WO2013077859A1 - Signature d'expression génique pour le pronostic d'un cancer épithélial - Google Patents

Signature d'expression génique pour le pronostic d'un cancer épithélial Download PDF

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WO2013077859A1
WO2013077859A1 PCT/US2011/061871 US2011061871W WO2013077859A1 WO 2013077859 A1 WO2013077859 A1 WO 2013077859A1 US 2011061871 W US2011061871 W US 2011061871W WO 2013077859 A1 WO2013077859 A1 WO 2013077859A1
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cancer tissue
expression profile
myb
cdkn2a
kiaa0882
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PCT/US2011/061871
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Karl Thomas RIED
Jens Habermann
Nancy Lan Guo
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The United States Of America, As Represented By The Secretary, Department Of Health And Human Services
West Virginia University Research Corporation
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Priority to PCT/US2011/061871 priority Critical patent/WO2013077859A1/fr
Publication of WO2013077859A1 publication Critical patent/WO2013077859A1/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
    • C12Q2600/00—Oligonucleotides characterized by their use
    • C12Q2600/118—Prognosis of disease development
    • 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 provides a method of assessing the prognosis of a patient with non-breast epithelial cancer, or assessing the genomic instability of a non-breast epithelial cancer tissue, comprising (a) measuring the expression level in a sample of non-breast epithelial cancer tissue of (i) CDKN2A, SCYA18, STK15, NXFl , cDNA Dkfzp762M127, p28, KIAA0882, MYB, Human clone 23948, RERG, HNF3A, and (ii) ACADSB or a nucleic acid sequence comprising about 90% or greater sequence identity to SEQ ID NO: 21 to provide an expression profile of the cancer tissue, and (b) comparing the expression profile of the cancer tissue to a control.
  • the invention also provides a method of assessing the prognosis of a patient with epithelial cancer, or assessing the genomic instability of epithelial cancer tissue, comprising (a) measuring the expression level in a sample of epithelial cancer tissue of a gene signature consisting essentially of (i) NXFl , KIAA0882, MYB, CDKN2A, and RERG; (ii) NXFl , KIAA0882, cDNA Dkfzp762M127, p28, MYB, CDKN2A, SCYA18, STK15, and HNF3A; or (iii) NXFl , p28, KIAA0882, MYB, CDKN2A, SCYA1 8, STK15, and HNF3A; to provide an expression profile of the epithelial cancer tissue; and (b) comparing the expression profile of the cancer tissue to a control.
  • a gene signature consisting essentially of (i) NXFl ,
  • the invention further provides a method of screening for a compound that alters the prognosis of a patient with non-breast epithelial cancer, or alters the genomic stability of the non-breast epithelial cancer tissue, comprising (a) measuring the expression level in a sample of non-breast epithelial cancer tissue of (i) CDKN2A, SCYA 18, STK15, NXFl , cDNA Dkfzp762M127, p28, KIAA0882, MYB, Human clone 23948, RERG, HNF3A, and (ii) ACADSB or a nucleic acid sequence comprising about 90% or greater sequence identity to SEQ ID NO: 21 to provide an expression profile of the cancer tissue, (b) contacting the cancer tissue with one or more test compounds, and (c) detecting a change in the expression profile after contact with the one or more test compounds as compared to the expression profile in the absence of the one or more test compounds, wherein
  • the invention also provides a method of screening for a compound that alters the prognosis of a patient with epithelial cancer, or alters the genomic stability of epithelial cancer tissue, comprising (a) measuring the expression level in a sample of epithelial cancer tissue of a gene signature consisting essentially of (i) NXFl , KIAA0882, MYB, CDKN2A, and RERG; (ii) NXFl, KIAA0882, cDNA Dkfzp762M127, p28, MYB, CDKN2A, SCYA18, STK15, and HNF3A; or (iii) NXFl, p28, KIAA0882, MYB, CDKN2A, SCYA18, STK15, and HNF3 A; to provide an expression profile of the epithelial cancer tissue; (b) contacting the cancer tissue with one or more test compounds, and (c) detecting a change in the expression profile after contact with
  • the invention provides an array comprising (a) a substrate and (b) a set of addressable elements that each comprise at least one polynucleotide that binds to a mRNA transcript of a set of genes consisting essentially of: (i) NXFl , KIAA0882, MYB, CDKN2A, and RERG; (ii) NXFl , KIAA0882, cDNA Dkfzp762M127, p28, MYB,
  • CDKN2A, SCYA18, STK15, and HNF3A or (iii) NXFl , p28, KIAA0882, MYB, CDKN2A, SCYA18, ST 15, and HNF3A, wherein the array comprises 1000 or fewer different addressable elements.
  • the invention provides a kit comprising the above-described array, or an array comprising (a) a substrate and (b) twelve or more different addressable elements that each comprise at least one polynucleotide that binds to a mRNA transcript of CDKN2A, SCYA18, ST 15, NXFl , cDNA Dkfzp762M127, p28, KIAA0882, MYB, Human clone 23948, RERG, HNF3A, and ACADSB or a nucleic acid sequence comprising about 90% or greater sequence identity to SEQ ID NO: 21.
  • the kit also includes a control to which a test sample is compared.
  • Figure 1 A is a histogram of gene expression-defined risk scores in the training cohort of Schm et al., Nature, 439: 353-357 (2006) for lung cancer as described in Example 2.
  • Figures 1B-D are graphs depicting the survival rates by Kaplan-Meier analyses of patient cohorts from Schm et al, Nature, 439: 353-357 (2006) (Fig. IB), Bhattacharjee et al., Proc. Natl. Acad. Sci. U.S.A., 98: 13790-13795 (2001) (Fig. 1 C), and Shedden et al., Nat. Med., 14: 822-827 (2008) (Fig. ID) as described in Example 2. High risk is indicated by solid line and low risk is indicated by dotted line.
  • Figure 2A is a histogram of gene expression-defined risk scores in the training cohort from Schm et al., Nature, 439: 353-357 (2006) for ovarian cancer as described in Example 3.
  • Figures 2B-C are graphs depicting the relapse- free survival rates in postoperative ovarian cancer patients in the training (Fig. 2B) and testing (Fig. 2C) cohorts from Jardin et al., Nature, 439: 353-357 (2006) for ovarian cancer as described in Example 3. High risk is indicated by solid line and low risk is indicated by dotted line.
  • the invention provides methods, arrays, and kits that are useful for evaluating the prognosis of a patient with cancer, or assessing the genomic stability of a cancer tissue, which can be useful for assessing patient prognosis.
  • Patient prognosis is the predicted outcome or course of the disease, including the likelihood or potential for metastasis, relapse, or survival, over a given period of time (e.g., one year, two years, three years, four years, five years, seven years, ten years, etc.).
  • the methods described herein generally involve measuring the expression of twelve genes, or a subset thereof: CDKN2A, SCYA18, STK15, NXF1, cDNA
  • ACADSB or a nucleic acid sequence comprising about 90% or greater sequence identity to SEQ ID NO: 21.
  • the expression of a "gene” is used to refer to the expression of a nucleic acid (e.g., mRNA) or protein product encoded thereby.
  • Cyclin-dependent kinase inhibitor 2A (CDKN2A) is referenced in the NCBI database by GenelD 1029 (UniGenelD Hs.512599), and is also known as ARF; MLM; pi 4; pl6; pl9; CMM2; INK4; MTS1 ; TP16; CDK4I; CDKN2; INK4a; pl4ARF; pl6INK4; and pl 6INK4a.
  • NC_000009.1 1 The sequence of the mRNA transcripts of CDKN2A are referenced in the NCBI database under NC_000009.1 1 (Gl: 224589821) as NM_058195.3 (Gl: 300863095) (SEQ ID NO: 1), NM_058197.4 (Gl: 300863098) (SEQ ID NO: 2), and NM_000077.4 (Gl:
  • SCYA18 Small inducible subfamily Al 8
  • GenelD 6362 UniGenelD Hs.143961
  • the sequence of the mRNA transcript of SCYA18 is referenced in the NCBI database under NC_000017.9 (gi:5151 1734) as NMJD02988.2 (GI:22547150) (SEQ ID NO: 4).
  • Serine/threonine kinase 15 (STK15) is referenced in the NCBI database by
  • GenelD 6790 (UniGenelD Hs.250822), and is also known as Aurora kinase A (AURKA); AIK; ARKl ; AURA; BTAK; STK6; STK7; AURORA2; and MGC34538.
  • AURKA Aurora kinase A
  • AIK Aurora kinase A
  • ARKl a sequence of the mRNA transcripts of STK15
  • NM_003600.2 (gi:38327561)
  • SEQ ID NO: 5 NMJ98433.1
  • NMJ98434.1 (gi:38327565) (SEQ ID NO: 8), NMJ98437.1 (gi:38327571) (SEQ ID NO: 9), and NMJ 98436.1 (gi:38327569) (SEQ ID NO: 10).
  • NXF1 Nuclear RNA export factor 1
  • GenelD 10482 (UniGenelD Hs.523739), and is also known as TAP; MEX67; and
  • DKFZp667O031 The sequence of the mRNA transcripts of NXF1 are referenced in the
  • NCBI database under NCJXXX) 1 1.8 (gi:51511727) as NM_001081491.1 (gi: 125625323)
  • cDNA Dkfzp762M127 Homo sapiens mRNA cDNA Dkfzp762M127 (hereinafter "cDNA
  • Dkfzp762M127 is referenced in the NCBI database by Accession Number AL157484.1
  • sequence of the mRNA transcript associated with cDNA Dkfzp762M127 also is provided herein as SEQ ID NO: 13.
  • p28 is referenced in the NCBI database by GenelD 7802 (UniGenelD
  • Hs.406050 and is also known as dynein, axonemal, light intermediate chain 1 (DNALI1); hp28; and dj423B22.5.
  • DNALI1 axonemal, light intermediate chain 1
  • hp28 axonemal
  • dj423B22.5 The sequence of the mRNA transcript of p28 is referenced in the
  • NCBI database as NMJ303462.3 (GI: 37595559) and provided herein as SEQ ID NO: 14.
  • KIAA0882 is referenced in the NCBI database by GenelD 23158 (UniGenelD
  • TBC1 domain family member 9 (TBC1D9) or MDR1.
  • the sequence of the mRNA transcript of KIAA0882 is referenced in the NCBI database as
  • NM 015130.2 (GI: 139394667) and provided herein as SEQ ID NO: 15.
  • v-Myb myeloblastosis viral oncogene homolog (avian) (MYB) is referenced in the NCBI database by GenelD 4602, and is also known as efg; Cmyb; c-myb; and c- myb_CDS.
  • the sequence of the mRNA transcript of MYB is referenced in the NCBI database as NM_005375.2 (GI: 46361979) and provided herein as SEQ ID NO: 16.
  • Human clone 23948 mRNA sequence (hereinafter "human clone 23948) is referenced in the NCBI database by Accession Number U79293.1 (GI: 1710274)
  • RAS-like, estrogen-regulated, growth inhibitor (RERG) is referenced in the
  • NCBI database by GenelD 85004 (UniGenelD Hs.199487), and is also known as
  • MGC 15754 The sequence of the mRNA transcript of RERG is referenced in the NCBI database as NM_032918.2 (GI: 299758437) and provided herein as SEQ ID NO: 18.
  • Hepatocyte nuclear factor 3 alpha is referenced in the NCBI database by GenelD 3169 (UniGenelD Hs.163484), and is also known as forkhead box Al (FOXA1); TCF3A; and MGC33105.
  • the sequence of the mRNA transcript of HNF3A is referenced in the NCBI database as NM_004496.2 (GI: 24497500) and provided herein as SEQ ID NO: 19.
  • Acyl-Coenzyme A dehydrogenase, short/branched chain (ACADSB) is referenced in the NCBI database by GenelD 36 (UniGenelD Hs.81934), and is also known as ACAD7, SBCAD, and 2-MEBCAD.
  • the sequence of the mRNA transcript of ACADSB is NM 001609.3 (GI:96361828) (SEQ ID NO: 20).
  • nucleic acid comprising about 90% or greater sequence identity to SEQ ID NO:
  • nucleic acid includes, for instance, a nucleic acid comprising about 92% or greater, 95% or greater, 96% or greater, 97% or greater, 98% or greater, or even 99% or greater sequence identity to SEQ ID NO: 21 , as well as a nucleic acid that comprises SEQ ID NO: 21.
  • a nucleic acid includes, for instance, a nucleic acid referenced in the art as Incyte EST (Clone ID 88935).
  • the expression level of the following genes, or subset thereof can be used to assess the prognosis of a patient with epithelial cancer, or to assess the genomic instability of the cancer tissue (e.g., instability of the genome of the cells of the cancer tissue), which can be used, in turn, as an indicator of patient prognosis or for other purposes.
  • genomic instability as reflected in specific chromosomal aneuploidies and variation in the nuclear DNA content, is a defining feature of human carcinomas. It is solidly established that the degree of genomic instability influences clinical outcome, with greater genomic instability indicating a likelihood of a poorer outcome and lesser genomic instability indicating a likelihood of a better outcome.
  • the invention provides a method of assessing the prognosis of a patient with non-breast epithelial cancer comprising (a) measuring the expression level of (i) CDKN2A, SCYA18, STK15, NXF1 , cDNA Dkfzp762M127, p28, KIAA0882, MYB, Human clone 23948, RERG, HNF3A, and (ii) ACADSB or a nucleic acid comprising about 90% or greater sequence identity to SEQ ID NO: 21 , in a non-breast epithelial cancer tissue sample from the patient to provide an expression profile, and (c) comparing the expression profile of the cancer tissue sample to a control.
  • the invention provides a method of assessing genomic instability in non-breast epithelial cancer tissue comprising (a) measuring the expression level of (i) CDKN2A, SCYA18, STK15, NXF1 , cDNA Dkfzp762M127, p28, KIAA0882, MYB, Human clone 23948, RERG, HNF3A, and (ii) ACADSB or a nucleic acid sequence comprising about 90% or greater sequence identity to SEQ ID NO: 21, in the cancer tissue to provide an expression profile of the cancer tissue, and (b) comparing the expression profile of the cancer tissue to a control.
  • measuring the expression level in a sample of non-breast epithelial cancer tissue can comprise measuring the expression of a gene signature comprising, consisting essentially of, or consisting of (i) NXF1 , IAA0882, MYB, CDKN2A, and RERG; (ii) NXF1, KIAA0882, cDNA Dkfzp762M127, p28, MYB, CDKN2A, SCYA18, ST 15, and HNF3A; or (iii) NXF1, p28, KIAA0882, MYB, CDKN2A, SCYA18, STK15, and HNF3A.
  • a gene signature comprising, consisting essentially of, or consisting of (i) NXF1 , IAA0882, MYB, CDKN2A, and RERG; (ii) NXF1, KIAA0882, cDNA Dkfzp762M127, p28, MYB, CDKN
  • the invention also provides a method of assessing the prognosis of a patient with epithelial cancer comprising (a) measuring the expression level in a sample of epithelial cancer tissue from the patient of a gene signature consisting essentially of or consisting of (i) NXF1 , KIAA0882, MYB, CDKN2A, and RERG; (ii) NXF1 ,
  • KIAA0882 cDNA Dkfzp762M127, p28, MYB, CD N2A, SCYA18, STK15, and HNF3A; or (iii) NXF1 , p28, KIAA0882, MYB, CDKN2A, SCYA18, STK15, and HNF3A; to provide an expression profile of the epithelial cancer tissue; and (b) comparing the expression profile of the cancer tissue to a control.
  • the invention provides a method of assessing genomic instability in non-breast epithelial cancer tissue comprising (a) measuring the expression level in a sample of epithelial cancer tissue of a gene signature consisting essentially of or consisting of (i) NXF1 , IAA0882, MYB, CDKN2A, and RERG; (ii) NXF1 , KIAA0882, cDNA
  • Epithelial cancers include any cancer of epithelial origin.
  • Epithelial cancers include, without limitation, breast, lung, prostate, colon, rectal, pancreatic, ovarian, prostate, cervical, bladder, esophageal, gastric and endometrial cancers, as well as head and neck squamous cell carcinoma (HNSCC).
  • HNSCC head and neck squamous cell carcinoma
  • Non-breast epithelial cancers are those epithelial cancers other than breast cancers. The methods described herein are believed to be particularly useful for ovarian, lung, and colon cancers.
  • Measuring or detecting the expression of any of the foregoing genes or nucleic acids comprises measuring or detecting any nucleic acid transcript (e.g., mRNA) or protein product thereof. Where a gene is associated with more than one mRNA transcript, the expression of the gene can be measured or detected by measuring or detecting any one or more of the mRNA transcripts of the gene, or all of the mRNA transcripts).
  • nucleic acid transcript e.g., mRNA
  • protein product thereof e.g., a gene is associated with more than one mRNA transcript
  • the expression of the gene can be measured or detected by measuring or detecting any one or more of the mRNA transcripts of the gene, or all of the mRNA transcripts).
  • the gene expression can be detected or measured on the basis of mRNA or cDNA levels, although protein levels also can be used when appropriate.
  • Suitable methods of detecting or measuring mRNA or cDNA levels include, for example, Northern Blotting, reverse-transcription PCR (RT-PCR), real-time RT-PCR, and microarray analysis. Such methods are described in Sambrook et al., Molecular Cloning: A Laboratory Manual, 2 nd Ed., Cold Spring Harbor Press, Cold Spring Harbor, N.Y., 1989.
  • RT-PCR reverse-transcription PCR
  • microarray analysis or a PCR-based method is used.
  • the measuring the expression of the foregoing nucleic acids in the cancer tissue can comprise, for instance, contacting a sample of the cancer tissue with probes specific to the nucleic acids, or with primers designed to amplify a portion of the nucleic acids, and detecting binding of the probe to the nucleic acid or amplification of the nucleic acid, respectively.
  • probes specific to the nucleic acids or with primers designed to amplify a portion of the nucleic acids
  • detecting binding of the probe to the nucleic acid or amplification of the nucleic acid respectively.
  • Detailed protocols for designed PCR probes are known in the art.
  • microarrays to analyze gene expression are known in the art and described herein.
  • the tissue sample can comprise any clinically relevant tissue sample, such as a tumor biopsy or fine needle aspiration.
  • samples of bodily fluid such as blood, plasma, serum, lymph, ascetic fluid, cystic fluid, or urine, might be used.
  • the patient from which the sample is obtained is a human or a non-human animal, such as a rodent (e.g., mouse, rat, guinea pig, hamster), rabbit, cat, dog, pig, cow, horse, or non-human primate.
  • the control can be any suitable reference that allows evaluation of the expression level of the genes of interest, as described above, in the cancer tissue as compared to the expression of the same genes in sample or a pool of samples from patients with known outcomes, or a sample or pool of samples of known genomic stability.
  • the control can be a tissue sample that is analyzed simultaneously or sequentially with the test sample.
  • Suitable control tissue samples include a non-cancerous tissue sample, or a tissue sample confirmed to be genomically stable.
  • the control can be a cancerous tissue sample from a patient of a known prognosis, or a tissue sample with an established degree of genomic instability.
  • the control can be the average expression level of the genes of interest of a pool of any such tissue samples.
  • the control can embodied, for example, in a pre-prepared microarray used as a standard or reference, or in data that reflects the expression profile of relevant genes in a sample or pool of samples, such as might be part of an electronic database or computer program.
  • the expression profile of the cancer tissue can be compared to the control by any suitable technique.
  • comparing the expression profile of the cancer tissue to a control comprises calculating a correlation coefficient of the expression profile of the cancer tissue with respect to the control.
  • Any statistical method can be used to calculate the correlation coefficient, such as using Pearson's correlation method (see Strickert et al., BMC Bioinfonnatics, 8, (2007)). Pearson's correlation coefficient describes the linear dependency of vectors x and w by: where ⁇ and ⁇ are the respective means of the vectors x and w. The equation is standardized by the product of the standard deviations of the vectors.
  • Pearson's correlation coefficient is method commonly used for identifying gene expression patterns associated with traits of biological phenotypes (see Strickert et al., BMC Bioinfonnatics, 8, (2007); Kraft et al., Am J Hum Genet, 72(5), 1323-30 (2003)). Based on the similarity of the expression profile in the cancer tissue to the control (how closely the expression profile of the test tissue sample correlates to the control), patient prognosis or genomic instability can be assessed.
  • control is the average expression profile of the genes in a tissue sample or pool of tissue samples from patients with good prognosis (little or no metastasis and/or relapse)
  • a correlation coefficient that indicates a high degree of similarity between the control and the test sample would indicate a good prognosis
  • a correlation coefficient that indicates a low degree of similarity would indicate a poor prognosis.
  • control is the average expression profile of the genes in a pool of tissue samples from cancer patients with poor prognosis. The same can be said for the assessment of genomic instability.
  • control is the average expression profile of the genes in a tissue sample or pool of tissue samples known to be genomically stable
  • a correlation coefficient that indicates a high degree of similarity between the control and the test sample would indicate genomic stability in the test sample, while a correlation coefficient that indicates a low degree of similarity would indicate genomic instability in the test sample.
  • the assessment of patient prognosis can further comprise generating a risk score.
  • Risk scores can be generated, for instance, by using a Cox model of the gene signature (see Examples). Patients with a risk score greater than the median are defined as high risk, whereas patients with a risk score less than the median are classified as low risk.
  • the analysis can be independent of clinical-pathological parameters, although any standard risk evaluation can be used.
  • a large value or the risk score indicates a high risk of relapse/metastasis (i.e., poor prognosis), while a small value indicates a lower risk of cancer relapse/metastases (i.e., good prognosis).
  • a patient's prognostic categorization also can be determined using a statistical model or a machine learning algorithm, which computes the probability of recurrence based on the patient's gene expression profiles. Cutoffs can be defined for patient stratification based on the specific clinical setting.
  • patient prognosis e.g., metastatic potential, relapse potential, and/or survival rate
  • genomic instability can be based on a pre-determined metric or "cut-off based on the correlation coefficient or risk score.
  • a pre-determined metric e.g., metastatic potential, relapse potential, and/or survival rate
  • the statistical significance can be assessed using the test for the significance of the Pearson product-moment correlation coefficient (see Moore, D. Basic Practice of Statistics . 4 ed. WH Freeman Company (2006)).
  • the assessment of prognosis also can be relative, as might be the case when both a positive and negative control is used.
  • the method can comprise comparing the expression profile of the cancer tissue to a first and second control by calculating a correlation coefficient of the expression profile of the cancer tissue with respect to each of the first and second controls (i.e., to provide a first and second correlation coefficient, respectively), wherein the first control is the average expression profile of the genes in a pool of tissue samples (e.g., cancer tissue samples) from cancer patients with good prognosis (little or no metastasis and/or relapse) and the second control is the average expression profile of the genes in a pool of tissue samples (e.g., cancer tissue samples) from cancer patients with poor prognosis (high incidence of metastasis and/or relapse with low survival rates).
  • tissue samples e.g., cancer tissue samples
  • the second control is the average expression profile of the genes in a pool of tissue samples (e.g., cancer tissue samples) from cancer patients with poor progno
  • the methods can comprise comparing the expression profile of the cancer tissue to a first and second control.
  • the assessment of genomic stability or patient prognosis can be made based on the relative similarity of the gene expression profile to the two controls.
  • the method can comprise calculating a correlation coefficient of the expression profile of the cancer tissue with respect to each of the first and second controls (i.e., to provide a first and second correlation coefficient, respectively).
  • the first control can be a the expression profile of a tissue sample, or the average expression profile of a pool of tissue samples, known to be genomically stable or from patients with good prognosis
  • the second control can be the expression profile of a tissue sample, or the average expression profile of a pool of tissue samples, known to have a high level of genomic instability or from patients with a poor prognosis.
  • genomic instability or poor prognosis is indicated if the correlation coefficient of the expression profile of the cancer tissue calculated with respect to the second control is greater (or otherwise indicates greater similarity) than the correlation coefficient of the expression profile of the cancer tissue calculated with respect to the first control.
  • the method described herein can comprise establishing a control by measuring (or otherwise determining or assembling) the expression level of the genes in a pool of tissue samples (e.g., cancer tissue samples) of patients with known outcome or tissue samples of known degrees of genomic instability.
  • the expression levels can be correlated with outcome or genomic stability to establish a model for a given population.
  • This model can then be applied to interpret the expression levels of the same genes in samples of similar patients of unknown status (e.g., patients with the same or similar disease), in order to classify the patients with respect to prognosis or treatment options.
  • Determining genomic instability of tissue samples for use as controls can be performed by any suitable method, including assessing nuclear DNA content using routine methods (e.g., image cytometry) and tissues classified as genomically stable or genomically unstable according to known indices (e.g., Kronenwett et al., Cancer Res, 64(3), 904-9 (2004); Kronenwett et al., Cancer Epidemiol Biomarkers Prev, 15(9), 1630-5 (2006)).
  • routine methods e.g., image cytometry
  • indices e.g., Kronenwett et al., Cancer Res, 64(3), 904-9 (2004)
  • Kronenwett et al. Cancer Epidemiol Biomarkers Prev, 15(9), 1630-5 (2006).
  • poor patient prognosis can mean that a given patient is associated with, classified as belonging to, or otherwise predicted to follow the course of a patient population in which a high proportion of the patients (e.g., 30% or more, 40% or more, 50% or more, 60% or more, 70% or more, 80% or more, or even 90% or more patients) experience one or more of disease progression, relapse of disease, metastasis or death (fail to survive) within a time frame of six months, one year, two years, three years, four years, five years, seven years, or ten years.
  • a high proportion of the patients e.g., 30% or more, 40% or more, 50% or more, 60% or more, 70% or more, 80% or more, or even 90% or more patients
  • good patient prognosis can mean that a given patient predicted is associated with, classified as belonging to, or otherwise predicted to follow the course of a patient population in which a high proportion of the patients (e.g., 30% or more, 40% or more, 50% or more, 60% or more, 70% or more, 80% or more, or even 90% or more patients) do not experience one or more of disease progression, relapse of disease, metastasis or death (fail to survive) within a time frame of six months, one year, two years, three years, four years, five years, seven years, or ten years.
  • Good and poor prognosis can be expressed in many different ways (e.g., mean-time to disease progression, relapse, metastasis or death), and the forgoing is not intended to be a limitation on the methods described herein.
  • Naive Bayes method, neural networks, or decision trees can be used for predicting patient survival or the metastatic potential of a tumor.
  • the "correlation coefficient" is used generically to refer to any appropriate distance metric.
  • the foregoing ranges expressed in terms of Pearson's correlation are only for the purposes of illustration, and the equivalent of the foregoing ranges using a different statistical technique also can be used.
  • Computer algorithms are known and embodied in commercially available software that facilitate the comparison, and analyze the relatedness, between gene expression profiles any of which can be used in conjunction with the methods of the invention.
  • the methods of the invention also can be used for other purposes, such as to monitor the progression or regression of disease (e.g., cancer), or assess the effectiveness of treatment, in a patient.
  • the method can be applied to different samples (e.g., a first and second sample) taken from the same patient at different points in time and the results compared, wherein a change in the gene expression profile of the 12-gene signature can be used to reach a determination as to whether the disease has progressed or regressed or a given treatment has been effective. For instance, increasing genomic instability between a sample taken at an earlier point in time (first sample) and a sample taken at a later point in time (second sample) suggests progression of disease or lack of
  • the inventive methods also can be used to screen for compounds that alter the prognosis of a patient with cancer, or that alter genomic stability of a cancer tissue.
  • the method of screening a compound can comprise (a) measuring the expression level of (i) CDKN2A, SCYA18, STK15, NXFl , cDNA Dkfzp762M127, p28, KIAA0882, MYB, Human clone 23948, RERG, HNF3A, and (ii) ACADSB or a nucleic acid sequence comprising about 90% or greater sequence identity to SEQ ID NO: 21 in a sample of non-breast epithelial cancer tissue to provide an expression profile, (b) contacting the cancer tissue with one or more test compounds, and (c) detecting a change in the expression profile after contact with the one or more test compounds as compared to the expression profile of the cancer tissue in the absence of (e.g., before contact with) the one or more test compounds, wherein a change in the expression
  • the method can comprise (a) measuring the expression level in a sample of epithelial cancer tissue of a gene signature consisting essentially of: (i) NXFl , KIAA0882, MYB, CDKN2A, and RERG; (ii) NXFl, KIAA0882, cDNA Dkfzp762M127, p28, MYB, CDKN2A, SCYA18, ST 15, and HNF3A; or (iii) NXFl, p28, KIAA0882, MYB, CD N2A, SCYA18, ST 15, and HNF3 A; to provide an expression profile of the epithelial cancer tissue; (b) contacting the cancer tissue with one or more test compounds, and (c) detecting a change in the expression profile after contact with the one or more test compounds as compared to the expression profile in the absence of the one or more test compounds, wherein a change in the correlation coefficient indicates that the one or more test compounds alters the prognosis
  • Detecting a change in the expression profile after contact with the one or more test compounds as compared to the expression profile in the absence of the one or more test compounds can comprise (i) calculating a first correlation coefficient of the expression profile of the cancer tissue from step (b) with respect to a control; (ii) measuring the expression level of the same genes measured in step (a) after contacting the cancer tissue with the one or more test compounds to provide a second expression profile; (iii) calculating a second correlation coefficient with respect to the control; and (iv) comparing the first and second correlation coefficients, wherein a difference between the first and second correlation coefficients indicates a change in the expression profile.
  • the array comprises (a) a substrate and (b) a set of addressable elements that each comprise at least one polynucleotide that binds to a mRNA transcript of a set of genes comprising, consisting essentially of, or consisting of: (i) NXF1 , KIAA0882, MYB, CDKN2A, and RERG; (ii) NXF1 , KIAA0882, cDNA Dkfzp762M127, p28, MYB, CDKN2A, SCYA18, STK15, and HNF3A; or (iii) NXF1, p28, IAA0882, MYB, CDKN2A, SCYA18, STK15, and HNF3A; wherein the array comprises 1000 or fewer different addressable elements.
  • the term "addressable element” means an element that is attached to the substrate at a predetermined position and specifically binds a known target molecule, such that when target-binding is detected (e.g., by fluorescent labeling), information regarding the identity of the bound molecule is provided on the basis of the location of the element on the substrate.
  • Addressable elements are "different” for the purposes of the present invention if they do not bind to the same target molecule.
  • the addressable element comprises one or more polynucleotides (i.e., probes) specific for an mRNA transcript of a given gene, or a cDNA synthesized from the mRNA transcript.
  • the addressable element can comprise more than one copy of a polynucleotide, can comprise more than one different polynucleotide, provided that all of the polynucleotides bind the same target molecule.
  • the addressable element for the gene can comprise different probes for different transcripts, or probes designed to detect a nucleic acid sequence common to two or more (or all) of the transcripts.
  • the array can comprise an addressable element for the different transcripts.
  • the addressable element also can comprise a detectable label, suitable examples of which are well known in the art.
  • the array can comprise addressable elements that bind to mRNA or cDNA other than that of CDKN2A, SCYA18, STK15, NXF1 , cDNA Dkfzp762M127, p28,
  • the array preferably comprises a limited number of addressable elements.
  • the array desirably comprises no more than about 1000 or fewer different addressable elements, more preferably no more than about 500 or fewer different addressable elements, or even no more than about 100 or fewer different addressable elements, such as about 75 or fewer different addressable elements, or even about 50 or fewer different addressable elements.
  • even smaller arrays can comprise about 25 or fewer different addressable elements, such as about 15 or fewer different addressable elements.
  • the array can be limited to the number of different addressable elements needed to measure the expression of the genes of interest without interfering with its functionality.
  • the substrate can be any rigid or semi-rigid support to which polynucleotides can be covalently or non-covalently attached.
  • Suitable substrates include membranes, filters, chips, slides, wafers, fibers, beads, gels, capillaries, plates, polymers, microparticles, and the like.
  • Materials that are suitable for substrates include, for example, nylon, glass, ceramic, plastic, silica, aluminosilicates, borosilicates, metal oxides such as alumina and nickel oxide, various clays, nitrocellulose, and the like.
  • probes can be attached to the substrate in a pre-determined 1-, 2-, or 3-dimensional arrangement, such that the pattern of hybridization or binding to a probe is easily correlated with the expression of a particular gene. Because the probes are located at specified locations on the substrate (i.e., the elements are "addressable"), the hybridization or binding patterns and intensities create a unique expression profile, which can be interpreted in terms of expression levels of particular genes and can be correlated with genomic instability in accordance with the methods described herein.
  • Polynucleotide and polypeptide probes can be generated by any suitable method known in the art (see, e.g., Sambrook et al., Molecular Cloning: A Laboratory Manual, 2nd Ed., Cold Spring Harbor Press, Cold Spring Harbor, N.Y., (1989)).
  • polynucleotide probes that specifically bind to the mRNA transcripts of the genes described herein (or cDNA synthesized therefrom) can be created using the nucleic acid sequences of the mRNA or cDNA targets themselves (e.g., SEQ ID NOs: 1 -12 or fragments thereof) by routine techniques (e.g., PCR or synthesis).
  • fragment means a contiguous part or portion of a polynucleotide sequence comprising about 10 or more nucleotides, preferably about 15 or more nucleotides, more preferably about 20 or more nucleotides (e.g., about 30 or more or even about 50 or more nucleotides).
  • a polynucleotide probe that binds to an mRNA transcript of CD N2A can be provided by a polynucleotide comprising a nucleic acid sequence that is complementary to the mRNA transcript (e.g., SEQ ID NO: 1 ) or a fragment thereof, or sufficiently complementary to SEQ ID NO: 1 or fragment thereof that it selectively binds to SEQ ID NO: 1.
  • SEQ ID NO: 1 a nucleic acid sequence that is complementary to the mRNA transcript
  • SEQ ID NO: 1 e.g., SEQ ID NO: 1
  • SEQ ID NO: 1 a nucleic acid sequence that is complementary to the mRNA transcript
  • SEQ ID NO: 1 e.g., SEQ ID NO: 1
  • the polynucleotide probes will comprise 10 or more nucleic acids (e.g., 20 or more, 50 or more, or 100 or more nucleic acids).
  • the probe will have a sequence identity to a complement of the target sequence (e.g., SEQ ID NOs: 1-41 or corresponding fragment thereof) of about 90% or more, preferably about 95% or more (e.g., about 98% or more or about 99% or more) as determined, for example, using the well-known Basic Local Alignment Search Tool (BLAST) algorithm (available through the National Center for Biotechnology Information (NCBI), Bethesda, MD).
  • BLAST Basic Local Alignment Search Tool
  • the array can comprise other elements common to polynucleotide arrays.
  • the array also can include one or more elements that serve as a control, standard, or reference molecule, such as a housekeeping gene or portion thereof (e.g., PBGD or GAPDH), to assist in the normalization of expression levels or the determination of nucleic acid quality and binding characteristics, reagent quality and effectiveness, hybridization success, analysis thresholds and success, etc.
  • PBGD housekeeping gene or portion thereof
  • the invention provides a kit for assessing prognosis (e.g., metastatic potential, relapse potential, and/or survival) in a cancer patient comprising the array and a control, wherein the results of the array analysis from the control and a test sample from the cancer patient can be compared to assess patient prognosis of the test sample.
  • the control can be in a pre-prepared microarray used as a standard or reference or in data that reflects the expression profile of relevant genes in a sample or pool of samples from a patient with a known prognosis (e.g., metastasis and/or relapse), which may be part of an electronic database or computer program.
  • the kit can be used to assess genomic instability in a cancer tissue sample from an cancer patient.
  • inventive gene signatures and methods described herein can be combined with other prognostic gene signatures associated with cancer, or portions of such signatures (e.g., ovarian, lung, or colon cancer) known in the art (see, e.g., Guo et al., Clin. Cancer Res., 14(24): 8213-8220 (2008) and Guo et al., Int. J. Comput. Biol. Drug Des., 4(1): 19-39 (2011)).
  • Bhattacharjee et al. was measured on Affymetrix U95A arrays.
  • the non-small cell lung cancer dataset from Schm et al. was quantified with Affymetrix U133 Plus 2.0 arrays.
  • the lung adenocarcinoma dataset from Shedden et al. was generated with Affymetrix U133A.
  • the ovarian cancer dataset from Prof et al. was assayed with Affymetrix U133A (retrieved with record GSE3149 from Gene Expression Omnibus).
  • RIPPER - RIPPER is a prepositional rule learning algorithm proposed by
  • Cox proportional hazard model In survival analysis, the hazard function assesses the instantaneous risk of death at time t condition on survival to that time point:
  • a Cox proportional hazard model defines the relationship between the survival of patients and a set of variables, such as gene expressions.
  • a hazard ratio is defined as the hazard for one individual divided by the hazard for a different individual:
  • the hazard ratio represents the ratio of hazard (i.e., death from cancer between the average risk scores of 2 prognostic groups).
  • IP A Ingenuity Pathways Analysis
  • the 12-gene genomic instability signature comprises the following: CDKN2A,
  • the second cohort contained 24 patients with stage II colon adenocarcinoma
  • the third cohort contained 18 patients with colon adenocarcinoma (see Barrier et al., Oncogene, 24: 6155-6164 (2005)). Ten of these patients had no lymph node metastasis (stage II) and did not receive chemotherapy. The other 8 patients had lymph node metastasis (stage III) and received 6-month adjuvant chemotherapy with 5-fluorouracil (5-FU) and levamisole. Patients were evaluated at 3 -month intervals for the first postoperative year and at 6-month intervals thereafter. Nine of the 18 patients (5 stage II patients and 4 stage III patients) developed a distant metastasis within 53 months. The other 9 patients remained disease- free for at least 60 months with mean follow-up of 75 months.
  • the matching genes in the 12-gene genomic instability signature were identified with Affymetrix IDs. Of the 12-gene signature set, nine common genes were found in each of the 3 colon cancer cohorts with 6 genes having matches to multiple probes. The mean expression of the duplicative probes for each gene was used. The patient cohort from Barrier et al.
  • genomic instability signature correctly predicted recurrence in 72% (36/50) of patients with a sensitivity of 68% (17/25) and a specificity of 76% (19/25).
  • genomic instability signature correctly predicted recurrence in 69.1% (29/42) of patients with a sensitivity of 73.9% (14/19) and a specificity of 65.2% (15/23).
  • 13795 contained 84 patients with lung adenocarcinoma. Sixty-two patients were in stage I, 14 were in stage II, and 8 were in stage III. Twenty-six tumors were well- differentiated, 43 were moderately differentiated, and 15 were poorly differentiated.
  • the DNA microarray data were generated for each lung cancer cohort using different Affymetrix platforms.
  • the non-small cell lung cancer dataset from Schm et al. was quantified with Affymetrix U133 Plus 2.0 arrays.
  • the lung adenocarcinoma dataset from Bhattacharjee et al. was measured on Affymetrix U95A arrays.
  • the lung adenocarcinoma dataset from Shedden et al. was generated with Affymetrix U133A arrays.
  • Cox proportional hazard model was constructed by using genes from the 12-gene signature set as covariates to predict lung cancer survival after the initial treatment.
  • Cohort 1 (Bild et al.) was used as a training set.
  • a risk score was generated for each patient in this cohort.
  • a high risk score represents a high probability of
  • the low-risk groups had above 80% of 2-year postoperative survival rate, representing a significantly better prognosis compared with the corresponding high-risk groups for which the 2-year survival was ranging from 38% to 58%o.
  • the 12-gene genomic instability signature had a significant hazard ratio (HR > 1.55, p ⁇ 0.05) in predicting poor-prognosis in all three studied lung cancer cohorts as indicated below:
  • the clinical characteristics of the ovarian cancer cohort is set forth below:
  • a cutoff value was identified for patient stratification.
  • a cutoff value of -3 was identified based on the histogram of the risk scores in the training set (Fig. 2A).
  • Patients with a risk score greater than the cutoff were stratified into the high-risk group, and otherwise, into the low- risk group.
  • This example demonstrates a functional pathway analysis of the 12 signature genes using IPA software.
  • the 12-gene genomic instability signature was able to distinguish more aggressive tumors in multiple cancer types, indicating that the signature likely is involved in important mechanisms of tumor genesis and progression.
  • Functional pathway analysis was performed based on a curated database of molecular interactions reported in the literature using IPA. The results show that the signature genes interact with multiple prominent cancer signaling pathways, including the oncogenes NFKBIA, MYC, BCL2, CDKI (CDC2), E2F1, and SOD2, as well as the tumor suppressor genes TP53 and CASP9. A summary of these pathways as described in the literature is set forth below.
  • Aurora-A is an important regulator of mitosis whose amplification is associated with human cancer. Perturbed expression of Aurora-A in mammalian cells induces carcinogenic phenotypes, including centrosome amplification, genomic instability, and transformation. UBE2N binds to Aurora-A and inhibits it both in vitro and in vivo through the stimulation of ubiquitination. In MCF7 cells, human Aurora-A protein increases polyubiquitination of human 1KB A ⁇ NFKBIA) protein.
  • human p!4 ARF (CDKN2A) protein activates the Caspase 9 (CASP9) and mitochondrial apoptosis pathway entirely independent of TP53, and these caspase-9- ⁇ i e activities were greatly enhanced in cells lacking functional p21 ⁇ CDKNIA).
  • BCL-2 is a direct target of c-Myb, and overexpression of c-Myb is accompanied by upregulation of BCL- 2 expression.
  • E2F family members (E2F1 through E2F5) is blocked by methylated E2F elements derived from E2F1 and CDC2 promoters.
  • E2F1, but not E2F2 through E2F5 is significantly inhibited by methylated E2F elements derived from the c-Myc and c-Myb promoters.
  • CCL18 is one of the most abundant chemokines produced by immature dendritic cells, and may be part of an inhibitory pathway to limit specific immune responses at peripheral sites in maturing human dendritic cells.
  • TNF- (TNF) protein decreases expression of CCL18 mRNA.
  • containing are to be construed as open-ended terms (i.e., meaning “including, but not limited to,”) unless otherwise noted. Wherever an open-ended term is used, the substitution of a closed-ended term (e.g., “consisting essentially of or “consisting of) is specifically contemplated. Recitation of ranges of values herein are merely intended to serve as a shorthand method of referring individually to each separate value falling within the range, unless otherwise indicated herein, and each separate value is incorporated into the specification as if it were individually recited herein. All methods described herein can be performed in any suitable order unless otherwise indicated herein or otherwise clearly contradicted by context.

Abstract

L'invention concerne des procédés pour l'estimation du pronostic d'un patient cancéreux ou de l'instabilité génomique d'un tissu cancéreux, par la mesure du taux d'expression de CDKN2A, SCYA18, STK15, NXF1, ADNc Dkfzp762M127, p28, KIAA0882, MYB, du clone humain 23948, RERG, HNF3A et ACADSB ou d'une séquence d'acide nucléique comprenant environ 90 % ou plus d'identité de séquence avec SEQ ID NO: 21 dans le tissu cancéreux, ou un sous-ensemble de celui-ci, et des procédés, trousses et réseaux associés.
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