A recurrence risk group classification method was constructed by extracting and repeating clustering by combination of functional modules.

  FIG. 6 shows the results of recurrence risk group classification in the training set cases. Hereinafter, the increase / decrease in the expression level is determined based on the average expression level of the gene in all patient cases. For example, when the expression level of a certain gene is equal to or higher than the above average value, it is determined that the relative expression level is increased, and when it is smaller than the above average value, it is determined that the relative expression level is decreased. As shown in FIG. 6, a decrease in the relative expression level of a gene group on the long chain of chromosome 18 (hereinafter sometimes referred to as “first gene group” or “18q Loss module”) from the training set, In addition, a case showing a relative increase in the expression level of a gene group on the long chain of chromosome 20 (hereinafter sometimes referred to as “second gene group” or “20q Amp module”) is extracted, and this is type B Defined. Cases in which the expression pattern of the 18q Loss module and the 20q Amp module was reversed from type B were extracted from the training set and defined as type A. In the training set, cases characterized by strong expression of the stroma-related gene group appeared regardless of the expression levels of the genes in type A and type B, and these cases were defined as independent type C. The genes constituting the three functional modules used are shown in Table 1.

  Table 2 shows the number of cases (existence ratio) for each type classified as described above and the recurrence rate of colorectal cancer.

  Among all 72 cases, 22 cases were classified as type A, 24 cases were classified as type B, and 26 cases were classified as type C. The recurrence rate of colorectal cancer was 4.5% in type A, 12.5% in type B, and 23.1% in type C.

FIG. 7 shows Kaplan-Meier curves created for each classified type. As shown in FIG. 7, it was found that there was a large difference in the recurrence-free survival rate after surgery in each type.
From the results shown in Table 2 and FIG. 7, it can be seen that type A can be defined as a low risk group with low recurrence risk, type B as a medium risk group with moderate recurrence risk, and type C as a high risk group with high recurrence risk. It was. Hereinafter, the low risk group, the medium risk group, and the high risk group may be collectively referred to as a recurrence risk group.

Example 2: Verification of reliability of recurrence risk group classification 1
Affymetrix GeneChip, Human Genome U133 plus 2.0 Array data set GSE14333 (obtained from NCBI Gene Expression Omnibus (URL; http://www.ncbi.nlm.nih.gov/geo/)) 74 patients who did not exist were used as validation set 1. Note that the case of the training set and the case of the validation set 1 are selected to be specimens acquired at different medical facilities.
Clustering was performed on the 146 cases obtained by adding 74 cases of validation set 1 to 72 cases of training set using the genes shown in Table 1 in the same manner as in Example 1.

  FIG. 8 shows the results of the recurrence risk group classification in the training set and validation set 1 cases. As shown in FIG. 8, the training set and the validation set 1 are different in the facility from which the biological sample was obtained, but all cases are one of the three recurrence risk groups without forming a cluster derived from the difference in the facility. It was found that it was classified.

Example 3: Verification of reliability of recurrence risk group classification 2
Affymetrix GeneChip, Human Genome U133 plus 2.0 Array Data Set GSE18088 (from NCBI Gene Expression Omnibus (URL; http://www.ncbi.nlm.nih.gov/geo/) for 53 patients with colorectal cancer (colon cancer) Obtained) was used as validation set 2. The 53 cases were clustered using the genes in Table 1 in the same manner as in Example 1.

In FIG. 9, the result of the recurrence risk group classification | category in the case of the validation set 2 is shown. As shown in FIG. 9, the validation set 2 is different from the training set in the facility where the biological sample was obtained and the facility where the GeneChip measurement was performed, but all cases in the validation set 2 are one of the three recurrence risk groups. It was found that it was classified.
Table 3 shows the number of cases (existence ratio) for each type classified as described above and the recurrence rate of colorectal cancer.

  Of the 53 cases, 23 cases were classified into the low-risk group, 25 cases were classified into the medium-risk group, and 5 cases were classified into the high-risk group. The recurrence rate of colorectal cancer was 8.7% in the low-risk group, 28.0% in the medium-risk group, and 80.0% in the high-risk group. From the results in Table 3, validation set 2 differs from the training set in the facility where the biological sample was obtained and the facility where GeneChip measurement was performed, but each relapse risk group was not affected by the difference in the facility. It turned out that the result similar to 1 is shown.

Example 4: Verification of reliability of recurrence risk group classification 3
Affymetrix GeneChip, Human Genome U133 plus 2.0 Array data set GSE39582 (from NCBI Gene Expression Omnibus (URL; http://www.ncbi.nlm.nih.gov/geo/) for 258 patients with colorectal cancer (colon cancer) Obtained) was used as validation set 3. The 256 cases were clustered using the genes in Table 1 in the same manner as in Example 1.

In FIG. 10, the result of the recurrence risk group classification | category in the case of the validation set 3 is shown. As shown in FIG. 10, the validation set 3 is different from the training set in the facility where the biological sample was obtained and the facility where the GeneChip measurement was performed, but all cases in the validation set 3 are one of the three recurrence risk groups. It was found that it was classified.
Table 4 shows the number of cases (existence ratio) for each type classified as described above and the recurrence rate of colorectal cancer.

  Of the 258 cases, 74 cases were classified into the low risk group, 123 cases were classified into the medium risk group, and 61 cases were classified into the high risk group. The recurrence rate of colorectal cancer was 12.2% in the low-risk group, 23.6% in the medium-risk group, and 39.3% in the high-risk group.

FIG. 11 shows Kaplan-Meier curves created for each classified type. As shown in FIG. 11, it was found that there was a large difference in the recurrence-free survival rate after surgery in each type.
From the results in Table 4 and FIG. 11, validation set 3 is different from the training set in the facility where the biological sample was obtained and the facility where GeneChip measurement was performed, but each recurrence risk group was not affected by the difference in the facility. It was found that the same results as in Example 1 were shown.

Example 5: Verification of reliability of recurrence risk group classification 4
As validation set 4, tissues were collected from 85 cases of colon cancer (colon cancer) patients and stored frozen. Expression analysis was performed using Affymetrix's GeneChip, Human Genome U133 plus 2.0 Array using 85 cryopreserved tissues. The 85 samples were clustered using the genes in Table 1 in the same manner as in Example 1.

In FIG. 12, the result of the recurrence risk group classification | category in the case of the validation set 4 is shown. As shown in FIG. 12, the validation set 4 is different from the training set in the facility where the biological sample was obtained and the facility where the GeneChip measurement was performed, but all cases in the validation set 4 are one of the three recurrence risk groups. It was found that it was classified.
Table 5 shows the number of cases (existence ratio) for each type classified as described above and the recurrence rate of colorectal cancer.

  Of the 85 cases, 23 cases were classified into the low risk group, 26 cases were classified into the medium risk group, and 36 cases were classified into the high risk group. The recurrence rate of colorectal cancer was 0% in the low-risk group, 11.5% in the medium-risk group, and 22.2% in the high-risk group.

FIG. 13 shows Kaplan-Meier curves created for each classified type. As shown in FIG. 13, it was found that there was a large difference in the recurrence-free survival rate after surgery in each type.
From the results in Table 5, it was found that validation set 4 also showed the same results as in Example 1.

  As described above, cases of colorectal cancer could be classified into three recurrence risk groups by functional module analysis. Each recurrence risk group had a different recurrence risk. In addition, from the results of Examples 1 to 5, it was found that the classification of the recurrence risk group is a highly reliable classification method that is not affected by the source of the data set. Therefore, it was shown that a sufficiently stable and reliable result can be obtained by the method for assisting diagnosis of recurrence risk using the recurrence risk group classification of colorectal cancer of this embodiment.

Comparative Example: Prognosis Prediction by Conventional Method (Dukes Classification) FIG. 14 shows the results of survival time analysis by Dukes classification for 72 cases in the training set as a comparative control of prognostic prediction performance. In FIG. 14, Dukes A shows a state where the cancer stays in the large intestine wall, Dukes B shows a state where the cancer penetrates the large intestine wall but no lymph node metastasis, and Dukes C shows a lymph node metastasis. Indicates a state with

  As shown in FIG. 2 and FIG. 14, 26 cases were determined to be high risk by the diagnosis support method of the present embodiment, whereas it was determined to be high risk (Dukes C) by the determination method of the comparative example. There were 15 cases. In addition, there is almost no difference in the recurrence-free survival rate between the case determined to be Dukes A by the determination method of the comparative example and the case determined to be Dukes B, whereas the low risk group by the diagnosis assisting method of the present embodiment There was a difference in the recurrence-free survival rate between cases determined to be in the middle risk group and cases determined to be in the medium risk group. From these results, it was suggested that the recurrence risk diagnosis assisting method of the present embodiment can determine the recurrence risk more accurately than the conventional pathological classification.

Example 6: Improvement of prognosis prediction performance by stratification of medium risk group due to KRAS gene mutation 1
Samples having the KRAS gene mutation among the samples in the analysis result performed in Example 4 were classified as high risk, samples having no KRAS gene mutation were defined as low risk, and all samples were divided into two groups (FIG. 15). reference). Specifically, the presence or absence of a KRAS mutation in the DNA of the specimen was measured as described below, and all specimens were divided into two groups based on the results.
First, a PCR master mix having the composition shown in Table 6 below was prepared.

  Next, 10 ng of genomic DNA was dispensed into a 0.5 ml PCR tube, and Nuclease free water was added to a total volume of 20 μL. Then, 4.8 μl / tube of PCR master mix was added and mixed. The primers added to PCR Master Mix are those shown in Table 7 below. To amplify the region containing the 12th and 13th codons of exon 2 of the KRAS gene, use the primer pair of SEQ ID NOs: 57 and 58, and to amplify the region containing the 61st codon of exon 3, use the primer pair of SEQ ID NOs: 59 and 60 Using.

  The obtained PCR master mix was set in the Thermal Cycler and the following program was executed to amplify the KRAS exon 2 and exon 3 sequences by PCR. KRAS exon 295 ℃: 10min → (94 ℃: 1min → 55 ℃: 1min → 72 ℃: 1min) x 38 cycle → 72 ℃: 10min → 4 ℃ holdKRAS exon 395 ℃: 10min → (94 ℃: 1min → 63 ℃: 1min → 72 ℃: 1min) x 38 cycle → 72 ℃: 10min → 4 ℃ hold

  After amplification, 1% agarose gel electrophoresis was performed to confirm a single band. Thereafter, 5 μl of PCR product was dispensed into a 0.5 ml PCR tube, 2 μl of ExoSAP-IT was added and mixed, set in the Thermal Cycler, and the following program was executed. 37 ℃ 15min → 80 ℃ 15min → 4 ℃ hold

  9.6 μl of 1 pmol / μl primer (F or R) and 9.4 μl of NFW were added to 2 μl of the product and mixed. Sequence analysis was commissioned to Operon. The nucleotide sequence to be analyzed was compared with the nucleotide sequence of SEQ ID NO: 1. If even one mutation was found, the KRAS gene was mutated. Regarding samples determined to be in the medium risk group in Example 4, samples in which a mutation was found in the KRAS gene were classified into a high risk group, and samples in which no mutation was found in a KRAS gene were classified into a low risk group.

  The results are shown in FIG. 15 for Kaplan-Meier curves for each group. Comparison of FIG. 15 and FIG. 11 shows that by adding the presence or absence of KRAS gene mutation to the criteria, cases in the medium risk group can be classified into two risk groups with high and low relapse-free survival rates. It was.

Example 7: Improvement of prognosis prediction performance by stratification of medium risk group due to KRAS gene mutation 2
Of the samples in the analysis results performed in Example 5, the samples having the KRAS gene mutation were classified as high risk, the samples having no KRAS gene mutation were defined as low risk, and all samples were divided into two groups.
The results are shown in FIG. 16 for Kaplan-Meier curves for each group. Comparison of FIG. 16 and FIG. 13 shows that by adding the presence or absence of KRAS gene mutation to the criteria, cases in the medium risk group can be classified into two risk groups with high and low recurrence-free survival rates. It was.

Example 8: Verification using formalin-fixed paraffin-embedded (FFPE) tissue FFPE tissue samples were prepared from 18 of the 85 cryopreserved tissue samples used in Example 5. These 18 specimens were used to classify recurrence risk groups. More specifically, first, total RNA was extracted from the FFPE tissue specimen using RNAeasy FFPE kit (QIAGEN). Nucleic acid chip pretreatment was performed using Sensation Plus FFPE Amplification and 3 'IVT Labeling Kit (Affymetrix). Gene chip measurement was performed using the total RNA obtained above. In the same manner as in Example 1, clustering was performed for the genes in Table 1.

  FIG. 17 shows the results of recurrence risk group classification obtained for the 18 FFPE tissue specimens. As shown in FIG. 17, even when FFPE tissue specimens were used, it was found that colon cancer cases could be classified into three recurrence risk groups based on the gene expression levels in Table 1. Table 8 shows the number of cases (existence ratio) and the recurrence rate of colorectal cancer for each type classified for 18 FFPE tissue specimens.

  Of the 18 cases, 4 cases were classified into the low-risk group, 6 cases were classified into the medium-risk group, and 8 cases were classified into the high-risk group. The recurrence rate of colorectal cancer was 0% in the low-risk group, 0% in the medium-risk group, and 37.5% in the high-risk group. These results show that cases of colorectal cancer can be classified with high accuracy even when FFPE tissue specimens are used.

For the above FFPE tissue samples, KRAS gene mutations in 6 cases classified into the medium risk group were measured. As a result, in all 6 cases, the KRAS gene mutation was negative and could be classified into the low-risk group.
FIG. 18 shows a Kaplan-Meier curve of each group when the recurrence risk group classification performed in Examples 6 and 7 was performed on the FFPE tissue specimen. As shown in FIG. 18, even when FFPE tissue specimens were used, it was found that there was a large difference in the recurrence-free survival rate after surgery in each type.

From the results shown in FIG. 17 and Table 8, it was found that cases of colorectal cancer can be classified into recurrence risk groups as in Example 1 using FFPE tissue specimens. Further, from the results of FIG. 18, as in Examples 6 and 7, it was found that the accuracy of relapse risk group classification can be further increased based on the presence or absence of KRAS gene mutation even when FFPE tissue specimens are used.
Table 9 below shows a correlation table between the determination result of the cryopreserved tissue sample and the determination result of the FFPE tissue sample.

  The coincidence rate was very high at 83.3%. From this result, it was found that the risk of recurrence can be determined even when FFPE tissue specimens are used, as in the case of using cryopreserved tissue specimens.

Example 9: Recurrence risk determination method using correlation coefficient In the low risk group, medium risk group and high risk group classified in Example 1, the expression levels of 55 genes shown in Table 1 were measured. Based on this expression level, a low risk group expression pattern, a medium risk group expression pattern, and a high risk group expression pattern were obtained. Each expression pattern includes an average value of each gene.

  As the sample, the same sample as in Example 4 was used. The expression level of 55 genes shown in Table 1 was measured for each specimen. Based on this expression level, the expression pattern of each specimen was obtained.

  The correlation coefficient between the expression pattern of the specimen and the expression pattern of each risk group was calculated based on Spearman's rank correlation. The risk group showing the highest correlation coefficient for each specimen was identified.

  Table 10 shows the match rate between the results of Example 4 (risk classification by clustering analysis) and the results of Example 9.

  As shown in Table 10, the concordance rate between the results of Example 9 and the results of Example 4 was 83%. From this result, it was found that the risk of recurrence of the specimen can be determined even when the correlation coefficient is used.

Example 10: Determination of recurrence risk using KRAS gene mutation The presence or absence of a KRAS gene mutation was detected in the patient group in which the recurrence risk was determined to be moderate in Example 9. Among the medium risk groups, samples with KRAS gene mutation were classified as high recurrence risk, and samples without KRAS gene mutation were classified as recurrence risk low.

  Table 11 shows the concordance rate between the results of Example 6 and the results of Example 10.

  As shown in Table 11, the concordance rate between the results of Example 10 and the results of Example 6 was 85%. From this result, it was found that the risk of recurrence of the specimen can be determined even when the correlation coefficient is used.

  FIG. 19 shows a Kaplan-Meier curve created from the results of Example 10. As shown in FIG. 19, by adding the presence or absence of KRAS gene mutation to the criteria, each specimen could be classified into two risk groups with greatly different recurrence-free survival rates.

DESCRIPTION OF SYMBOLS 1 Diagnosis assistance apparatus 2 Measuring apparatus 3 Computer system 3a Computer main body 3b Input part 3c Display part 4 Mutation measuring apparatus 30 CPU
31 ROM
32 RAM
33 Hard Disk 34 Input / Output Interface 35 Reading Device 36 Communication Interface 37 Image Output Interface 38 Bus 40 Recording Medium 301 Receiving Unit 302 Storage Unit 303 Calculation Unit 304 Determination Unit 305 Output Unit

Claims (15)

In a biological sample collected from a colorectal cancer patient, a plurality of genes selected from the first gene group existing in the region from 18q21 to 18q23 on chromosome 18 long chain, from 20q11 to 20q13 on chromosome 20 long chain A plurality of genes selected from the second gene group existing in the region of, and ANGPTL2, AXL, C1R, C1S, CALHM2, CTSK, DCN, EMP3, GREM1, ITGAV, KLHL5, MMP2, RAB34, SELM, SRGAP2P1 and VIM A measurement step of measuring the expression levels of a plurality of genes selected from the third gene group including:
Determining the recurrence risk of colorectal cancer of the patient based on the expression level measured in the measurement step;
A method for assisting diagnosis of recurrence risk of colorectal cancer. In the determination step,
When the third gene group is highly expressed, it is determined that the risk of recurrence is high,
When the first gene group is low expression and the third gene group is low expression, the risk of recurrence is determined to be moderate,
When the first gene group is high expression, the second gene group is high expression, and the third gene group is low expression, it is determined that the risk of recurrence is moderate,
When the first gene group is high expression, the second gene group is low expression, and the third gene group is low expression, it is determined that the risk of recurrence is low,
The method of claim 1. In the determination step,
The expression level measured in the measurement step, the high risk group expression level measured in advance from the biological sample of the patient group determined to have a high recurrence risk, and the biological sample of the patient group determined to have a moderate recurrence risk Based on the pre-measured medium risk group expression level and the low risk group expression level measured in advance from the biological sample of the patient group determined to have a low recurrence risk, the recurrence risk of the biological sample has the highest correlation The method according to claim 1, wherein the method is classified into a risk group. In the determination step,
Calculating a correlation coefficient between the expression level measured in the measurement step and the high-risk group expression level,
Calculating a correlation coefficient between the expression level measured in the measurement step and the medium risk group expression level,
Calculating a correlation coefficient between the expression level measured in the measurement step and the low-risk group expression level,
Classifying the risk of recurrence of the biological sample into a risk group with the highest correlation coefficient;
The method of claim 3. In the determination step,
By performing clustering analysis using the expression level measured in the measurement step, the expression level of the high-risk group, the expression level of the medium-risk group, and the expression level of the low-risk group, the biological sample Classify risk of recurrence into the most correlated risk group,
The method of claim 3. In the determination step,
Regardless of the expression level of the gene selected from each of the first and second gene groups, the risk of recurrence is high when the expression level of the gene selected from the third gene group is equal to or higher than the reference value for that gene group. And
Regardless of the expression level of the gene selected from the second gene group, the expression level of the gene selected from the third gene group is smaller than the reference value for the gene group, and the gene selected from the first gene group is If it is less than the reference value for that gene group, we determine that the risk of recurrence is moderate,
The expression level of the gene selected from the third gene group is smaller than the reference value for the gene group, the expression level of the gene selected from the first gene group is greater than or equal to the reference value for the gene group, If the expression level of a gene selected from a gene group is greater than or equal to the reference value for that gene group, the risk of recurrence is determined to be moderate,
The expression level of the gene selected from the third gene group is smaller than the reference value for the gene group, the expression level of the gene selected from the first gene group is greater than or equal to the reference value for the gene group, The method according to claim 1 or 2, wherein the recurrence risk is determined to be low when the expression level of a gene selected from the gene group is smaller than a reference value for the gene group.   The first gene group includes C18orf22, C18orf55, CCDC68, CNDP2, CYB5A, LOC400657, LOC440498, MBD2, MBP, MYO5B, NARS, PQLC1, RTTN, SEC11C, SOCS6, TNFRSF11A, TXNL1, TXNL4A, VPS4B, and ZNF407. The method according to any one of 1 to 6.   The second gene group includes ASXL1, C20orf112, C20orf177, CHMP4B, COMMD7, CPNE1, DIDO1, DNAJC5, KIF3B, NCOA6, PHF20, PIGU, PLAGL2, POFUT1, PPP1R3D, PTPN1, RBM39, TAF4 and TCFL5. 8. The method according to any one of items 7. In the measuring step,
The method according to any one of claims 1 to 8, wherein the gene expression level is measured by a microarray.   The group according to any one of claims 2 to 9, wherein the group having a moderate risk of recurrence has a high risk of recurrence if it has a KRAS gene mutation, and has a low risk of recurrence if it has no KRAS gene mutation. The method according to claim 1. A computer program executed by a computer,
The computer program is for the computer.
In a biological sample collected from a colorectal cancer patient, it receives the expression level of a plurality of genes selected from the first gene group existing in the region from 18q21 to 18q23 on the long chain of chromosome 18, and on the long chain of chromosome 20. Receiving the expression level of a plurality of genes selected from the second gene group present in the region from 20q11 to 20q13, and ANGPTL2, AXL, C1R, C1S, CALHM2, CTSK, DCN, EMP3, GREM1, ITGAV, KLHL5 Receiving an expression level of a plurality of genes selected from the third gene group including MMP2, RAB34, SELM, SRGAP2P1 and VIM;
Determining the recurrence risk of colorectal cancer in the patient based on the received expression level;
A computer program that executes A computer system used to determine the recurrence risk of colorectal cancer,
A computer including a processor and memory;
In the memory,
In a biological sample collected from a colorectal cancer patient, it receives the expression level of a plurality of genes selected from the first gene group existing in the region from 18q21 to 18q23 on the long chain of chromosome 18, and on the long chain of chromosome 20. Receiving the expression level of a plurality of genes selected from the second gene group present in the region from 20q11 to 20q13, and ANGPTL2, AXL, C1R, C1S, CALHM2, CTSK, DCN, EMP3, GREM1, ITGAV, KLHL5 Receiving an expression level of a plurality of genes selected from the third gene group including MMP2, RAB34, SELM, SRGAP2P1 and VIM;
Determining the recurrence risk of colorectal cancer in the patient based on the received expression level;
Is recorded a computer program for causing the computer to execute,
Computer system. In the determination step,
When the third gene group is highly expressed, it is determined that the risk of recurrence is high,
When the first gene group is low expression and the third gene group is low expression, the risk of recurrence is determined to be moderate,
When the first gene group is high expression, the second gene group is high expression, and the third gene group is low expression, it is determined that the risk of recurrence is moderate,
When the first gene group is high expression, the second gene group is low expression, and the third gene group is low expression, it is determined that the risk of recurrence is low,
The system of claim 12. The memory stores the expression level of the high risk group, the expression level of the medium risk group, and the expression level of the low risk group,
The processor is
Receiving the expression level measured in the measurement step;
Read the expression level of the high risk group, the expression level of the medium risk group and the expression level of the low risk group from the memory,
Compare the expression level measured in the measurement step and the expression level of the high-risk group,
Compare the expression level measured in the measurement step and the expression level of the medium risk group,
Compare the expression level measured in the measurement step and the expression level of the low risk group,
Classifying the recurrence risk of the biological sample into the most correlated risk group;
Expression levels of the plurality of genes selected from the plurality of genes selected from the first gene group, a plurality of genes selected from the second gene group, and a plurality of genes selected from the third gene group. Is a value measured in advance from a biological sample of a patient group determined to have a high risk of recurrence,
The expression level of the medium risk group is expressed by a plurality of genes selected from the first gene group, a plurality of genes selected from the second gene group, and a plurality of genes selected from the third gene group Is a value measured in advance from a biological sample of a patient group determined to have a moderate recurrence risk,
Expression levels of the low risk group are expression levels of a plurality of genes selected from the first gene group, a plurality of genes selected from the second gene group, and a plurality of genes selected from the third gene group Is a value measured in advance from a biological sample of a patient group determined to have a low risk of recurrence,
The system according to claim 12 or 13. The processor is
Calculating a correlation coefficient between the expression level measured in the measurement step and the expression level of the high-risk group;
Calculating a correlation coefficient between the expression level measured in the measurement step and the expression level of the medium risk group;
Calculating a correlation coefficient between the expression level measured in the measurement step and the expression level of the low-risk group;
Classifying the risk of recurrence of the biological sample into a risk group with the highest correlation coefficient;
The system of claim 14.