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WO2019158662A1 - Méthodes - Google Patents

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Publication number
WO2019158662A1
WO2019158662A1 PCT/EP2019/053733 EP2019053733W WO2019158662A1 WO 2019158662 A1 WO2019158662 A1 WO 2019158662A1 EP 2019053733 W EP2019053733 W EP 2019053733W WO 2019158662 A1 WO2019158662 A1 WO 2019158662A1
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WIPO (PCT)
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optionally
cancer
expression
algorithm
samples
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PCT/EP2019/053733
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English (en)
Inventor
Godfrey GRECH
Christian SCERRI
Christan SALIBA
Shawn BALDACCHINO
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University Of Malta
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Priority to EP19707717.5A priority Critical patent/EP3752637A1/fr
Publication of WO2019158662A1 publication Critical patent/WO2019158662A1/fr

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    • C—CHEMISTRY; METALLURGY
    • C12—BIOCHEMISTRY; BEER; SPIRITS; WINE; VINEGAR; MICROBIOLOGY; ENZYMOLOGY; MUTATION OR GENETIC ENGINEERING
    • C12Q—MEASURING OR TESTING PROCESSES INVOLVING ENZYMES, NUCLEIC ACIDS OR MICROORGANISMS; COMPOSITIONS OR TEST PAPERS THEREFOR; PROCESSES OF PREPARING SUCH COMPOSITIONS; CONDITION-RESPONSIVE CONTROL IN MICROBIOLOGICAL OR ENZYMOLOGICAL PROCESSES
    • C12Q1/00—Measuring or testing processes involving enzymes, nucleic acids or microorganisms; Compositions therefor; Processes of preparing such compositions
    • C12Q1/68—Measuring or testing processes involving enzymes, nucleic acids or microorganisms; Compositions therefor; Processes of preparing such compositions involving nucleic acids
    • C12Q1/6876—Nucleic acid products used in the analysis of nucleic acids, e.g. primers or probes
    • C12Q1/6883—Nucleic acid products used in the analysis of nucleic acids, e.g. primers or probes for diseases caused by alterations of genetic material
    • C12Q1/6886—Nucleic acid products used in the analysis of nucleic acids, e.g. primers or probes for diseases caused by alterations of genetic material for cancer
    • C—CHEMISTRY; METALLURGY
    • C12—BIOCHEMISTRY; BEER; SPIRITS; WINE; VINEGAR; MICROBIOLOGY; ENZYMOLOGY; MUTATION OR GENETIC ENGINEERING
    • C12Q—MEASURING OR TESTING PROCESSES INVOLVING ENZYMES, NUCLEIC ACIDS OR MICROORGANISMS; COMPOSITIONS OR TEST PAPERS THEREFOR; PROCESSES OF PREPARING SUCH COMPOSITIONS; CONDITION-RESPONSIVE CONTROL IN MICROBIOLOGICAL OR ENZYMOLOGICAL PROCESSES
    • C12Q1/00—Measuring or testing processes involving enzymes, nucleic acids or microorganisms; Compositions therefor; Processes of preparing such compositions
    • C12Q1/68—Measuring or testing processes involving enzymes, nucleic acids or microorganisms; Compositions therefor; Processes of preparing such compositions involving nucleic acids
    • C12Q1/6844—Nucleic acid amplification reactions
    • C12Q1/6851—Quantitative amplification
    • C—CHEMISTRY; METALLURGY
    • C12—BIOCHEMISTRY; BEER; SPIRITS; WINE; VINEGAR; MICROBIOLOGY; ENZYMOLOGY; MUTATION OR GENETIC ENGINEERING
    • C12Q—MEASURING OR TESTING PROCESSES INVOLVING ENZYMES, NUCLEIC ACIDS OR MICROORGANISMS; COMPOSITIONS OR TEST PAPERS THEREFOR; PROCESSES OF PREPARING SUCH COMPOSITIONS; CONDITION-RESPONSIVE CONTROL IN MICROBIOLOGICAL OR ENZYMOLOGICAL PROCESSES
    • C12Q2600/00—Oligonucleotides characterized by their use
    • C12Q2600/112—Disease subtyping, staging or classification
    • C—CHEMISTRY; METALLURGY
    • C12—BIOCHEMISTRY; BEER; SPIRITS; WINE; VINEGAR; MICROBIOLOGY; ENZYMOLOGY; MUTATION OR GENETIC ENGINEERING
    • C12Q—MEASURING OR TESTING PROCESSES INVOLVING ENZYMES, NUCLEIC ACIDS OR MICROORGANISMS; COMPOSITIONS OR TEST PAPERS THEREFOR; PROCESSES OF PREPARING SUCH COMPOSITIONS; CONDITION-RESPONSIVE CONTROL IN MICROBIOLOGICAL OR ENZYMOLOGICAL PROCESSES
    • C12Q2600/00—Oligonucleotides characterized by their use
    • C12Q2600/158—Expression markers

Definitions

  • the invention relates to the treatment, detection and classification of cancer, including the identification of heterogeneous tumours and in particular relates to breast cancer. It also relates to identifying patients who are likely to respond to cancer therapy with a PP2A activator.
  • the invention defines the use of biomarkers (ERBB2, ESR1 , PGR, AURKA, KIF2C and FOXC1 expression) and the need of the novel biomarkers AURKA and KIF2C to classify breast cancer patients as Basal or Luminal.
  • a method is described to further classify Luminal cases into good (Luminal A-like) or bad (Luminal B-like) prognoses.
  • the invention also relates to methods useful in predicting if a sample comprises a gene amplification or gene reduction, or high or low gene expression.
  • PP2A protein phosphatase 2A
  • PP2A activators have recently been shown to be useful in treating breast cancers that overexpression AURKA and/or KIF2C (GB. 1704536.0, the disclosure of which is specifically incorporated herein by reference).
  • tumour cells Due to the genetic instability of tumour cells, genomic rearrangements frequently result in gene amplification or gene loss. Thus the overexpression of some genes, for instance HER2 in some breast cancers, is not mediated at the transcriptional level per se but is instead the result of an increase in gene copy number.
  • the identification of gene amplification or the loss of genes that are directly involved in tumorigenesis or tumour suppression can be clear indicators of the presence of a tumour, or of a particular tumour subtype.
  • the detection of an amplification or loss event of a gene that is not itself involved in tumorigenesis but which occurs as the result of the type of genomic instability that is frequently found in tumours can also be a marker of the presence of tumour cells.
  • a particular indicator gene may be duplicated as part of the translocation process that results in the amplification of an oncogene, and increased expression from the indicator gene may be used as a surrogate to indicate duplication of the oncogene.
  • Luminal A subtype is positive for oestrogen receptor (ER) and/or progesterone receptor (PgR) expression with low expression of Ki-67
  • luminal B apart from having an ER/PgR positive expression, includes also HER2 positive and negative subgroups associated with high Ki-67 expression.
  • the HER2-enriched subtype is well defined, with high expression of HER2 receptor, due to the ERBB2 gene amplification, combined with low or absent ER and PgR.
  • HER2 expression was found to be present in microvesicles originating from tumour cells, that induced activation of mitogenic signals in recipient fibroblasts 10 .
  • Basal type tumours are in general negative for the 3 receptors significantly overlapping with the triple negative breast cancer (TNBC) diagnostic subtype 5 ⁇ 6 .
  • TNBC triple negative breast cancer
  • Other markers are used to determine epithelial and mesenchymal characteristics.
  • Fibronectin (FN1 ) is a main component of breast tissue mesenchymal compartment. Increased FN1 expression is accompanied by high Ki67 staining showing a signature for a more invasive tumour 7 8 and an increased expression is associated with metastasis 9 .
  • the luminal A-like or luminal A, and luminal B-like or luminal B subclasses are classified based immunohistochemical and clinicopathological criteria set out in the St Gallen 2013 conference (Harbeck, N., Thomssen, C., & Gnant, M. (2013). St. Gallen 2013: Brief Preliminary Summary of the Consensus Discussion. Breast Care, 8(2), 102-109. http://doi.Org/10.1 159/000351 193). Alternative classifications have been proposed (Maisonneuve et al 2014 Breast Cancer Res 16(3): R65; and Inic et al 2014 Clinical Medicine Insights. Oncology, 8, 107-1 1 1. http://doi.orq/10.4137/CMQ.S18006).
  • Each subtype of cancer may be associated with a different response to a particular treatment and so identification of the subtype of cancer is important to ensure that the subject receives the most appropriate treatment.
  • gene amplification events are routinely assayed by either immunohistochemistry (IHC) and in-situ hybridization (ISH).
  • IHC testing can show how much of the particular protein is present on the cancer cell surface which may in some situations be considered to be the most relevant parameter to assay, while ISH testing measures the number of copies of the particular gene inside each cell.
  • ISH tests measures the number of copies of the particular gene inside each cell.
  • fluorescence and bright-field ISH www.cancer.net).
  • both IHC and ISH suffer from many problems that make them less than ideal tests to be carried out on large numbers of samples. For example, both tests require a visual interpretation of the degree of hybridisation of a probe, either to the DNA or protein, and are therefore susceptible to variation between pathologists and pathology labs. Both methods require a high quality sample and are affected by degradation, fixation time and care in handling.
  • ISH is considered to be more accurate than IHC, though it is IHC that is the more widely used since it is more practical.
  • ISH is also susceptible to problems such as loss of the control CEP17 chromosomal region, or the concomitant amplification of CEP17 in ERBB2 amplified cells, both of which would present misleading results.
  • some breast cancer cell lines such as MDAMB453 and JIMT1 harbour the ERBB2 gene amplification but show a relatively low expression of HER2 that is undetectable by HER2 immunohistochemistry (Koninki et al., 2010. Kao et al., 2009).
  • the JIMT1 ERBB2 gene amplified cell line is resistant to trastuzumab and has reduced levels of the ERBB2 transcript compared to SKBR3 and BT474 HER2 positive cell lines (Tanner et al., 2004, Koninki et al., 2010).
  • Table 1 A comparative audit of diagnostic HER2 results on breast biopsy specimens as opposed to the patient-matched breast resection specimen where data was available. Out of 2303 breast cancer cases diagnosed at Mater Dei Hospital , Malta between 2009-2016, only 111 breast cancer cases (4.8%) had a diagnostic result for HER2 for both the biopsy and the main specimen. A discrepancy in the HER2 status was observed in 29/111 (26.13%) of cases (unpublished results).
  • the Prosigna Breast Cancer Prognostic Gene Signature Assay (NanoString) based on PAM50 biomarker panel was US FDA Approved in September 2013 and CE mark in 2012. However, the Prosigna assay is designed as an indicator of prognosis of tumours and not classification, therapeutic selection or to detect response 12 .
  • the present invention provides an accurate method or test to allow the classification of a cancer as either cancerous, or a sub-class of that cancer, and in particular relates to cancers or sub-cancers that are known to be associated with a gene amplification event, for example the ERBB2 gene in some breast cancers.
  • the transcription level is used to diagnose the status of the cell with respect to that gene or genes, for example whether a breast cancer sample is HER2 positive or negative.
  • the present method is improved over the currently used ISH method since discrepancies between amplification at the level of chromosomes and levels of transcribed RNA are recorded both in cell lines and patient material; and is improved over the currently used IHC method for at least the reasons given above.
  • the invention also provides a further method which classifies breast cancer tumours into various sub-classes of breast cancer.
  • the invention also provides a set of normalisation genes useful for use with the tests described above when applied to breast cancer.
  • cancer cells sensitive to treatment with a PP2A activator exhibit overexpression of the markers AURKA and KIF2C.
  • These include cancer cells exemplified by Triple negative breast cancer (TNBC) cells that do not benefit from targeted therapy and have bad prognosis when current front-line anti-cancer therapies are administered.
  • TNBC Triple negative breast cancer
  • the markers AURKA and KIF2C further correlate with low PP2A enzymatic activity.
  • the markers AURKA and KIF2C may thus be used to predict responsiveness of a patient to treatment with a PP2A activator.
  • AURKA and KIF2C may also be targeted with antagonists to thereby treat cancer, based on their correlation with cancer.
  • the markers AURKA and KIF2C have utility in detecting, prognosing and classifying cancer.
  • the markers may be used to classify cancers with a worse prognosis, in particular basal versus luminal breast cancer subtype and subsets of luminal cases with bad prognosis (Luminal B).
  • the markers may thus be assayed using suitable reagents in kits for detection and classification of cancer and for predicting therapeutic benefit from activation of the PP2A complex.
  • the present invention aims to address the problems of the prior art discussed above and provides methods (exemplified by the role of HER2 in breast cancer and the classification of breast cancers as HER2 positive (HER2+), triple negative breast cancer (TNBC), Basal, Luminal A or Luminal B) but which can now be generalised and extrapolated to other genes involved in various diseases, for the determination of the status of a given tumour, tumour sample or tumour subsample with regards to a particular gene or set of genes that are quick, can be standardised, are much less susceptible to user interpretation and which advantageously can be used on degraded samples, which makes these methods suitable for use in situations where it isn’t possible to handle or store samples under optimum conditions.
  • RNA based assays using archival formalin-fixed paraffin-embedded (FFPE) material is challenging due to variability in surgical tissue processing and degradation of RNA caused by tissue integrity preservation using Formalin 1 ⁇ 2 .
  • the invention makes use of branched-chain DNA (bDNA) technology, for example the QuantiGene ® technology.
  • bDNA technology replaces enzymatic amplification of target template with hybridisation of specific probes and amplification of a reporter signal 3 .
  • the short recognition sequences of the capture and detection probes are designed to hybridise to short fragments of target RNA 4 .
  • tissue homogenates directly as starting material of this assay overcomes the inevitable loss of RNA occurring in assays requiring prior RNA extraction and purification and also allows multiple assays to be performed on the same sample.
  • Multiplex technology such as the Luminex technology provides the possibility to multiplex the assay, measuring expression of a panel of targets from low material input.
  • the present invention can be used in conjunction with archived, degraded, formalin-fixed and paraffin-embedded samples means that the present invention can be used to readily identify novel biomarkers by using historic samples.
  • the invention is further advantageous since the multiplex nature of one embodiment of the invention allows a single assay to be performed which can fully classify a cancer, for example a breast cancer.
  • the methods of the invention (which include in one embodiment the following markers ESR1 , HER2, PGR, AURKA, KIF2C, FOXC1 ) along with particular normalisation genes is able to classify breast cancer in the current diagnostic groups (as determined by IHC and FISH) with high accuracy (see for example Examples 2-7).
  • the potential of this molecular-based assay with quantitative measurements has potential to digitalise the current methodologies of breast cancer diagnosis. Therefore, this assay can assist in the diagnosis of breast cancer as well as determine therapeutic decisions.
  • AURKA and KIF2C are biomarkers of PP2A activity
  • classification of breast cancer subtypes using these markers set another potential therapeutic subtype, that is considered to benefit from PP2A activation therapy.
  • the prediction by an algorithm as used herein based on ESR1 , ERBB2, PGR, AURKA, KIF2C and FOXC1 expression (6 biomarker panel) allows the definition of Luminal and Basal subtypes and the same measurements can be used to discriminate between Luminal A and Luminal B when prediction is run using the Luminal cases only (see for example Examples 2-4).
  • the use of the methods and panels of the invention reduces the cost of runs, digitalise the workflow, and allows minimal sample requirement through multiplexing of the RNA-based assay.
  • PAM50 algorithms have been trained on microarray and RT-PCR data which require RNA extraction 19 .
  • the methods described herein utilise algorithms that have been assessed on RNA Sequencing data as well as Quantigene 2.0 data. The latter methodology eliminates extraction bias, and is minimally affected by degraded RNA.
  • Methods of the invention can also be used to detect heterogeneity within a single tumour sample, for instance, can detect the presence of both luminal and basal subtypes within the same tumour, in some instances without the need of, for instance, microdissection and separate analysis of morphologically distinct tumours identified following staining and microscopy. This is important since whilst the majority of a tumour may be of one sub- type and may respond to a particular treatment, the presence of an additional tumour subtype which may not respond to that treatment indicates that an alternative or additional treatment may be needed.
  • the present invention provides a method for classifying a cancer into one or more sub-classes wherein the method involves the use of bDNA technology.
  • breast cancer As discussed above, it is well known that although on the face of it a cancer is described as, for example, a breast cancer, within that broad classification exists a heterogenous range of cancers, distinguished by unique phenotypic and genotypic differences and which can each require a different therapy.
  • breast cancer can be classified as HER2+, Basal, Luminal (which encompasses both the Luminal A and Luminal B sub classes), and each of which responds better to different therapies.
  • HER2+ breast cancers are known to be suitable for treatment with Herceptin (trastuzumab), Kadcyla (Herceptin and emtansine), Nerlynx (neratinib), Perieta (pertuzumab), and Tykerb (lapatinib), all of which take advantage of the overexpression of the HER2 protein to specifically target the cancer cell.
  • Such treatments are considered to be less effective and/or not targeted to the cancer cell if the cell does not either harbour the HER2 gene amplification or the has some equivalent mutation/alteration that increases the expression of HER2.
  • the present invention is exemplified by the development and optimisation of a method to classify breast cancer samples, for example as HER2+ cancers, or basal cancers or Luminal A or Luminal B cancers
  • the methods and algorithms described herein can be used to similarly develop and optimise methods to classify other cancers, for example other cancers that have a sub-class associated with a gene amplification.
  • the skilled person will understand for which cancers and sub-classes of cancers the present invention is appropriate, for example by understanding which cancers or sub-classes of cancers are associated with which gene amplifications.
  • Intrachromosomal amplifications of chromosome 21 involves amplification of the gene RUNX1 , defining a subgroup of B-cell precursor Acute Lymphoblastic Leukemia (ALL), predicting high relapse rare. Intensifying the therapy in these patients significantly reduce the replication rate [Harrison CJ: Blood spotlight on iAMP21 acute lymphoblastic leukemia (ALL), a high-risk pediatric disease. Blood 125: 1383-1386 (2015)].
  • ALL Acute Lymphoblastic Leukemia
  • MYCN gene amplification is found in various cancer types including colorectal cancer, neuroblastoma and others. MYCN gene amplification is an independent adverse prognostic factor in neuroblastoma predicting patients associated with rapid disease progression, involving all ages and stages [Thompson D, Vo KT, London WB, Fischer M, Ambros PF, et al: Identification of patient subgroups with markedly disparate rates of MYCN amplification in neuroblastoma: a report from the International Neuroblastoma Risk Group Project. Cancer 122: 935-945 (2016).]. hTERC gene amplification
  • hTERC gene amplification in liquid-based cervical samples was found in 37% of HPV genotype positive individuals, and 70% of hTERC amplified samples were diagnosed as CIN2+.
  • hTERC amplification significantly improves the specificity and positive predictive value of HPV screening [Zappacosta R, lanieri M M, Buca D, Repetti E, Ricciardulli A, and Liberati M. Clinical Role of the Detection of Human Telomerase RNA Component Gene Amplification by Fluorescence in situ Hybridization on Liquid-Based Cervical Samples: Comparison with Human Papillomavirus-DNA Testing and Histopathology. Acta Cytologica 2015;59:345-354]
  • Programmed death-ligand 1 is a protein that in humans is encoded by the CD274 gene while PD-L2 is encoded by the PDCD1 LG2 gene.
  • the binding of PD-L1 or PD-L2 with the PD-1 receptor on T cells induces a signal that inhibits TCR-mediated activation of IL-2 production and T cell proliferation [Sheppard KA, Fitz LJ, Lee JM, Benander C, George JA, Wooters J, Qiu Y, Jussif JM, Carter LL, Wood CR, Chaudhary D (September 2004).
  • PD-1 inhibits T-cell receptor induced phosphorylation of the ZAP70/CD3zeta signalosome and downstream signaling to PKCtheta.
  • P-L1 co-stimulation contributes to ligand-induced T cell receptor down- modulation on CD8+ T cells.
  • EMBO Molecular Medicine. 3 (10): 581 -92.]. This interaction is thought to be one of the causes of how tumour cells might evade detection and destruction by the body’s immune system.
  • MET is a proto-oncogene that encodes a receptor tyrosine kinase.
  • the aberrant activation of MET signalling in a subgroup of cancers is an example of how certain cancer become dependent on a single overactive oncogene for their proliferation and survival, a phenomenon that has become known as“oncogene addiction”. This activation is usually the result of gene amplification, polysomy, and gene mutations.
  • MET deregulation can be identified in various human malignancies, including cancers of kidney, liver, stomach, breast, and brain [Kawakami H Okamoto I Okamoto W Tanizaki J Nakagawa K et. al. Targeting MET Amplification as a New Oncogenic Driver. Cancers 2014 vol: 6 (3) pp: 1540-52] AR-V7
  • Androgen deprivation therapy provides effective, though temporary tumour control in patients with metastatic prostate cancer [G. Attard, J.S. de Bono Translating scientific advancement into clinical benefit for castration-resistant prostate cancer patients Clin Cancer Res, 17 (201 1 ), pp. 3867-3875,]. Androgen deprivation therapy can be achieved through a number of chemotherapeutic protocols. But there is clinical evidence that not all patients benefit from such therapy [H.l. Scher, K. Fizazi, F. Saad, M.-E. Taplin, C.N. Sternberg, K. Miller, et al. Increased survival with enzalutamide in prostate cancer after chemotherapy N Engl J Med, 367 (2012), pp.
  • FGFR fibroblast growth factor receptor
  • the fibroblast growth factor receptor (FGFR) family is a novel potential therapeutic target in cancer.
  • FGFR plays an impoartant role in stimulating cell proliferation and migration as well as in promoting survival of various types of cells [Beenken A, Mohammadi M. The FGF family: biology, pathophysiology and therapy. Nat Rev Drug Discov 2009; 8: 235- 253.]
  • FGFR aberrations including receptor overexpression through gene amplification or post-transcriptional regulation, FGFR mutations, FGFR translocations, alternative splicing of FGFR, have been identified, all active aberrations constitutively activate downstream pathways and contribute to tumour development.
  • the method of the invention may be used to identify new sub-classes of cancers.
  • the method could be used to assess the RNA expression level of a multitude of samples and analysis of the results can reveal novel therapeutic and/or prognostic tumour sub-classes of cancers of distinct tissue origin, for example new sub-classes of lung cancer, or new sub-classes of colon or breast cancer, each with a unique and distinctive expression pattern.
  • novel therapeutic and/or prognostic tumour sub-classes of cancers of distinct tissue origin for example new sub-classes of lung cancer, or new sub-classes of colon or breast cancer, each with a unique and distinctive expression pattern.
  • Cancers are generally classified according to one or more various markers that the cancer displays, for example the cancer may have one or two or more genes are expressed to a higher level than the expression level of those genes in another sub-class of the same cancer.
  • the HER2+ subclass of cancers is typically defined by the presence of an amplification of the ERBB2 gene, it is the important consequence of this amplification, i.e. an increased expression of the ERBB2 gene, i.e. an increased level of the HER2 mRNA and protein that truly makes the HER2+ sub-class of breast cancer a suitable target for certain therapies such as Herceptin.
  • an ER+ cancer is a cancer in which a certain number of the cells in the sample display the ER, as determined by IHC.
  • E- Cadherin positivity defines cancers as of ductal origin as opposed to being of lobular origin (E-Cadherin negative) ; whilst a mutation in the c-kit gene is associated with c- kit gastrointestinal stromal tumours (GIST).
  • RNA associated with each of the key genes that are used to classify the different sub-classes of cancers is a more accurate and informative method of producing clinically relevant classifications than assessment of gene copy number.
  • the method provided herein, which is quick, sensitive and not subject to pathologist error or interpretation is also improved over those methods such as IHC which directly assess the resultant protein.
  • the present invention makes use of the RNA expression levels of at least 1 , for example at least 2, optionally at least 3, or at least 4, or at least 5, or at least 6, or at least 7, or at least 8, or at least 9, or at least 10 genes that are known to be associated with various cancer sub-classes.
  • RNA expression level can be assessed by any means, for example by reverse transcription PCR (rtPCR), a preferred embodiment utilises branched-DNA (bDNA) technology. This is advantageous for a number of reasons.
  • the present invention provides a method for classifying a cancer into one or more sub-classes wherein the method involves the use of bDNA technology.
  • bDNA technology replaces enzymatic amplification of target template with hybridisation of specific probes and amplification of a reporter signal 3 .
  • the short recognition sequences of the capture and detection probes are designed to hybridise to short fragments of target RNA 4 .
  • bDNA removes the requirement for RNA extraction and allows the direct use of a tissue homogenate from, for example, fresh tissue, fresh-frozen tissue, FFPE tissue sections or a laser dissected, stained or unstained sample, or exosomes, the technical variation obtained by using this assay is much reduced.
  • kits for performing bDNA assays include the QuantiGene Plex assay from Thermo Fisher; Chiron branched DNA signal amplification (bDNA) assay used for viral molecular diagnositics [VERSANT® HIV-1 RNA 3.0 Assay (bDNA) ]; Diacarta (http://www.diacarta.com/technology/bdna-signal-amplification- technology/); and RNAscope (http://acdbio.com/science/technoloqv-overview).
  • bDNA Chiron branched DNA signal amplification
  • Diacarta http://www.diacarta.com/technology/bdna-signal-amplification- technology/
  • RNAscope http://acdbio.com/science/technoloqv-overview.
  • branched DNA uses sequential hybridization of oligonucleotides to a captured target RNA in order to amplify a signal for quantitative measurement.
  • the sample is first added to a bead mix that consists of both the magnetic Luminex beads as well as a set of probes that are used to capture the target RNA.
  • the capture extenders hybridize to the capture probes conjugated to the beads while also hybridizing to the target RNA sequence. This captures the target RNA onto the desired beads through a process called ‘cooperative hybridization’.
  • Each bead colour has its own target-specific set of probes, allowing multiple genes to be captured onto different beads.
  • label extenders which provide the basis for the branched DNA signal amplification structures. These label extenders are always designed in pairs to enhance the specificity of the assay.
  • the third type of probe, the blocking probe hybridizes to any piece of the target sequence that is not already targeted by the capture extenders or label probes. The purpose of the blocking probes is to form a complete double-stranded piece of RNA, protecting it from RNases and helping to prevent secondary structure within the target region.
  • the capture extenders, label extenders, and blocking probes all comprise the target-specific probe set which can be designed and provided commercially, for example from Thermo Fisher.
  • the branched DNA oligonucleotides form the signal amplification structure.
  • a pre-amplifier hybridizes to the label extender pairs.
  • many amplifiers hybridize to each pre-amplifier, and in the following incubation, many label probe oligonucleotides hybridize to each amplifier.
  • the label probe molecule is conjugated with biotin, so when streptavidin-phycoerythrin is added in the last step, a fluorescent signal is created and measured.
  • the invention is herein exemplified by use of the QuantiGene Plex Assay, it is considered that any bDNA assay can be used in the methods of the invention.
  • the method involves the detection of RNA levels of relevant genes via the QuantiGene Plex Assay.
  • the bDNA assay for example a QuantiGene Plex Assay, is a multiplex assay, which allows the simultaneous detection the expression level of many RNA species.
  • the bDNA assay is a multiplex assay, for example an assay that allows the simultaneous detection of the expression level of more than 1 RNA species, optionally more than 2 RNA species, optionally more than 3 RNA species, optionally more than 4 RNA species, optionally more than 5 RNA species, optionally more than 6 RNA species, optionally more than 7 RNA species, optionally more than 8 RNA species, optionally more than 9 RNA species, optionally more than 10 RNA species, optionally more than 1 1 RNA species, optionally more than 12 RNA species, optionally more than 14 RNA species, optionally more than 16 RNA species, optionally more than 18 RNA species, optionally more than 20 RNA species, optionally more than 30 RNA species, optionally more than 40 RNA species, optionally more than 50 RNA species.
  • microbead technology for example the Luminex platform
  • detection of the level of RNA expression is by the use of a multiplex platform, optionally a microbead platform, optionally the Luminex platform.
  • the detection of the level of RNA expression is by the use of the QuantiGene Plex assay (Thermo Fisher).
  • the present invention allows the classification of the cancer into at least two sub-classes, optionally at least three sub-classes, optionally at least four-subclasses, optionally at least five or more sub-classes.
  • the sub-classes of cancer may be associated with a gene amplification or gene reduction, or may be associated with a change in RNA expression level that occurs independently of a gene amplification or reduction event.
  • one or more of the sub-classes of cancer is associated with a gene amplification or a gene reduction event.
  • associated with a gene amplification or gene reduction event we include the meaning that that particular class of cancer typically encompasses cancers with a gene amplification or gene reduction event, but does not mean that all cancers that make up that sub-class necessarily have the gene amplification or gene reduction.
  • the well known HER2+ sub-class of breast cancers typically arises due to a gene amplification of the ERBB2 gene, and currently clinical diagnosis of the HER2+ phenotype often involves detection of a gene amplification event.
  • the skilled person will appreciate that there are many factors that can affect the expression level of a particular gene, for example whether a particular gene is overexpressed or not, for example there may be some breast cancers that have a mutation in the ERBB2 gene which results in an increased level of mRNA, for example due to a decreased rate of ERBB2 mRNA turnover.
  • such a sample may still be considered to be a HER2+ cancer, provided the expression level of ERBB2 reaches a particular threshold.
  • the threshold is set according to the level of RNA expression of samples that are known to harbour the gene amplification or gene reduction event, for example that are known to be HER2.
  • the skilled person is well equipped to determine the relevant threshold levels of expression using standard lab techniques.
  • one or more of the sub-classes of cancer is not associated with a gene amplification or gene reduction event, for example is associated with a change in the RNA expression level of a particular gene due to other mechanisms.
  • such mechanisms can involve a mutation in the gene that is used to classify the cancer, or a mutation in a gene that encodes for a regulatory protein that affects the expression level of the gene that is used to classify the cancer.
  • epigenetic mechanisms such as hypermethylation.
  • hypermethylation in particular has significant effects on gene expression and the resulting changes in RNA expression can be detected using the methods of the invention.
  • At least one sub-class of cancer is associated with a gene amplification event. In another embodiment, at least one sub-class of cancer is associated with an overexpression of a particular gene.
  • one or more of the sub-classes of cancer is associated with an increase or decrease in copy number of a gene.
  • the cancer can be classified into at least one sub-class of cancer associated with a gene amplification event and at least one sub-class of cancer that is not associated with a gene amplification event, optionally at least one sub-class of cancer that is associated with an overexpression or an underexpression of a particular gene.
  • the expression level of any number of RNA species may be determined in order to classify the cancer into the various sub-classes.
  • the method comprises the determination of the level of RNA expression of optionally at least one RNA species, optionally more than 2 RNA species, optionally more than 3 RNA species, optionally more than 4 RNA species, optionally more than 5 RNA species, optionally more than 6 RNA species, optionally more than 7 RNA species, optionally more than 8 RNA species, optionally more than 9 RNA species, optionally more than 10 RNA species, optionally more than 1 1 RNA species, optionally more than 12 RNA species, optionally more than 14 RNA species, optionally more than 16 RNA species, optionally more than 18 RNA species, optionally more than 20 RNA species, optionally more than 30 RNA species, optionally more than 40 RNA species, optionally more than 50 RNA species.
  • Some of the RNA species may be associated with the cancer or sub-class of the cancer, whilst others may not be associated with the cancer or sub-class of the cancer, and are used for normalising the expression levels of the genes that are associated with the cancer or sub cancer.
  • the skilled person will be very aware of the significance in choosing the correct normalisation genes. Methods of doing so are described herein, along with a specific and novel set of markers that can used with the method of the invention for the classification of breast cancer. Accordingly, the invention provides particular markers, ACTB; PPIB; HPRT 1 ; and/or TBP, which either alone, in particular combinations, or all together, can be used to normalise the data obtained from the method of the invention when applied to breast cancer.
  • the selection of appropriate normalisation genes is affected by cell or tissue type as well as the expression level of the “test” genes, i.e. the genes that are associated with the cancer or sub-cancer.
  • the method involves the determination of the expression level:
  • At least one or more genes associated with the cancer or at least one sub-class of cancer optionally at least one gene associated with the cancer or at least one sub-class of the cancer, optionally at least two genes associated with the cancer or at least one sub class of the cancer, optionally at least three genes associated with the cancer or at least one sub-class of the cancer, optionally at least four genes associated with the cancer or at least one sub-class of the cancer, optionally at least five genes associated with the cancer or at least one sub-class of the cancer, optionally at least six genes associated with the cancer or at least one sub-class of the cancer, optionally more than six genes associated with the cancer or at least one sub-class of the cancer, optionally wherein the at least one or more genes associated with the cancer or at least one sub-class of cancer are selected from the group consisting of ERBB2, ESR1 , AURKA, KIF2C, PGR and FOXC1 and/or
  • the one or more genes has been determined to be suitable for use as a normalising gene, optionally determined by an algorithm trained on a dataset of known cancer classification, optionally wherein the at least one gene not associated with the cancer or sub-cancer is selected from the group consisting of ACTB; PPIB; FIPRT1 ; and TBP.
  • a test which requires the determination of fewer RNA expression levels is preferable over a test that requires the determination of more RNA expression levels, provided that the test meets the required level of accuracy.
  • the expression levels of the ERBB2, ESR1 , PGR and FOXC1 genes can be used. Flowever, a greater accuracy of classification can be achieved by determining the expression levels of all of the ERBB2, ESR1 , PGR, AURKA, KIF2C and FOXC1 genes.
  • genes ERBB2, ESR1 , FOXC1 , PGR, AURKA, KIF2C, ACTB, PPIB, HPRT1 and TBP are also encompassed by the terms ERBB2, ESR1 , FOXC1 , PGR, AURKA, KIF2C, ACTB, PPIB, HPRT1 and TBP.
  • the genes or proteins may contain polymorphisms that are not represented in the sequences provided below.
  • ERBB2, ESR1 , FOXC1 , PGR, AURKA, KIF2C, ACTB, PPIB, HPRT1 and TBP we include the meaning of the relevant gene or protein sequences as provided below, as well as genes or proteins with at least 75% homology to those sequences, for example at least 80% homology to those sequences, for example at least 85% homology to those sequences, for example at least 90% homology to those sequences, for example at least 95% homology to those sequences, for example at least 98% homology to those sequences, for example at least 99% homology to those sequences, for example 100% homology to those sequences.
  • Table 2 protein and nucleic acid sequences of the exemplified genes associated with the cancer or at least one sub-class of cancer and normalisation genes
  • accuracy 99.5% is required in order for a test to have clinical significance and to be adopted in practice.
  • accuracy we include the meaning of concordance with samples of known sub-class as determined by a different method. For example, as described herein samples are assessed with the method of the invention and compared to samples of known sub-class, the status of which has in some embodiments been determined previously by IHC or FISH. An accuracy or concordance of 100% means that all samples that were previously identified as of a particular sub-class are also identified by the methods described herein as being of that same sub-class.
  • accuracy is determined using well defined samples such as cell lines and patient samples that are classified positive or negative to the measured analyte, example HER2 IHC scores of 3+ and 0 or 1 + on FFPE whole sections from tumour resections showing no heterogeneity (only one tumour site).
  • Discrepancies may occur wherein, for example, the status of a sample has been previously characterised using FISH, i.e. determining gene copy number, and has been determined as comprising a gene amplification, for example of the ERBB2 gene. If that amplification does not result in the expected increase in RNA level, then the two results will not correlate.
  • FISH i.e. determining gene copy number
  • the cancer is breast cancer.
  • the sub-classes of cancer comprise at least one or more of HER2+, ER+, Basal, Triple Negative Breast Cancer (TNBC), and Luminal A, Luminal B and HER2 enriched.
  • TNBC Triple Negative Breast Cancer
  • the sub-classes are TNBC, ER+ and HER2+.
  • the sub-classes are Basal, Luminal and HER2-enriched.
  • the Luminal sub-class can be further classified as Luminal A or Luminal B.
  • the method can distinguish, or classify, a breast cancer as HER2+ or not HER2+.
  • the method can distinguish, or classify, a breast cancer as ER+ or not ER+.
  • the method can distinguish or classify a breast cancer as TNBC or not TNBC.
  • the method can distinguish or classify a breast cancer as Basal or not Basal.
  • the method can distinguish or classify a breast cancer as Luminal or not Luminal.
  • the method can distinguish or classify a breast cancer as Luminal A or not Luminal A.
  • the method can distinguish or classify a breast cancer as Luminal B or not Luminal B.
  • the method can distinguish or classify a breast cancer as HER2-enriched or not Her2-enriched.
  • the method can distinguish or classify a breast cancer as TNBC, ER+ and/or HER2+ or not TNBC, ER+ and/or HER2+.
  • the method can distinguish or classify a breast cancer as Basal, Luminal and HER2-enriched or not Basal, Luminal and HER2-enriched.
  • the Luminal sub-class can be further classified as Luminal A or Luminal B.
  • the method can distinguish or classify a breast cancer as HER2+; ER+, HER2+ and ER+; or TNBC. In yet another embodiment the method can classify a breast cancer as Luminal or Basal breast cancer. Further, in another embodiment, the method can classify a breast cancer as Luminal A or Luminal B breast cancer. In a preferred embodiment the method can classify a breast cancer as HER2+; ER+; HER2+ and ER+; TNBC; Basal; Luminal A; and/ or Luminal B.
  • the method comprises the determination of the level of RNA expression of ERBB2.
  • the method comprises the determination of the level of RNA expression of ESR1 .
  • the method comprises the determination of the level of RNA expression of AURKA.
  • the method comprises the determination of the level of RNA expression of KIF2C.
  • the method comprises the determination of the level of RNA expression of PGR.
  • the method comprises the determination of the level of RNA expression of FOXC1.
  • any determination of the expression level of a gene associated with a cancer or sub-class of a cancer requires normalisation against appropriately selected normalising genes.
  • the inventors have shown that normalisation of expression data to at least any one of ACTB, HPRT1 , TBP and PPIB; preferably all of ACTB, HPRT1 , TBP and PPIB allows a highly accurate classification of breast cancers.
  • the expression levels as determined by the bDNA are normalised to at least any one of ACTB, HPRT1 , TBP and PPIB; preferably all of ACTB, HPRT1 , TBP and PPIB.
  • the method comprises the determination of the level of RNA expression of any one or more of ACTB, PPIB, HPRT1 and TBP.
  • the method comprises the determination of the level of RNA expression of ERBB2, ACTB, PPIB, HPRT1 and TBP.
  • the method comprises the determination of the level of RNA expression of ESR1 , ACTB, PPIB, HPRT1 and TBP.
  • the method comprises the determination of the level of RNA expression of ESR1 , ERBB2, ACTB, PPIB, HPRT1 and TBP.
  • the method comprises the determination of the level of RNA expression of ERBB2; AURKA and/or KIF2C; ACTB; PPIB; HPRT1 ; and TBP.
  • the method comprises the determination of the level of RNA expression of ESR1 ; AURKA and/or KIF2C; ACTB; PPIB; HPRT1 ; and TBP.
  • the method comprises the determination of the level of RNA expression of ERBB2; ESR1 ; AURKA and/or KIF2C; ACTB; PPIB; HPRT1 ; and TBP.
  • the method comprises the determination of the level of RNA expression of ERBB2; ESR1 ; AURKA; KIF2C; PGR; FOXC1 ; ACTB; PPIB; HPRT1 ; and TBP.
  • the method comprises the determination of the level of RNA expression of ERBB2; ESR1 ; PGR; FOXC1 ; ACTB; PPIB; HPRT1 ; and TBP.
  • the method comprises the determination of the level of RNA expression of AURKA; KIF2C; PGR; FOXC1 ; ACTB; PPIB; HPRT1 ; and TBP.
  • the method comprises the determination of the level of RNA expression of PGR; FOXC1 ; ACTB; PPIB; HPRT1 ; and TBP. In one embodiment the method comprises the determination of the level of RNA expression of PGR; ACTB; PPIB; HPRT1 ; and TBP.
  • the method comprises the determination of the level of RNA expression of ESR1 ; PGR; ACTB; PPIB; HPRT1 ; and TBP.
  • the method comprises the determination of the level of RNA expression of AURKA; KIF2C; ACTB; PPIB; HPRT1 ; and TBP.
  • the method comprises the determination of the level of RNA expression of ESR1 ; ERBB2; PGR; ACTB; PPIB; HPRT1 ; and TBP.
  • the method comprises the determination of the level of RNA expression of ESR1 ; ERBB2; AURKA; ACTB; PPIB; HPRT1 ; and TBP.
  • the method comprises the determination of the level of RNA expression of ESR1 ; ERBB2; KIF2C; ACTB; PPIB; HPRT1 ; and TBP.
  • the method further comprises the use of machine learning to classify the cancer into the sub-classes.
  • the machine learning is an algorithm, optionally is an algorithm selected from the group consisting of the Neural Network algorithm, Decision Tree, Random Forest and Support Vector Machine.
  • the machine learning is the Neural Network algorithm.
  • the method comprises the use of the Neural Network algorithm, Decision Tree, Random Forest and/or Support Vector Machine.
  • the method comprises the use of the Neural Network algorithm.
  • the method comprises the use of RapidMiner Studio Community software.
  • Such algorithms require training on a defined data set so that the algorithm can learn which features are associated with each other, for example which level of HER2 expression is associated with a declaration of HER2+ status; which level of ER expression is associated with a declaration of ER+ status; which expression levels of certain genes are associated with the Luminal A and Luminal B sub-classes etc.
  • the algorithm for example the Neural Network, has been trained on a suitable data set.
  • Suitable datasets are considered to include at least large databases of cancers samples, for example those found within the The Cancer Genome Atlas and Oncomine portals.
  • such databases have details or the classification of samples, for examples samples may have been classed as various sub-classes of cancer based on FISH or IHC data.
  • the dataset contains details of samples that have been classified using a clinically relevant method, for example IHC and FISH, preferably without equivocal or heterogenous cases.
  • the algorithm may be trained on all samples that samples that have been classified using a clinically relevant method, for example IHC and FISH, preferably without equivocal or heterogenous cases, or it may be necessary to remove certain samples prior to analysis, for example it may be necessary to first identify samples of a certain sub-class, for example HER2+ samples, and remove them from the dataset prior to training the algorithm for, for example Luminal vs Basal classification.
  • a clinically relevant method for example IHC and FISH, preferably without equivocal or heterogenous cases
  • it may be necessary to remove certain samples prior to analysis for example it may be necessary to first identify samples of a certain sub-class, for example HER2+ samples, and remove them from the dataset prior to training the algorithm for, for example Luminal vs Basal classification.
  • Algorithm 1 trained on normalised expression of ERBB2 in:
  • a sample set comprising HER2+ samples as defined by IHC and FISH;
  • a sample set comprising HER2+ and/or ER+ samples as defined by IHC and FISH; and a sample set not comprising HER2+ and ER+ samples as defined by IHC and FISH;
  • Algorithm 4 trained on normalised expression of ESR1 , ERBB2, AURKA and/or KIF2C in: a sample set comprising HER2+ and/or ER+ samples as defined by IHC and FISH; and a sample set not comprising HER2+ and/or ER+ samples as defined by IHC and FISH;
  • Algorithm 6 trained on normalised expression of ERBB2 and ESR1 in samples in a dataset from the TCGA, comprising
  • HER2+ and/or ER+ samples as defined by IHC and FISH;
  • Algorithm 7 trained on normalised expression of ERBB2 and ESR1 in samples in a dataset from the TCGA which had HER2-enriched samples as defined by PAM50 and HER2+ as defined by IHC/FISH removed.
  • Algorithm 8 trained on normalised expression of ERBB2, ESR1 , PGR, AURKA, KIF2C and FOXC1 in samples in a dataset from the TCGA which had HER2-enriched samples as defined by PAM50 and HER2+ samples as defined by IHC/FISH removed.
  • Algorithm 9 trained on normalised expression of ERBB2, ESR1 , FOXC1 , PGR normalised expression in:
  • Algorithm 10 trained on normalised expression of ERBB2, ESR1 , FOXC1 , PGR, AURKA and KIF2C in:
  • Algorithm 1 identifies samples as HER2 positive or HER2 Negative.
  • Algorithm 2 identifies samples as ER positive or ER Negative.
  • Algorithm 3 identifies samples as HER2 positive and/or ER positive or TNBC.
  • Algorithm 4 identifies samples as HER2 positive and/or ER positive or TNBC.
  • Algorithm 5 identifies samples as HER2 positive or HER2 Negative.
  • Algorithm 6 identifies samples as HER2 positive and/or ER positive or TNBC.
  • Algorithm 7 identifies samples as Luminal or Basal.
  • Algorithm 8 identifies samples as Luminal or Basal.
  • Algorithm 9 identifies samples as Luminal A or Luminal B.
  • Algorithm 10 identifies samples as Luminal A or Luminal B.
  • Algorithm 1 1 identifies samples as Luminal A or Luminal B.
  • Algorithm 12 identifies samples as Luminal A or Luminal B.
  • the method classifies the cancer as a HER2+ cancer or a HER2 negative cancer, optionally wherein the method comprises the determination of the level of expression of the ERBB2 gene, optionally further comprises normalisation of the level of expression of the ERBB2 gene to the expression levels of ACTB; PPIB; HPRT1 ; and TBP, optionally further comprise the use of any one or more of Algorithm 1 , Algorithm 3, Algorithm 4 and Algorithm 5 as defined above.
  • the method classifies the cancer as an ER+ cancer or an ER negative cancer, optionally wherein the method comprises the determination of the level of expression of the ESR1 gene, optionally further comprises normalisation of the level of expression of the ESR1 gene to the expression levels of ACTB; PPIB; HPRT1 ; and TBP, optionally further comprise the use of any one or more of Algorithm 2, Algorithm 3, Algorithm 4 and Algorithm 6 as defined above.
  • the method classifies the cancer as a HER2+, ER+ or TNBC, optionally wherein the method comprises the determination of the level of expression of the ERBB2, ESR1 , AURKA and/or KIF2C genes, optionally further comprises normalisation of the level of expression of the ERBB2, ESR1 , AURKA and/or KIF2C genes to the expression levels of ACTB; PPIB; HPRT1 ; and TBP, optionally further comprise the use of any one or more of Algorithm 3, Algorithm 4 and Algorithm 6 as defined above.
  • the method classifies the cancer as a Basal cancer or a Luminal cancer, optionally wherein the method comprises the determination of the level of expression of the ERBB2 and ESR1 genes, optionally further comprises normalisation of the level of expression of the ERBB2 and ESR1 genes to the expression levels of ACTB; PPIB; HPRT1 ; and TBP, optionally further comprise the use of Algorithm 7 as defined above.
  • the method classifies the cancer as a Basal cancer or a Luminal cancer, optionally wherein the method comprises the determination of the level of expression of the ERBB2, ESR1 , PGR, AURKA, KIF2C and FOXC1 genes, optionally further comprises normalisation of the level of expression of the ERBB2, ESR1 , PGR, AURKA, KIF2C and FOXC1 genes to the expression levels of ACTB; PPIB; HPRT1 ; and TBP, optionally further comprise the use of Algorithm 8 as defined above.
  • the method classifies the cancer as a Luminal A or a Luminal B cancer, optionally wherein the method comprises the determination of the level of expression of the ERBB2, ESR1 , PGR and FOXC1 genes, optionally further comprises normalisation of the level of expression of the ERBB2, ESR1 , PGR and FOXC1 genes, to the expression levels of ACTB; PPIB; HPRT1 ; and TBP, optionally further comprise the use of Algorithm 9 as defined above.
  • the method classifies the cancer as a Luminal A or a Luminal B cancer, optionally wherein the method comprises the determination of the level of expression of the ERBB2, ESR1 , PGR, AURKA, KIF2C and FOXC1 genes, optionally further comprises normalisation of the level of expression of the ERBB2, ESR1 , PGR, AURKA, KIF2C and FOXC1 genes, to the expression levels of ACTB; PPIB; HPRT1 ; and TBP, optionally further comprise the use of Algorithm 10 as defined above.
  • the method classifies the cancer as a Luminal A or a Luminal B cancer, optionally wherein the method comprises the determination of the level of expression of the PGR, AURKA, KIF2C and FOXC1 genes, optionally further comprises normalisation of the level of expression of the PGR, AURKA, KIF2C and FOXC1 genes, to the expression levels of ACTB; PPIB; HPRT1 ; and TBP, optionally further comprise the use of Algorithm 1 1 as defined above.
  • the method classifies the cancer as a Luminal A or a Luminal B cancer, optionally wherein the method comprises the determination of the level of expression of the PGR and FOXC1 genes, optionally further comprises normalisation of the level of expression of the PGR and FOXC1 genes, to the expression levels of ACTB; PPIB; HPRT1 ; and TBP, optionally further comprise the use of Algorithm 12 as defined above.
  • the method classifies the cancer as HER2+, ER+, TNBC, Luminal, Basal, Luminal A and Luminal B by a) Identifying the HER2+ class as defined diagnostically by IHC (accuracy compared to IHC/FISH positive cases); b) Define between Luminal and Basal (concordance with PAM50); c) Luminal subclassification into Luminal A or Luminal B (prognostic significance); wherein part a) provides information to further classify the clinically actionable classes: HER+, ER+ and TNBC.
  • the method of the invention is able to classify a breast cancer as either HER2+, ER+, Basal, Luminal A, Luminal B or TNBC, optionally wherein the method comprises the determination of the level of expression of the ERBB2, ESR1 , PGR, AURKA, KI F2C and FOXC1 genes, optionally further comprises normalisation of the level of expression of the ERBB2, ESR1 , PGR, AURKA, KIF2C and FOXC1 genes, to the expression levels of ACTB; PPIB; HPRT1 ; and TBP, optionally further comprise the use of the Algorithms as defined above.
  • Suitable combinations of algorithms are as follows: a) Algorithm 1 and Algorithm 2 together; or Algorithm3; or Algorithm 4; or Algorithm 6; to classify the ER+, HER2+ and TNBC diagnostic classification.
  • a preferred algorithm is Algorithm 4 (see Table 4).
  • the HER2+ diagnostic class (by IHC/FISH) can be defined by Algorithm 1 or 5
  • a preferred algorithm is Algorithm 1 .
  • the ER+ diagnostic class by IHC can be defined by Algorithm 2.
  • the ER+ and TNBC case set (for example as defined by any of algorithms 1 and 2; 3 or 4) or the HER2 negative case set (for example as defined by any of algorithms 1 or 5) can be re-classified into the molecular classes Luminal and Basal using Algorithms 7 or 8.
  • a preferred algorithm is Algorithm 8 (see Table 10).
  • the Luminal class (as defined in (c) above) can be re-classified into Luminal A and Luminal B using any of Algorithms 9-12.
  • a preferred algorithm is Algorithm 10 (see Table 12).
  • a preferred sequential application of algorithms to expression data obtained from a single sample is as follows (summarised in Figure 4):
  • the method of the invention is considered to be an in vitro method, wherein the method is performed on a sample obtained from a subject.
  • the method is suitable for in vivo use and in vivo detection of the cancer and/or sub-class of cancer, for example where the means required for performing the method are incorporated into an implantable sensor.
  • Such in vivo use is also encompassed by the present invention and the method of the invention is in one embodiment an in vivo method.
  • the method of the invention in an in vitro method.
  • RNA intact or degraded
  • Data provided herein demonstrate that the method of the invention can classify breast cancer with a high degree of accuracy when the sample is a Hematoxylin and Eosin stained sample and/or is a sample comprising totally degraded RNA, i.e. a sample with RNA with a RIN value of around 2.0.
  • Data also provided herein demonstrates that the use of the method with a sample comprising exosomes is appropriate.
  • the sample may be from any organism, for example any mammal, for example a human, a dog, a primate, cattle or any virus and other microorganisms.
  • the sample is from a human.
  • the sample may be a fresh sample, for example may be a fresh biopsy of a tumour, or may be a fresh blood or plasma sample.
  • the sample may be a sample that comprises exosomes.
  • the sample may also be not a fresh sample, for example may be an archived sample, for example a sample that has been stained and/or frozen and/or fixed for example fixed with formalin and/or embedded for example embedded in paraffin.
  • the sample may be a tissue sample obtained from a subject; a cell line; a liquid biopsy, for example a blood sample or a plasma sample.
  • the liquid biopsy comprises circulating tumour cells.
  • the liquid biopsy comprises exosomes.
  • the sample is a homogenate or lysate, for example a homogenate or lysate of a homogenous tumour.
  • the sample is a homogenate or lysate of a heterogeneous tumour.
  • the sample is an archived/historical sample, for example the sample is between 1 month-100 years old, optionally wherein the sample is at least 1 month old, optionally wherein the sample is at least 2 months old, or at least 3 months old, or at least
  • the sample is a fresh sample, for example the sample is less than 1 month old, optionally less than 4 weeks old, optionally less than 21 days old, optionally less than 14 days old, optionally less than 7 days old, optionally less than 6 days old, optionally less than 5 days old, optionally less than 4 days old, optionally less than 3 days old, optionally less than 2 days old optionally less than 1 day old, optionally less than 18 hours old, optionally less than 12 hours old, optionally less than 6 hours old, optionally less than 5 hours old, optionally less than 4 hours old, optionally less than 3 hours old, optionally less than 2 hours old, optionally less than 1 hour old, optionally less than 30 minutes old, optionally less than 15 minutes old, optionally less than 10 minutes old, optionally less than
  • the present invention involving the use of bDNA technology, is able to accurately classify cancer samples even when the sample comprises degraded RNA.
  • the sample comprises RNA, including degraded RNA, for example wherein the sample has a RIN of 8 or less, for example less than 7.5, for example less than 7.0, for example less than 6.5, for example less than 6.0, for example less than 5.5, for example less than 5.0, for example less than 4.5, for example less than 4.0, for example less than 3.5, for example less than 3.2 , for example less than 3.0, for example less than 2.8, for example less than 2.6, for example less than 2.4, for example less than 2.2, for example less than 2.0, for example less than 1 .8, for example less than 1 .6, for example less than 1 .4, for example less than 1.2, for example 1 .0.
  • the skilled person will be aware of the RIN parameter, for example it is discussed in https://www.aqilent
  • the method of the present invention is not affected by formalin fixation of paraffin embedding.
  • the sample has been formalin-fixed (FF).
  • the sample has been paraffin- embedded (PE).
  • the sample is a formalin-fixed paraffin- embedded sample (FFPE).
  • the method of the present invention is not affected by staining, for example is not affected by Haematoxylin and Eosin staining.
  • the sample is a stained sample, for example is a Haematoxylin and Eosin stained sample.
  • tumours are often heterogeneous, often comprising numerous individual morphologically distinct tumours.
  • a breast cancer sample may contain several different tumours within it, for example may comprise both HER2+ and HER2 negative tumours.
  • analysis of the entire tumour sample is considered to be useful, it is considered more useful if the individual tumours can be isolated and assessed separately. For example, analysing a whole tumour sample that actually contains both HER2+ and HER2- microtumours may give an overall HER2+ phenotype. This may direct the clinician to administer Herceptin therapy. Whilst Herceptin may be effective against the HER2+ microtumour, the HER2 negative microtumour is unlikely to be affected and may continue to grow unchecked.
  • an advantage of the fact that the present invention is not affected by staining is that it allows the staining of a sample, for example with Haematoxylin and Eosin, and the dissection of the individual morphologically distinct tumours followed by direct determination of RNA expression level, according to the present invention, classification of each individual microtumour that was isolated and analysed.
  • a sample for example with Haematoxylin and Eosin
  • the skilled person will be well aware of suitable techniques for staining the samples and subsequent identification and isolation of morphologically distinct tumours.
  • the sample is a laser dissected sample or a macro dissected sample.
  • the sample is a sample of a morphologically distinct tumour within a larger tumour.
  • the present invention has the advantage of being able to detect morphologically distinct tumours of different sub-classes.
  • the invention further provides a method for the detection of heterogeneity in a tumour.
  • a method for the detection of heterogeneity in a tumour involves the detection of the RNA expression level of particular genes, for instance the ERBB2 and ESR1 gene, via bDNA technology, for example via the QuantiGene Plex Assay, wherein the expression levels have been normalised to ACTB, PPIB, HPRT1 , and TBP.
  • the method comprises performing a method according to the first aspect of the invention separately on more than one sample obtained from the tumour.
  • samples may be taken from several different sites in the tumour.
  • the advantages of the present invention which include a rapid and accurate assessment of cancer type or cancer sub-class permits the routine classification of multiple samples from the same tumour. The skilled person can then readily determine if a subject has a tumour that has heterogeneity, and what that heterogeneity is, for example if it is a tumour that has FIER2+ and FIER2 negative microtumours, or both Luminal and Basal microtumours. It is considered that such an approach would greatly inform the clinical practitioner’s therapeutic strategy.
  • the present invention also provides the use of bDNA technology to predict the presence of a gene amplification.
  • kits that are suitable for use in the methods of the present invention.
  • the invention provides a kit for use in one or more methods of the invention, said kit comprising bDNA probes directed towards at least two of: ERBB2, ESR1 , PGR, AURKA, KIF2C, FOXC1 , ACTB, PPIB, HPRT1 , and TBP.
  • the kit may contain bDNA probes directed towards ERBB2 and ACTB, PPIB, HPRT1 , and TBP; or ESR1 and ACTB, PPIB, HPRT1 , and TBP; or ERBB2, ESR1 and ACTB, PPIB, HPRT1 , and TBP; or all of ERBB2, ESR1 , PGR, AURKA, KIF2C, FOXC1 , ACTB, PPIB, HPRT1 , and TBP.
  • the kit may additionally or alternatively contain bDNA probes directed towards any two or more of ACTB, PPIB, HPRT1 , and TBP; any three or more of ACTB, PPIB, HPRT1 , and TBP; or all four of ACTB, PPIB, HPRT1 , and TBP.
  • the kit may additionally or alternatively contain bDNA probes directed towards:
  • the kit may additionally or alternatively contain bDNA probes to AURKA and/or KIF2C.
  • the kit may also additionally or alternatively contain means for staining the sample to identify morphologically distinct tumours within the sample, for example may contain Haematoxylin & Eosin.
  • kits described above are also for use in the diagnosis or prognosis of cancer.
  • the present invention also provides the use of any or part of the kits as described above in a method of manufacture of a composition for use in diagnosing or prognosing cancer.
  • the present methods are suitable for use on archived samples which means that a wealth of data can be mined using the present invention.
  • the invention also therefore provides a method of validating a potential gene amplification or gene reduction as a biomarker, the method comprising the use of any one or more of the methods of the invention.
  • the present invention can be used in a method of diagnosis or prognosis.
  • the methods described herein can be used to diagnose cancer, for example to diagnose breast cancer or can be used to diagnose a particular sub-class of cancer, for example to diagnose a subject has having a HER2+ breast cancer and/or an ER+ breast cancer and/or a TNBC and/or a Basal breast cancer and/or a Luminal breast cancer, for example a Luminal A or a Luminal B breast cancer.
  • the methods described herein can be used in a method for aiding in determining the prognosis of a subject, for example where a subject has been diagnosed with breast cancer
  • the methods of the invention can be used to aid in determining the prognosis of the subject by classifying the sub-type or sub-types of cancer that the subject has.
  • the skilled person will be aware that a diagnosis of Basal breast cancer is associated with a worse prognosis than HER2-enriched, for example, and that HER2-enriched is associated with a worse prognosis than Luminal B, for example, and that Luminal B is associated with a worse prognosis than Luminal A.
  • TNBC is associated with a worse prognosis than HER2+
  • HER2+ is associated with a worse prognosis than ER+.
  • the present invention is suitable for research use. Accordingly in one embodiment the methods of the present invention are for research use.
  • the present invention provides a method of selecting a suitable treatment strategy wherein the method comprises any of the methods of the invention as described above.
  • a diagnosis of a HER2+ breast cancer can be used to select the subject for treatment with Herceptin (trastuzumab), Kadcyla (Herceptin and emtansine), Nerlynx (neratinib), Perieta (pertuzumab), and/or Tykerb (lapatinib);
  • a diagnosis of ER+ breast cancer can be used to select the subject for treatment with Tamoxifen, Aromatase Inhibitors, and/or SERMs; and a diagnosis of Basal breast cancer can be used to select the subject for treatment with other chemotherapeutic agents.
  • the invention also provides Herceptin (trastuzumab), Kadcyla (Herceptin and emtansine), Nerlynx (neratinib), Perieta (pertuzumab), and/or Tykerb (lapatinib) for use in the treatment of a subject with breast cancer wherein a sample from the subject has been identified as HER2+ by use of any of the preceding methods.
  • the invention provides a method for treating HER2+ cancer wherein the subject has been diagnosed as having a HER2+ cancer or microtumour, wherein said method comprises administration of any one or more of Herceptin (trastuzumab), Kadcyla (Herceptin and emtansine), Nerlynx (neratinib), Perieta (pertuzumab), and/or Tykerb (lapatinib) to said subject.
  • Herceptin tacuzumab
  • Kadcyla Herceptin and emtansine
  • Nerlynx neratinib
  • Perieta pertuzumab
  • Tykerb lapatinib
  • the invention also provides Tamoxifen, Aromatase Inhibitors, and/or SERMs for use in the treatment of a subject with breast cancer wherein a sample from the subject has been identified as ER+ by use of any of the preceding methods.
  • the invention also provides various algorithms that have been trained on different datasets. Preferences for the algorithms as given above, for instance a preferred algorithm is the Neural Network algorithm. Accordingly the invention provides any one or more of Algorithms 1 -12 as defined above. In one embodiment the algorithm is the Neural Network algorithm.
  • the invention also provides a method of treating a patient having a cancer comprising an overexpression of AURKA and/or KIF2C, comprising administering to the patient a PP2A activator and thereby treating the cancer, wherein the overexpression of AURKA and/or KIF2C has been determined by use of any of the methods disclosed herein.
  • the invention further provides a method of treating cancer in a patient, the method comprising administering an AURKA antagonist and/or a KIF2C antagonist to the patient and thereby treating the cancer wherein the patient has a cancer with an overexpression of AURKA and/or KIF2C, wherein the overexpression of AURKA and/or KIF2C has been determined by use of any of the methods disclosed herein.
  • the invention additionally provides a method of treating cancer in a patient, or predicting sensitivity of a cancer to PP2A activators, the method comprising (a) measuring the amount of AURKA and/or KIF2C RNA in the cancer according to any of the methods described herein and optionally (b) if the cancer comprises an overexpression of AURKA and/or KIF2C, administering to the patient a PP2A activator and thereby treating the cancer.
  • the invention also provides a PP2A activator for use in treating cancer in a patient, wherein the cancer comprises an overexpression of AURKA and/or KIF2C, wherein the overexpression of AURKA and/or KIF2C has been determined by use of any of the methods disclosed herein, and use of a PP2A activator in the manufacture of a medicament for treating cancer in a patient, wherein the cancer comprises an overexpression of AURKA and/or KIF2C wherein the overexpression of AURKA and/or KIF2C has been determined by use of any of the methods disclosed herein.
  • the invention further provides an AURKA antagonist and/or a KIF2C antagonist for use in treating cancer in a patient, and use of an AURKA antagonist and/or a KIF2C antagonist in the manufacture of a medicament for treating cancer in a patient wherein the cancer has an overexpression of AURKA and/or KIF2C, wherein the overexpression of AURKA and/or KIF2C has been determined by use of any of the methods disclosed herein.
  • the invention additionally provides a method for detecting cancer in a patient, the method comprising measuring expression of AURKA and KIF2C in the patient in accordance with any of the methods described herein, wherein overexpression of AURKA and KIF2C indicates that the patient comprises a cancer.
  • the invention further provides a method for prognosing a cancer in a patient, the method comprising determining whether or not the cancer comprises an overexpression of AURKA and/or KIF2C in accordance with any of the methods described herein, wherein an overexpression of AURKA and/or KIF2C in the cancer indicates that the patient has a worse prognosis than in the situation of normal expression of AURKA and/or KIF2C.
  • the invention also provides a method for determining whether or not a patient having or suspected of having or being at risk of developing cancer will respond to treatment with a PP2A activator, which method comprises measuring expression of AURKA and/or KIF2C in the individual in accordance with any of the methods described herein, and thereby predicting whether or not the patient will respond to treatment with a PP2A activator.
  • the invention further provides a method for classifying a cancer in a patient, the method comprising measuring expression of AURKA and/or KIF2C in the patient in accordance with any of the methods described herein, and classifying the cancer as of a particular subtype based on the expression.
  • the invention also provides a kit for detecting a cancer comprising a deregulation of PP2A, comprising reagents suitable for detecting expression of AURKA and/or KIF2C.
  • the invention also provides a system for detecting, classifying or prognosing cancer in a patient, or for predicting responsiveness of a cancer patient to treatment with a PP2A activator, the system comprising: (a) a measuring module for determining expression of AURKA and/or KIF2C in the patient, (b) a storage module configured to store control data and output data from the measuring module, (c) a computation module configured to provide a comparison between the value of the output data from the measuring module and the control data; and (d) an output module configured to display whether or not the patient has cancer based on the comparison, wherein an overexpression of AURKA and/or KIF2C in the patient indicates the presence of cancer, classifies the cancer, indicates a worse prognosis of cancer, or predicts that the patient will respond to treatment with a PP2A activator.
  • N 960 where 147 (15.31 %) cases show ERBB2 transcript overexpression while only 1 19 (12.4%) cases show ERBB2 gene amplification.
  • ERBB2 raw expression data against HER2 positive total RNA input derived from BT474 cell line [A] LX200 - shows saturation at approximately 100ng of input RNA [B] Magpix - shows signal saturation at approximately 180ng of RNA.
  • Quantigene results of Breast Cancer receptor status and normalising genes across a range of RNA concentration The x-axis represents the concentration of RNA (pg/ml) and the y-axis represent the Mean Fluorescence Intensity (MFI) measured using the Magpix Luminex instrument.
  • MFI Mean Fluorescence Intensity
  • Gene expression was measured using the breast cancer cell line BT474, positive for both oestrogen receptor and HER2 (ERBB2).
  • [B] zooms into the low MFIs to show the oestrogen receptor expression and normalising genes.
  • PCA Principal component analysis
  • RNA Integrity numbers and concentration for a ladder MDAMB453 RNA and degraded RNA controls, a BT474 RNA degradation array and 3 RNA samples extracted from patient derived breast cancer from FFPE blocks.
  • RNA degradation gradient Normalised ERBB2 expression profile across degraded BT474 RNA samples for Relative expression between different genes in the RNA profile is maintained across the RNA degradation gradient.
  • Expression Data from FFPE patient data [Localdata] is filtered for outliers using the Filter Examples operator.
  • ERBB2 expression data and HER2 diagnostic (IHC/FISH) status is retrieved by the Select Attributes operator.
  • the Set Role operator defines the HER2 diagnostic status as the variable to predict.
  • the streamlined data is used to train the Neural Network operator which is applied using the Apply model operator to the BT474 RNA degradation series data that is processed in an identical fashion.
  • the output from the Apply model operator provides a label for the unlabelled data along with a confidence value for the prediction.
  • tumour heterogeneity Morphologically distinct tumours were microdissected and treated as distinct samples.
  • [A] shows the master scan of the FI&E section.
  • [F] Normalised expression levels for the ESR1 gene in each tumour showing relatively high and equal expression between tumours as expected from the immunohistochemical result;
  • TNBC basal and triple negative breast cancer
  • the adjacent table shows a statistically significant difference in expression between sensitive and non-sensitive cell lines for AURKA and KIF2C gene expression.
  • Statistical analysis was done using the Mann-Whitney U test and a p-value smaller than 0.05 was considered significant.
  • the expression of CIP2A, AURKA and KIF2C are significantly higher in the FTY720-senstive cell lines (p values of ⁇ 0.05, ⁇ 0.02, ⁇ 0.001 respectively).
  • Sensitive cell lines thus include the TNBC cell lines MDAMB231 , BT- 20 and Hs578T, while non-sensitive cell lines include HCC1937, MDAMB436, MDAMB468, MDAMB453, BT-474, MCT-7 and SKBR-3.
  • Percent patients with amplifications or an expression level (z-score) greater than 2 (AMP; EXP>2) for AURKA (white bars) and KIF2C (black bars) in tumours of different origin.
  • the analysis of data was done using the data portal (cBioPortal for Cancer Genomics (Cerami et al., 2012), available at http://www.cbioportal.org.
  • the normalised RNASeqV2 data was used from the TCGA dataportal (https://tega-data.nci.nih.gov/tcga/).
  • the x-axis represents the expression in exosomes derived from COLO320 and the y-axis the expression measured using RNA from originator cells.
  • AURKA Log-io
  • HER2 expression of AURKA (Log-io) in cell line derived exosomes can be used to normalise and characterise HER2 expression into positive or negative.
  • a method of treating a patient having a cancer comprising an overexpression of AURKA and/or KIF2C comprising administering to the patient a PP2A activator and thereby treating the cancer.
  • a method of treating cancer in a patient comprising administering an AURKA antagonist and/or a KIF2C antagonist to the patient and thereby treating the cancer. 4. The method according to embodiment 3, further comprising administering a PP2A activator.
  • HER2, ER and PR selected for said treatment on the basis of the cancer having an underexpression of one or more of HER2, ER and PR.
  • the AURKA antagonist and/or KIF2C antagonist is a small molecule, a protein, an antibody, a polynucleotide, an oligonucleotide, an antisense RNA, a small interfering RNA (siRNA) or a small hairpin RNA (shRNA). 13. A method according to any one of the preceding embodiments, wherein the PP2A activator
  • the AURKA antagonist and/or KIF2C antagonist is administered in combination with another cancer therapy.
  • a method of treating cancer in a patient comprising (a) measuring the amount of AURKA and/or KIF2C in the cancer and (b) if the cancer comprises an overexpression of AURKA and/or KIF2C, administering to the patient a PP2A activator and thereby treating the cancer.
  • An AURKA antagonist and/or a KIF2C antagonist for use in treating cancer in a patient 17.
  • PP2A activator in the manufacture of a medicament for treating cancer in a patient, wherein the cancer comprises an overexpression of AURKA and/or KIF2C.
  • a method for detecting cancer in a patient comprising measuring expression of AURKA and KIF2C in the patient, wherein overexpression of AURKA and KIF2C indicates that the patient comprises a cancer.
  • a method for prognosing a cancer in a patient comprising determining whether or not the cancer comprises an overexpression of AURKA and/or KIF2C, wherein an overexpression of AURKA and/or KIF2C in the cancer indicates that the patient has a worse prognosis than in the situation of normal expression of AURKA and/or KIF2C. 22.
  • a method for determining whether or not a patient having or suspected of having or being at risk of developing cancer will respond to treatment with a PP2A activator, which method comprises measuring expression of AURKA and/or KIF2C in the individual, and thereby predicting whether or not the patient will respond to treatment with a PP2A activator.
  • a method for classifying a cancer in a patient comprising measuring expression of AURKA and/or KIF2C in the patient, and classifying the cancer as of a particular subtype based on the expression.
  • HER2, ER and PR to further classify the cancer.
  • a kit for treating cancer comprising (a) one or more reagents suitable for measuring expression of AURKA and/or KIF2C and (b) a PP2A activator.
  • kits for detecting a cancer comprising a deregulation of PP2A, comprising reagents suitable for detecting expression of AURKA and/or KIF2C. 31.
  • kit according to embodiment 30 or 31 further comprising reagents suitable for detecting expression of an endogenous inhibitor of PP2A or a PP2A subunit.
  • detecting expression are selected from nucleic acid probes or primers and antibodies.
  • a system for detecting, classifying or prognosing cancer in a patient, or for predicting responsiveness of a cancer patient to treatment with a PP2A activator comprising:
  • an output module configured to display whether or not the patient has cancer based on the comparison
  • an overexpression of AURKA and/or KIF2C in the patient indicates the presence of cancer, classifies the cancer, indicates a worse prognosis of cancer, or predicts that the patient will respond to treatment with a PP2A activator.
  • the invention provides a method for classifying a cancer as a Luminal A or a Luminal B cancer, and involves the determination of the level of expression of the ERBB2, ESR1 , PGR, AURKA, KIF2C and FOXC1 genes in a formalin- fixed paraffin-embedded sample (FFPE) of a morphologically distinct tumours identified in a sample obtained from a patient wherein the sample is 5 months old.
  • FFPE formalin- fixed paraffin-embedded sample
  • This exercise aims to identify the dynamic range for HER2 on both the LX200 and Magpix. This establishes the ideal RNA concentration for the detection of these expressions (linear phase).
  • a multi-plex Luminex based RNA assay was used to study the expression of HER2 (ERBB2) and ESR1 (oestrogen receptor) across the various concentrations to assess sensitivity of the assay. Normalising genes were included to select the best combination to ensure proper threshold settings for the simultaneous measurement of the lowly expressing ESR1 and the amplified HER2 gene.
  • the normalising genes ACTB and GAPDH are expressed at high levels using the cell line BT474 and at a concentration of 8 pg/ml (400ng total RNA input) the signal reaches saturation and hence can result in false negative when normalising the HER2 (ERBB2) expression.
  • concentration should not exceed 4pg/ml (200ng input RNA).
  • GAPDH shows a significantly lower expression in patient material with some extreme outliers, becoming among the lowest expressing normalising gene within the analysed normalising geneset. GAPDH does not follow the same trends as the other normalising genes analysed and thus was excluded from further analysis.
  • the normalising genes ACTB, PPIB, HPRT1 and TBP were selected to normalise the MFI. This allowed us to normalise the lowly expressed oestrogen receptor gene and the highly expressed amplified HER2 in one run.
  • Quality control starts by assessing the bead count per region. If the bead count is less than 30, sample is regarded as inadequate and a repeat would be necessary. Low bead counts are generally caused by bead clumping which can be avoided by obtaining a cleaner sample (less paraffin and tissue fragments) or by diluting the sample.
  • LOD Limit of Detection
  • Normalising gene data is expressed as a log base 10, resulting in a normally distributed dataset.
  • TNBC breast cancer classes
  • ER+ and HER2+ can be predicted using ESR1 and ERBB2 expression data normalised to 4 normalising genes (ACTB, PPIB, HPRT1 , and TBP) with up to 98.78% accuracy using the Neural network algorithm (1 out of 82 cases is misclassified).
  • Table 4 Crosstab of Breast cancer classification defined by IHC and FISH compared to Neural Network prediction based on ESR1, ERBB2 and AURKA and/or KIF2C normalised expression.
  • HER2 cases were selected using the Neural Network algorithm based on the ERBB2 gene expression.
  • Other algorithms such as Decision Tree, Random Forest and Support Vector Machine can be applied here with as similar prediction (Respective concordance of 95.0%, 95.3% and 94.0%).
  • Class recall 99.1% 74.3% Table 6 Crosstab of Breast cancer classification of HER2 status defined by IHC and FISH compared to Neural Net prediction based on the normalised expression of ERBB2 and shown as pred. HER2 negative and pred. HER2 positive. Classification concordance of 94.76%, N-401 Class
  • HER2 status as determined by IHC and FISH can be predicted perfectly by Neural Network based on the expression of ERBB2 normalised expression.
  • a Neural Network model trained on available dataset can be used to predict status of unknown cases.
  • Table 9 Crosstab of the molecular classification of breast cancer with PAM50 as reference. A Neural Net prediction based on the normalised expression of ESR1 and ERBB2 is shown as pred. Luminal and pred. Basal. Classification concordance of 97.25%
  • the algorithm When including also PGR, AURKA, KIF2C and FOXC1 normalised expression as predicting variables, the algorithm provides a more accurate classification of Luminal and Basal patients (Table 10).
  • Table 10 Crosstab of the molecular classification of breast cancer with PAMSO as reference. A Neural Net prediction based on the normalised expression of ESRl, ERBB2, PGR, AURKA, KIF2C and F0XC1 is shown as pred. Luminal and pred. Basal. Classification concordance of 98.62%
  • Algorithm 9 Gene signature: ERBB2, ESR1 , FOXC1 , PGR (Normalised to ACTB, HPRT1 and TBP)
  • Table 11 Crosstab of the Luminal classification of breast cancer with PAM50 as reference. A Neural Net prediction based on the normalised expression of ESRl, ERBB2, PGR and FOXC1 is shown as pred. Luminal A and pred. Luminal B. Classification concordance of 76.14%, N-285
  • Algorithm 10 Neural Net Prediction using gene signature: ERBB2, ESR1, FOXCl, PGR, AURKA, KIF2C (Normalised to ACTB, HPRT1 and TBP)
  • Algorithm 11 Gene signature: PGR, FOXC1 , AURKA and KIF2C (Normalised to ACTB, HPRT1 and TBP)
  • Table 13 Crosstab of the Luminal classification of breast cancer with PAM50 as reference. A Neural Net prediction based on the normalised expression of AURK A, KIF2C, PGR and F0XC1 is shown as pred. Luminal A and pred. Luminal B. Classification concordance of 8632%, N-285
  • Algorithm 12 Gene signature: PGR and FOXC1 (Normalised to ACTB, HPRT1 and TBP)
  • Table 14 Crosstab of the Luminal classification of breast cancer with PAM50 as reference. A Neural Net prediction based on the normalised expression of PGR and FOXC1 is shown as pred. Luminal A and pred. Luminal B. Classification concordance of 75.09%, N-285
  • Algorithm 1 trained on normalised expression of ERBB2 in:
  • a sample set comprising HER2+ samples as defined by IHC and FISH;
  • a sample set comprising HER2+ and/or ER+ samples as defined by IHC and FISH; and a sample set not comprising HER2+ and ER+ samples as defined by IHC and FISH;
  • Algorithm 4 trained on normalised expression of ESR1 , ERBB2, AURKA and/or KIF2C in: a sample set comprising HER2+ and/or ER+ samples as defined by IHC and FISH; and a sample set not comprising HER2+ and/or ER+ samples as defined by IHC and FISH;
  • Algorithm 6 trained on normalised expression of ERBB2 and ESR1 in samples in a dataset from the TCGA, comprising
  • HER2+ and/or ER+ samples as defined by IHC and FISH;
  • Algorithm 7 trained on normalised expression of ERBB2 and ESR1 in samples in a dataset from the TCGA which had HER2-enriched samples as defined by PAM50 and HER2+ as defined by IHC/FISH removed.
  • Algorithm 8 trained on normalised expression of ERBB2, ESR1 , PGR, AURKA, KIF2C and FOXC1 in samples in a dataset from the TCGA which had HER2-enriched samples as defined by PAM50 and HER2+ samples as defined by IHC/FISH removed.
  • Algorithm 9 trained on normalised expression of ERBB2, ESR1 , FOXC1 , PGR normalised expression in:
  • Algorithm 10 trained on normalised expression of ERBB2, ESR1 , FOXC1 , PGR, AURKA and KIF2C in:
  • Algorithm 1 identifies samples as HER2 positive or HER2 Negative.
  • Algorithm 2 identifies samples as ER positive or ER Negative.
  • Algorithm 3 identifies samples as HER2 positive and/or ER positive or TNBC.
  • Algorithm 4 identifies samples as HER2 positive and/or ER positive or TNBC.
  • Algorithm 5 identifies samples as HER2 positive or HER2 Negative.
  • Algorithm 6 identifies samples as HER2 positive and/or ER positive or TNBC.
  • Algorithm 7 identifies samples as Luminal or Basal.
  • Algorithm 8 identifies samples as Luminal or Basal.
  • Algorithm 9 identifies samples as Luminal A or Luminal B.
  • Algorithm 10 identifies samples as Luminal A or Luminal B.
  • Algorithm 1 1 identifies samples as Luminal A or Luminal B.
  • Algorithm 12 identifies samples as Luminal A or Luminal B
  • a Neural Net algorithm (as described in Algorithm section Example 2) has been trained based on a subset of breast cancer patients that have been previously analysed and classified into Luminal, Basal and HER2-Enriched by PAM50 (94.81 % overall concordance).
  • the reference classification was defined using a patented 12-gene signature that was validated on Quantigene 2.0 (W02009055823).
  • a Neural Net algorithm trained using 6-gene Quantigene data ERBB2, ESR1 , AURKA, KIF2C, FOXC1
  • the 6-gene signature presented here can be applied to the TCGA breast cancer data to predict PAM50 with 95.03% concordance when using the Neural Net algorithm.
  • Normalisation using various normalising genes shows that a combination of 4 normalising genes (ACTB, PPIB, HPRT1 and TBP) will allow a 100% concordance of HER2 overexpression when compared to IHC and FISH results.
  • RNA degradation was performed by heating RNA at 95°C (as suggested by TATAA Biocenter - AIT 2016) over the following time points:
  • RNA degradation was significant at 5 minutes until complete degradation at 30 minutes where it becomes comparable to degradation seen in tissue FFPE blocks, also in terms of RIN ( Figure 12).
  • RAW data is defined by MFI data with subtracted the blank average and any signals below (blank average + 3SDs) are indicated as 0 (undetected)
  • FFPE-cell line blocks were produced to represent well annotated cell lines that are formalin fixed, and embedded in paraffin. Using a 12-plex Quantigene assay, normalising genes and classifier genes are measured in the FFPE-cell blocks to provide quality control material for the patient classification runs.
  • Table 17 Neural Network Predicted HER2 status based on Breast Cancer cell line ( BCCL ) FFPE compared with the actual HER2 status confirmed by IHC.
  • RNA based assays using archival formalin-fixed paraffin-embedded (FFPE) material is challenging due to variability in surgical tissue processing and degradation of RNA caused by tissue integrity preservation using formalin 1 ⁇ 2 .
  • our group used the branched-chain DNA (bDNA) multiplex magnetic bead assay.
  • bDNA technology replaces enzymatic amplification of target template with hybridisation of specific probes and amplification of a reporter signal 3 .
  • the short recognition sequences of the capture and detection probes are designed to hybridise to short fragments of target RNA 4 .
  • tissue homogenates directly as starting material of this assay overcomes the inevitable loss of RNA occurring in assays requiring prior RNA extraction and purification. Signal amplification, the use of short recognition sequences and the lack of a purification step, contribute to reduce technical variation of the assay.
  • the technology provides the possibility to multiplex the assay (up to 80 RNA targets), measuring expression of a panel of targets from low material input.
  • This protocol describes the preparation and staining of tissue samples for laser microdissection. Staining on membrane slides facilitate imaging of tumour and histological architecture providing accurate selection and profiling of (1 ) tumour and normal ducts in breast tissue and (2) malignant cell clones within heterogeneous tumours.
  • HER2 human epidermal growth factor receptor 2
  • the HER2-enriched subtype is well defined, with high expression of HER2 receptor, due to the ERBB2 gene amplification, combined with low or absent ER and PgR.
  • Luminal subtype is generally positive for oestrogen receptor (ER) and the Basal type tumours are in general negative for the 3 receptors significantly overlapping with the triple negative breast cancer (TNBC) diagnostic subtype 5 ⁇ 6 .
  • TNBC triple negative breast cancer
  • Fibronectin is a main component of breast tissue mesenchymal compartment. Increased FN1 expression is accompanied by high Ki67 staining showing a signature for a more invasive tumour 7 8 and an increased expression is associated with metastasis 9 .
  • FN1 was found to be present in microvesicles originating from tumour cells, that induced activation of mitogenic signals in recipient fibroblasts 10 .
  • circulating microvesicles such as exosomes are potentially vehicles of early detection and early indicators of metastasis and relapse 11 .
  • Tumour area selection for breast cancer transcriptional subtyping has recurrently been done by macrodissection 12 13 .
  • tissue heterogeneity and increase sensitivity we have reliably combined classical tissue staining with multiplex molecular profiling methods.
  • two distinct breast cancer clones have been defined by their epithelial mesenchymal signature and metastatic potential.
  • the workflow of the described protocol can be easily translated to the current clinical setup and used to selectively isolate and characterise tissue subtypes using targeted mRNA profiling.
  • the described method has been applied for the simultaneous measurement of 40 transcripts in H&E stained ( Figure 16), microdissected (Figure 17) highly degraded FFPE material.
  • Figure 16 we show accurate characterisation of receptor status (Figure 18A), classification of tumours into luminal and basal molecular subtypes 17 and differential expression of the mesenchymal marker, FN1 when comparing tumour and matched control tissue ( Figure 18B), in the various receptor positive and negative subtypes.
  • a bead-based multiplex branched DNA (bDNA) assay has been optimised to quantify gene expression on degraded RNA derived from formalin fixed paraffin embedded (FFPE) breast cancer tissue and normal breast ducts.
  • Optimising the assay involved developing an algorithm to classify breast cancer patients in luminal and basal subtypes utilising a 8 well-known biomarkers and 5 potential normalising genes. Data normalisation was done using permutations of the normalising genes. The selection of the normalising genes was based on the best prediction of receptor status using the Luminal/Basal classifier genes. To classify Luminal/Basal subtypes from FFPE tissues, the normalising genes selected were Beta-actin (ACTB), Glyceraldehyde 3-phosphate dehydrogenase (GAPDFI) and Flypoxanthine Phosphoribosyltransferase 1 (FIPRT1 ).
  • ACTB Beta-actin
  • GPDFI Glyceraldehyde 3-phosphate dehydrogenase
  • FRPRT1 Flypoxanthine Phosphoribosyltransferase 1
  • the method can be adapted for use in other diagnostic and research areas following adequate selection of the normalising gene set.
  • One important application of this method in the research sector is the measurement of biomarkers in archival material that is well annotated with clinical outcomes. This provides the possibility to validate potential predictive markers in retrospective studies, quickly and accurately, avoiding long-term prospective studies awaiting disease-free survival and overall survival data.
  • Figures 25 and 26 show the correlation of RNA expression level of genes from colorectal cell lines with the expression level in exosomes.
  • the data shows that AURKA can be used as a normalising gene to characterise transcript expression of diagnostic genes such as FIER2 within cancer-derived exosomes. FIER2 expression is in concordance with HER2 amplification.
  • This method has also a wide range of possible applications in the diagnosis of tumours and is adapted to the current diagnostic workflow.
  • the main advantages of the method in the diagnostic field include (1 ) implementation of high throughput assays, (2) excluding subjectivity and equivocal results originating from image-based measurements; (3) accurate detection of multiple targets simultaneously, enhancing accuracy and minimising use of precious patient samples and (4) there is no need for highly specialised facilities and human resources.
  • the optimised sampling process, together with the low input of material required for the bead-based multiplex assay, allows further investigation of tumour heterogeneity by the use of laser microdissection to accurately separate multiple foci of malignant tissue from the same patient section and compare multiple gene expression between them and also with matched normal tissue (Figure 19).
  • TMA tissue microarray
  • the use of bDNA technology in combination with magnetic bead technology and the selection of the proper panel of target genes will provide the added advantage of measuring gene expression directly in tissue lysates derived from small amounts of patient material, including microdissected material, exosomes and circulating tumour cells.
  • the proper use of panels has the potential to detect tumour derived exosomes for early diagnostics and early detection of relapses. Since there is no need of any nucleic acid amplification step, signal amplification using the bDNA technology, combined with the bead-based multiplex measures multiple gene expression in clinically-annotated archival material providing a resource for biomarker validation.
  • TCGA publicly available databases
  • Basal breast cancer cell line MDAMB231 was sensitive to the PP2A activator, 20 FTY720, and showed a suppressed PP2A activity while the MDAMB453 Luminal cell line was not sensitive to FTY720 and had relatively high PP2A activity.
  • Drug sensitivity is the inverse of percent viability.
  • the dose-dependent effect of FTY720 on breast cancer cell lines provided information on the sensitivity of the cell line to PP2A activation. Cytotoxicity 25 results for the 12 breast cancer cell lines tested are illustrated in Figure 21 where the % cell viability is expressed as a percentage of the parallel control culture (without drug) against FTY720 concentration.
  • the average vehicle control (VC) % cell viability for each cell line was never under 85% viability compared to the untreated ruling out any interference by the vehicle.
  • the 10mM and the 25mM doses were considered to be cytotoxic as cellular 30 morphology was generally altered, and hence are not shown.
  • the three sensitive cell lines defined by % cell viability under 50% (IC50) at a dose lower than 5mM, were represented by a Triple Negative phenotype ( Figure 21 A).
  • BT-20 was the most sensitive at 0.5mM, followed by MDAMB231 and Hs578T showing and IC50 at 2.5mM FTY720 and 5mM FTY720, respectively.
  • the effective dose of FTY720 was set at 5mM for assessing the effect of 5 FTY720 on RNA expression.
  • MCF10A is a cell-line derived from normal epithelium. This cell line was used as a baseline control in the study of the PP2A mechanism in breast cancer cell lines.
  • PP2A biomarker genes were identified. As outlined in 25 various studies, a biomarker needs to be well defined in its objectives, use and target population (Altar et al., 2007; Pepe, Feng, Janes, Bossuyt, & Potter, 2008). The approach for biomarker discovery involved the use of breast cancer data in the TCGA data portals in the context of PP2A deregulation. Genes were shortlisted using network analysis and known biological evidence connecting biomarkers to the PP2A pathway.
  • the set of biomarkers was validated and evidence-based selection of biomarker candidates, identified AURKA and KIF2C as strong predictors of sensitivity to FTY720. These genes were used to classify patients and cell lines into those that are eligible to PP2A activation and those that are not predicted to have PP2A deregulation.
  • the novel 10 therapeutic class was mainly represented by the Basal or Triple Negative groups and was associated with higher tumour aggressivity.
  • the expression of the PP2A complex and regulator genes was measured in the 12 breast cancer cell lines when untreated, but also when treated with FTY720 (5mM). 15
  • FIG 22 shows that Cip2a (KIAA1524) RNA expression correlates with sensitivity of breast cancer cell lines to FTY720 ( Figure 22).
  • MDA-MB-231 exemplifies FTY720- sensitive breast cancer cell lines, showing a low PP2A enzymatic activity and a high Cip2a RNA expression relative to MCF10A (a cell line derived from normal epithelium).
  • AURKA and KIF2C RNA expression were found to be positively correlated with 20 FTY720-sensitive breast cancer cell lines ( Figure 22B). Of interest expression also correlated positively with PP2A endogenous inhibitors, cip2a and SET (Table 19).
  • AURKA and KIF2C are thus preferred as they are exclusive for aggressive tumours, providing evidence that they are directly involved in the malignant phenotype (Table 18).
  • KIF2C and AURKA expression also correlated with percentage cell viability (Table 19) during FTY720 treatment in FTY720-sensitive cell lines. There was no correlation, when non sensitive cell lines were used.
  • AURKA and KIF2C also correlates with CIP2A 5 across treatment.. This supports the use of KIF2C and AURKA as biomarkers to identify novel therapeutic groups within breast cancer patients eligible for PP2A activation therapy.
  • Table 19 Spearman correlation analysis between the expression of AURKA, KIF2C and CIP2A following treatment at 0.1, 1 and 5pM FTY720 in the FTY720-sensitive cell lines. 10 Correlation of expression with cellular viability is included. The normalised Quantigene expression levels were used.
  • AURKA expression was also found to correlate strongly with cytoplasmic cip2a staining (Table 20). AURKA significantly correlates with nuclear pS6K, establishing an association between AURKA expression and growth factor (PI3K/mTOR) signaling attenuation as part of the negative feedback driven by PP2A.
  • PI3K/mTOR growth factor
  • Table 20 Spearman correlations between PP2A activity biomarker expression and the protein expression of inhibitors of PP2A and downstream phospho-proteins with defined localisation in the breast cancer cell lines.
  • PP2A activity biomarkers is normalised to the housekeeping genes, while protein expression was scored using the H-score (0-300) for each cellular compartment and then expressed as positive or negative based on thresholds set on significance with survival and/or correlation with histopathological factors [ * significant at 95% confidence interval; + significant at 90% confidence interval]
  • trastuzumab and lapatinib resistance in JIMT-1 breast cancer cells Cancer Letters, 294, 21 1 -219.
  • TANNER Mlitis KAPANEN, A. L, JUNTTILA, T gris RAHEEM, 0., GRENMAN, S sharp ELO, J., ELENIUS, K. & ISOLA, J. 2004. Characterization of a novel cell line established from a patient with Herceptin-resistant breast cancer. Molecular Cancer Therapeutics, 3, 1585- 1592.
  • AURKA proliferation marker

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L'invention concerne des méthodes améliorées pour les cancers de sous-classification en sous-populations thérapeutiquement pertinentes par l'utilisation d'une technologie d'ADN marqué (bADN).
PCT/EP2019/053733 2018-02-14 2019-02-14 Méthodes WO2019158662A1 (fr)

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