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WO2013052480A1 - Score de risque pronostique de cancer du côlon basé sur des marqueurs - Google Patents

Score de risque pronostique de cancer du côlon basé sur des marqueurs Download PDF

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Publication number
WO2013052480A1
WO2013052480A1 PCT/US2012/058453 US2012058453W WO2013052480A1 WO 2013052480 A1 WO2013052480 A1 WO 2013052480A1 US 2012058453 W US2012058453 W US 2012058453W WO 2013052480 A1 WO2013052480 A1 WO 2013052480A1
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Prior art keywords
cancer
expression
biomarkers
vav3
patients
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Ju-Seong LEE
Sang Cheol OH
Yun-Yong Park
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University of Texas System
University of Texas at Austin
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University of Texas System
University of Texas at Austin
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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
    • G01N33/57535—
    • 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
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N2800/00—Detection or diagnosis of diseases
    • G01N2800/52—Predicting or monitoring the response to treatment, e.g. for selection of therapy based on assay results in personalised medicine; Prognosis

Definitions

  • the present invention relates generally to the fields of oncology, molecular biology, cell biology, and cancer. More particularly, it concerns cancer prognosis or classification using molecular markers.
  • Colorectal cancer is one of the most common cancers in the United States and the rest of the world, accounting for an estimated 146,900 new cases and 49,920 deaths in 2009 in the United States alone (Parkin et al., 2005; Jemal et al., 2009).
  • surgical resection is highly effective for patients with early-stage colon cancers, a high proportion of patients have relapse after complete surgical resection, with 40% to 50% of patients with stage III disease experiencing such relapse within 5 years (Carlsson et al., 1987; Midgley and Kerr, 1999).
  • the present invention overcomes limitations in the prior art by providing biomarker genes or gene expression signatures that may be used to detect or predict the prognosis of a colon cancer. More specifically, a genome-wide survey of gene expression data was applied to distinguish subtypes of colon cancer that have distinct biological characteristics associated with prognosis and to identify potential biomarker genes or a gene expression signature that reflect the biological or clinical characteristics of each subtype. A prediction model was established and may be used to help guide treatment strategies for colon cancer patients, e.g., after surgery. For example, detection of biomarkers or expression patterns may be used to select or identify colon cancer patients who may need further treatment due to the aggressive biological characteristics of their disease. A limited number of genes whose expression patterns can predict the survival of patients as well as their response to chemotherapy are provided herein.
  • An aspect of the present invention relates to a method of providing a prognosis or prediction for a subject determined to have a colorectal cancer, comprising: (a) obtaining expression information of biomarkers in a colorectal cancer sample of a subject by testing said sample, the biomarkers being at least ten genes selected from the group consisting of TMEM45A, ALOX5, DUSP4, ANOl, LOX, CD109, LOX, OLR1, FAP, CXCR4, CD55, SPON1, ANXA1, CD55, GAS1, MEIS2, BCAT1, ADAM 12, PLK2, TNFAIP6, POSTN, RGS2, PTGS2, DUSP4, RARRES1, COL11A1, SPP1, FCGR3B, RARRESl, SERPINB5, GPNMB, CRIPl, TNFAIP6, VGLL3, RARRESl, SFRP2, CTSE, COL10A1, KLK10, VNN1, AHNAK2, PTGS2,
  • Said obtaining expression information may comprise obtaining or receiving the sample.
  • the sample may be paraffin-embedded or frozen.
  • Said obtaining expression information may comprise RNA quantification, such as, e.g., cDNA microarray, quantitative RT-PCR, in situ hybridization, Northern blotting, or nuclease protection.
  • Said obtaining expression information may comprise protein quantification, such as, e.g., immunohistochemistry, an ELISA, a radioimmunoassay (RIA), an immunoradiometric assay, a fluoroimmunoassay, a chemiluminescent assay, a bioluminescent assay, a gel electrophoresis, or a Western blot analysis.
  • RNA quantification such as, e.g., cDNA microarray, quantitative RT-PCR, in situ hybridization, Northern blotting, or nuclease protection.
  • Said obtaining expression information may comprise protein quantification, such as,
  • Providing the prognosis or prediction may comprise generating a classifier based on the expression, wherein the classifier is defined as a weighted sum of expression levels of the biomarkers.
  • the classifier may be generated on a computer.
  • the classifier may be generated by a computer readable medium comprising machine executable instructions suitable for generating a classifier.
  • Providing the prognosis or prediction may comprise classifying a group of subjects based on the classifier associated with individual subjects in the group with a reference value.
  • the method may further comprise reporting said prognosis or prediction.
  • the method may further comprise prescribing or administering an adjuvant therapy to said subject based on said prediction.
  • the cancer may be a stage I cancer, a stage II cancer, a stage III cancer, or a stage IV cancer.
  • the cancer is not a stage IV cancer.
  • Another aspect of the present invention relates to an array comprising a plurality of antigen-binding fragments that bind to expression products of biomarkers or a plurality of primers or probes that bind to transcripts of the biomarkers to assess expression levels, the biomarkers comprising at least ten genes selected from the group consisting of TMEM45A, ALOX5, DUSP4, ANOl, LOX, CD109, LOX, OLR1, FAP, CXCR4, CD55, SPON1, ANXA1, CD55, GAS1, MEIS2, BCAT1, ADAM 12, PLK2, TNFAIP6, POSTN, RGS2, PTGS2, DUSP4, RARRES1, COL11A1, SPP1, FCGR3B, RARRESl, SERPINB5, GPNMB, CRIPl, TNFAIP6, VGLL3, RARRESl, SFRP2, CTSE, COL10A1, KLK10, VNN1,
  • kits comprising a plurality of antigen-binding fragments that bind to expression products of biomarkers or a plurality of primers or probes that bind to transcripts of the biomarkers to assess expression levels, the biomarkers comprising at least ten genes selected from the group consisting of TMEM45A, ALOX5, DUSP4, ANOl, LOX, CD109, LOX, OLR1, FAP, CXCR4, CD55, SPON1, ANXA1, CD55, GAS1, MEIS2, BCAT1, ADAM 12, PLK2, TNFAIP6, POSTN, RGS2, PTGS2, DUSP4, RARRESl, COL11A1, SPP1, FCGR3B, RARRESl, SERPINB5, GPNMB, CRIPl, TNFAIP6, VGLL3, RARRESl, SFRP2, CTSE, COL10A1, KLK10, VNN1, AHNAK2, PTGS2, CYP
  • the biomarkers may be measured in a sample either directly or indirectly.
  • a cancer sample is directly obtained from a subject at or near the laboratory or location where the biological sample will be analyzed.
  • the cancer sample may be obtained by a third party and then transferred, e.g., to a separate entity or location for analysis.
  • the sample may be obtained and tested in the same location using a point-of-care test.
  • said obtaining refers to receiving the sample, e.g., from the patient, from a laboratory, from a doctor's office, from the mail, courier, or post office, etc.
  • the method may further comprise reporting the determination or test results to the subject, a health care payer, an attending clinician, a pharmacist, a pharmacy benefits manager, or any person that the determination or test results may be of interest.
  • Embodiments discussed in the context of methods and/or compositions of the invention may be employed with respect to any other method or composition described herein. Thus, an embodiment pertaining to one method or composition may be applied to other methods and compositions of the invention as well.
  • FIG. 1 Kaplan-Meier plots of the prognosis of patients with colon cancer in the Moffit cohort. Patients were stratified according to AJCC stage or gene expression patterns (2 clusters). Recurrence free survival data are not available from 32 patients.
  • FIGS. 2A-B Construction of prediction model in the test cohort according to gene expression signatures from the Moffit cohort.
  • FIG. 2A Schematic overview of the strategy used for the construction of prediction models and evaluation of predicted outcomes based on gene expression signatures.
  • FIG. 2B Kaplan-Meier plots of OS. Patients were stratified according to AJCC stage or 2 subgroups predicted by compound covariate predictor (CCP). P values were obtained from the log-rank test. The + symbols in the panels indicate censored data.
  • CCP compound covariate predictor
  • FIGS. 3A-D Significant association of two subtypes with adjuvant chemotherapy.
  • CTX adjuvant chemotherapy
  • FIGS. 4A-B Subtype-specific gene expression patterns conserved in all 3 cohorts of colorectal cancer patients.
  • FIG. 4A Venn diagram of genes with expression that differed significantly between subtype A and B colorectal cancer patients in the 3 different cohorts. Univariate test (2-sample ?-test) with multivariate permutation test (10,000 random permutations) was applied. In each comparison, a cut-off P value of less than .001 was applied to retain genes with expression that differed significantly between the 2 groups of tissues examined.
  • FIG. 4B Expression patterns of selected genes shared in the 3 colon cancer cohorts. The expressions of only 755 genes were commonly upregulated or downregulated in all 3 cohorts.
  • FIG. 5 Gene set enrichment analysis of genes in prognostic gene expression signature. Fisher's exact test was applied to gene sets defined in Ingenuity Pathway Analysis database to identify enriched biological characteristics in prognostic gene expression signature.
  • FIGS. 6A-B Kaplan-Meier plots of OS colon cancer patients in VMP cohort.
  • patients in stage II (FIG. 6A) and III (FIG. 6B) were independently stratified by the signature.
  • P values were obtained from the log-rank test.
  • the + symbols in the panel indicate censored data.
  • FIGS. 7A-B Construction of prediction model in 2nd test (Melbourne) cohort according to gene expression signatures from Moffit cohort.
  • FIG. 7A Schematic overview of the strategy used for the construction of prediction models and evaluation of predicted outcomes based on gene expression signatures.
  • FIG. 7B Kaplan-Meier plots of DFS. Patients were stratified according to AJCC stage or two subgroups predicted by compound covariate predictor (CCP). P values were obtained from the log-rank test. The + symbols in the panel indicate censored data.
  • FIG. 8 Interaction of subgroups with adjuvant chemotherapy in patients with stage III colorectal cancer. Cox proportional hazard regression model was used to analyze interaction between subgroups and adjuvant chemotherapy treatment. Dotted lines represent 95% confidence interval of hazard ratios.
  • FIG. 9 TGF- ⁇ networks from Ingenuity® pathway analysis. Gene networks from Ingenuity® pathway analysis, showing networks of inter-connection among genes with expression significantly associated with the TGF- ⁇ pathway in conserved gene expression data from the 3 cohorts. Upregulated and downregulated genes in the H subgroup are indicated by red and green, respectively. The lines and arrows represent functional and physical interactions and directions of regulation, as demonstrated in the literature. Interactions with the TGF- ⁇ pathway are highlighted in bold light grey lines.
  • FIG. 10 NFkB networks from Ingenuity® pathway analysis.
  • the instant invention overcomes several major problems with current cancer prognosis in providing methods and compositions using novel combinations of biomarkers identified by expression profiling and survival analysis of colon cancer patients.
  • biomarkers have been identified that may be used predict response to chemotherapy and clinical outcome, e.g., overall survival or disease-free survival, in colorectal cancer patients.
  • cancer prognosis refers to a prediction of how a patient will progress, and whether there is a chance of recovery.
  • Cancer prognosis generally refers to a forecast or prediction of the probable course or outcome of the cancer.
  • cancer prognosis includes the forecast or prediction of any one or more of the following: duration of survival of a patient susceptible to or diagnosed with a cancer, duration of recurrence-free survival, duration of progression free survival of a patient susceptible to or diagnosed with a cancer, response rate in a group of patients susceptible to or diagnosed with a cancer, duration of response in a patient or a group of patients susceptible to or diagnosed with a cancer, and/or likelihood of metastasis in a patient susceptible to or diagnosed with a cancer.
  • Prognosis also includes prediction of favorable responses to cancer treatments, such as a conventional cancer therapy.
  • subject or “patient” is meant any single subject for which therapy is desired, including humans, cattle, dogs, guinea pigs, rabbits, chickens, and so on. Also intended to be included as a subject are any subjects involved in clinical research trials not showing any clinical sign of disease, or subjects involved in epidemiological studies, or subjects used as controls.
  • increased expression refers to an elevated or increased level of expression in a cancer sample relative to a suitable control (e.g., a non-cancerous tissue or cell sample, a reference standard), wherein the elevation or increase in the level of gene expression is statistically significant (p ⁇ 0.05).
  • Whether an increase in the expression of a gene in a cancer sample relative to a control is statistically significant can be determined using an appropriate t-test (e.g., one-sample t-test, two-sample t-test, Welch's t-test) or other statistical test known to those of skill in the art.
  • Genes that are overexpressed in a cancer can be, for example, genes that are known, or have been previously determined, to be overexpressed in a cancer.
  • decreased expression refers to a reduced or decreased level of expression in a cancer sample relative to a suitable control (e.g., a non-cancerous tissue or cell sample, a reference standard), wherein the reduction or decrease in the level of gene expression is statistically significant (p ⁇ 0.05).
  • the reduced or decreased level of gene expression can be a complete absence of gene expression, or an expression level of zero.
  • Whether a decrease in the expression of a gene in a cancer sample relative to a control is statistically significant can be determined using an appropriate ?-test (e.g., one-sample ?-test, two-sample t-test, Welch's ?-test) or other statistical test known to those of skill in the art.
  • Genes that are underexpressed in a cancer can be, for example, genes that are known, or have been previously determined, to be underexpressed in a cancer.
  • the marker level may be compared to the level of the marker from a control, wherein the control may comprise one or more tumor samples (e.g., colon cancer samples) taken from one or more patients determined as having a good prognosis ("good prognosis” control) or a poor prognosis (“poor prognosis” control), or both.
  • the control may comprise one or more tumor samples (e.g., colon cancer samples) taken from one or more patients determined as having a good prognosis ("good prognosis” control) or a poor prognosis (“poor prognosis” control), or both.
  • the control may comprise data obtained at the same time (e.g., in the same hybridization experiment) as the patient's individual data, or may be a stored value or set of values, e.g. stored on a computer, or on computer-readable media. If the latter is used, new patient data for the selected marker(s), obtained from initial or follow-up samples, can be compared to the stored data for the same marker(s) without the need for additional control experiments.
  • a good or bad prognosis may, for example, be assessed in terms of patient survival, likelihood of disease recurrence or disease metastasis (patient survival, disease recurrence and metastasis may for example be assessed in relation to a defined timepoint, e.g. at a given number of years after cancer surgery (e.g. surgery to remove one or more tumors) or after initial diagnosis.
  • a good or bad prognosis may be assessed in terms of overall survival or disease-free survival.
  • a "good prognosis” may refer to an increased likelihood that a patient afflicted with cancer, particularly colon cancer, will remain disease-free (i.e., cancer-free).
  • "Poor prognosis” may refer to an increased likelihood of a relapse or recurrence of the underlying cancer or tumor, metastasis, or death. Cancer patients classified as having a "good prognosis” may have an increased likelihood of remaining free of the underlying cancer or tumor. In contrast, "bad prognosis” cancer patients may have an increased likelihood of experiencing disease relapse, tumor recurrence, metastasis, or death.
  • the time frame for assessing prognosis and outcome is, for example, less than one year, one, two, three, four, five, six, seven, eight, nine, ten, fifteen, twenty, or more years.
  • the relevant time for assessing prognosis or disease-free survival time may begin with the surgical removal of the tumor or suppression, mitigation, or inhibition of tumor growth.
  • a "good prognosis" refers to the likelihood that a colon cancer patient will remain free of the underlying cancer or tumor for a period of at least five years, such as for a period of at least ten years.
  • a "poor prognosis” refers to the likelihood that a colon cancer patient will experience disease relapse, tumor recurrence, metastasis, or death within less than ten years, such as less than five years. Time frames for assessing prognosis and outcome provided herein are illustrative and are not intended to be limiting.
  • high risk means the patient is expected to have a relapse in a shorter period less than a predetermined value (for example, from a control), for example in less than 5 years, preferably in less than 3 years.
  • low risk means the patient is expected to have a relapse in a shorter period more than a predetermined value, for example, after 5 years, or in more than 3 years. Time frames for assessing risks provided herein are illustrative and are not intended to be limiting.
  • the term "antigen binding fragment” herein is used in the broadest sense and specifically covers intact monoclonal antibodies, polyclonal antibodies, multispecific antibodies (e.g. bispecific antibodies) formed from at least two intact antibodies, and antibody fragments.
  • the term "primer,” as used herein, is meant to encompass any nucleic acid that is capable of priming the synthesis of a nascent nucleic acid in a template-dependent process. Primers may be oligonucleotides from ten to twenty and/or thirty base pairs in length, but longer sequences can be employed. Primers may be provided in double-stranded and/or single-stranded form, although the single-stranded form is preferred.
  • the biomarkers as used herein may be related to cancer prognosis, for example, prediction of survival, recurrence, or therapy response.
  • the differential patterns of expression of a plurality of these biomarkers may be used to predict the survival outcome of a subject with cancer. Certain biomarkers tend to be over-expressed in long-term survivors, whereas other biomarkers tend to be over-expressed in short-term survivors.
  • the unique pattern of expression of a plurality of biomarkers in a subject i.e., the gene signature
  • Subjects with a high risk score may have a short survival time (e.g., less than about 2 years) after surgical resection.
  • Subjects with a low risk score may have a longer survival time (e.g., more than about 3 years) after resection.
  • the expression of each biomarker may be converted into an expression value. These expression values then will be used to calculate a risk score of survival for a subject with cancer using statistical methods well known in the art.
  • the risk scores may be calculated using a principal components analysis.
  • the risk scores may also be calculated using a partial Cox regression analysis.
  • the risk scores may be calculated using a univariate Cox regression analysis.
  • the scores generated may be used to classify patients into high or low risk score, wherein a high risk score is associated with a poor prognosis, such as a short survival time or a poorer survival, and a low risk score is associated with a good prognosis, such as a long survival time or a better survival.
  • the cut-off value may be derived from a control group of cancer patients as a median risk score.
  • the risk score might be developed by incorporating genomic data from surrounding tissues that does not overlap with but is complementary to those from tumor tissues.
  • the risk score may also be combined with other clinical characteristics or demographic information.
  • a tissue sample may be collected from a subject with a cancer, for example, a colon cancer.
  • the collection step may comprise surgical resection.
  • the sample of tissue may be stored in RNAlater or flash frozen, such that RNA may be isolated at a later date.
  • RNA may be isolated from the tissue and used to generate labeled probes for a nucleic acid microarray analysis.
  • the RNA may also be used as a template for qRT-PCR in which the expression of a plurality of biomarkers is analyzed.
  • the expression data generated may be used to derive a risk score, e.g., using the Cox regression classification method to obtain regression coefficients as the weight of each corresponding biomarker gene expression.
  • the risk score may be used to predict whether the subject will be a short-term or a long-term cancer survivor.
  • Biomarker genes that may be used in cancer prognosis or risk score generation may be one or more selected from Table 1 below.
  • the expression of a plurality of biomarkers may be measured in a sample of cells from a subject with cancer.
  • the type and classification of the cancer can and will vary.
  • the cancer may be an early stage cancer, i.e., stage I or stage II, or it may be a late stage cancer, i.e., stage III or stage IV.
  • the cancer may be a cancer of the colon.
  • Colon cancer is properly considered to be a cancer which starts in the colon, as opposed to a cancer which originates in another organ and migrates to the colon, known as a colon metastasis.
  • the most frequent colon cancer is adenocarcinoma.
  • surgical resection for colon cancer may provide the best chance for cure, the prognosis after surgery differs considerably among patients. Because of this clinical heterogeneity, predicting the recurrence or survival of colon cancer patients after surgical resection or chemotherapy remains challenging.
  • the present invention may also be used to predict prognosis, disease-free survival, or overall survival after treatment of a colon cancer.
  • a subject with a colon cancer may be treated via a surgery, a chemotherapeutic (e.g., leucovorin, leucovorin, 5-FU, capecitabine, irinotecan, oxaliplatin, bevacizumab, cetuximab, or panitumumab), or a radiation therapy.
  • chemotherapeutic e.g., leucovorin, leucovorin, 5-FU, capecitabine, irinotecan, oxaliplatin, bevacizumab, cetuximab, or panitumumab
  • a metastatic tumor such as a colon cancer metastasis, during treatment.
  • the colon cancer may comprise an adenocarcinoma (e.g., a mucinous (colloid) adenocarcinoma or a signet ring adenocarcinoma), a scirrhous tumor, or a neuroendocrine turor.
  • adenocarcinoma e.g., a mucinous (colloid) adenocarcinoma or a signet ring adenocarcinoma
  • Tumors with neuroendocrine differentiation typically have a poorer prognosis than pure adenocarcinoma variants (Saclarides et al., 1994).
  • anal cancer bladder cancer, bone cancer, brain cancer, breast cancer, cervical cancer, liver cancer, duodenal cancer, endometrial cancer, eye cancer, gallbladder cancer, head and neck cancer, larynx cancer, non-small cell lung cancer, small cell lung cancer, lymphomas, melanoma, mouth cancer, ovarian cancer, pancreatic cancer, penal cancer, prostate cancer, rectal cancer, renal cancer, skin cancer, testicular cancer, thyroid cancer
  • the sample of cells or tissue sample may be obtained from the subject with cancer by biopsy or surgical resection.
  • the type of biopsy can and will vary, depending upon the location and nature of the cancer.
  • a sample of cells, tissue, or fluid may be removed by needle aspiration biopsy. For this, a fine needle attached to a syringe is inserted through the skin and into the organ or tissue of interest.
  • the needle may be guided to the region of interest using ultrasound or computed tomography (CT) imaging.
  • CT computed tomography
  • a vacuum is created with the syringe such that cells or fluid may be sucked through the needle and collected in the syringe.
  • a sample of cells or tissue may also be removed by incisional or core biopsy. For this, a cone, a cylinder, or a tiny bit of tissue is removed from the region of interest.
  • CT imaging, ultrasound, or an endoscope is generally used to guide this type of biopsy.
  • the entire cancerous lesion may be removed by excisional biopsy or surgical resection.
  • RNA or protein may also be extracted from a fixed or wax-embedded tissue sample.
  • the subject with cancer may be a mammalian subject.
  • Mammals may include primates, livestock animals, and companion animals. Primates may include humans, New World monkeys, Old World monkeys, gibbons, and great apes.
  • Livestock animals may include horses, cows, goats, sheep, deer (including reindeer), and pigs.
  • Companion animals may include dogs, cats, rabbits, and rodents (including mice, rats, and guinea pigs).
  • the subject is a human.
  • this invention entails measuring expression of one or more prognostic biomarkers in a sample of cells from a subject with cancer.
  • the expression information may be obtained by testing cancer samples by a lab, a technician, a device, or a clinician.
  • the pattern or signature of expression in each cancer sample may then be used to generate a risk score for cancer prognosis or classification, such as predicting cancer survival or recurrence.
  • the level of expression of a biomarker may be increased or decreased in a subject relative to other subjects with cancer.
  • the expression of a biomarker may be higher in long-term survivors than in short-term survivors.
  • the expression of a biomarker may be higher in short-term survivors than in long-term survivors.
  • Expression of one or more of biomarkers identified by the inventors may be assessed to predict or report prognosis or prescribe treatment options for cancer patients, especially colon cancer patients.
  • the expression of one or more biomarkers may be measured by a variety of techniques that are well known in the art. Quantifying the levels of the messenger RNA (mRNA) of a biomarker may be used to measure the expression of the biomarker. Alternatively, quantifying the levels of the protein product of a biomarker may be used to measure the expression of the biomarker. Additional information regarding the methods discussed below may be found in Ausubel et al. (2003) or Sambrook et al. (1989). As would be recognized by one of skill in the art, various parameters may be manipulated to optimize detection of the mRNA or protein of interest. [0056] A nucleic acid microarray may be used to quantify the differential expression of a plurality of biomarkers.
  • mRNA messenger RNA
  • a nucleic acid microarray may be used to quantify the differential expression of a plurality of biomarkers.
  • Microarray analysis may be performed using commercially available equipment, following manufacturer's protocols, such as by using the Affymetrix GeneChip® technology (Santa Clara, CA) or the Microarray System from Incyte (Fremont, CA).
  • single-stranded nucleic acids e.g., cDNAs or oligonucleotides
  • the arrayed sequences are then hybridized with specific nucleic acid probes from the cells of interest.
  • Fluorescently labeled cDNA probes may be generated through incorporation of fluorescently labeled deoxynucleotides by reverse transcription of RNA extracted from the cells of interest.
  • the R A may be amplified by in vitro transcription and labeled with a marker, such as biotin.
  • the labeled probes are then hybridized to the immobilized nucleic acids on the microchip under highly stringent conditions. After stringent washing to remove the non-specifically bound probes, the chip is scanned by confocal laser microscopy or by another detection method, such as a CCD camera.
  • the raw fluorescence intensity data in the hybridization files are generally preprocessed with the robust multichip average (RMA) algorithm to generate expression values.
  • RMA robust multichip average
  • Quantitative real-time PCR may also be used to measure the differential expression of a plurality of biomarkers.
  • the RNA template is generally reverse transcribed into cDNA, which is then amplified via a PCR reaction.
  • the amount of PCR product is followed cycle-by-cycle in real time, which allows for determination of the initial concentrations of mRNA.
  • the reaction may be performed in the presence of a fluorescent dye, such as SYBR Green, which binds to double- stranded DNA.
  • the reaction may also be performed with a fluorescent reporter probe that is specific for the DNA being amplified.
  • a non-limiting example of a fluorescent reporter probe is a TaqMan® probe (Applied Biosystems, Foster City, CA).
  • the fluorescent reporter probe fluoresces when the quencher is removed during the PCR extension cycle.
  • Multiplex qRT-PCR may be performed by using multiple gene-specific reporter probes, each of which contains a different fluorophore.
  • Fluorescence values are recorded during each cycle and represent the amount of product amplified to that point in the amplification reaction.
  • qRT-PCR may be performed using a reference standard.
  • the ideal reference standard is expressed at a constant level among different tissues, and is unaffected by the experimental treatment.
  • Suitable reference standards include, but are not limited to, mRNAs for the housekeeping genes glyceraldehyde-3-phosphate-dehydrogenase (GAPDH) and ⁇ -actin.
  • GPDH glyceraldehyde-3-phosphate-dehydrogenase
  • ⁇ -actin The level of mRNA in the original sample or the fold change in expression of each biomarker may be determined using calculations well known in the art.
  • Immunohistochemical staining may also be used to measure the differential expression of a plurality of biomarkers. This method enables the localization of a protein in the cells of a tissue section by interaction of the protein with a specific antibody.
  • the tissue may be fixed in formaldehyde or another suitable fixative, embedded in wax or plastic, and cut into thin sections (from about 0.1 mm to several mm thick) using a microtome.
  • the tissue may be frozen and cut into thin sections using a cryostat.
  • the sections of tissue may be arrayed onto and affixed to a solid surface (i.e., a tissue microarray).
  • the sections of tissue are incubated with a primary antibody against the antigen of interest, followed by washes to remove the unbound antibodies.
  • the primary antibody may be coupled to a detection system, or the primary antibody may be detected with a secondary antibody that is coupled to a detection system.
  • the detection system may be a fluorophore or it may be an enzyme, such as horseradish peroxidase or alkaline phosphatase, which can convert a substrate into a colorimetric, fluorescent, or chemiluminescent product.
  • the stained tissue sections are generally scanned under a microscope. Because a sample of tissue from a subject with cancer may be heterogeneous, i.e., some cells may be normal and other cells may be cancerous, the percentage of positively stained cells in the tissue may be determined. This measurement, along with a quantification of the intensity of staining, may be used to generate an expression value for the biomarker.
  • An enzyme-linked immunosorbent assay may be used to measure the differential expression of a plurality of biomarkers.
  • an ELISA assay There are many variations of an ELISA assay. All are based on the immobilization of an antigen or antibody on a solid surface, generally a microtiter plate.
  • the original ELISA method comprises preparing a sample containing the biomarker proteins of interest, coating the wells of a microtiter plate with the sample, incubating each well with a primary antibody that recognizes a specific antigen, washing away the unbound antibody, and then detecting the antibody-antigen complexes. The antibody-antibody complexes may be detected directly.
  • the primary antibodies are conjugated to a detection system, such as an enzyme that produces a detectable product.
  • the antibody-antibody complexes may be detected indirectly.
  • the primary antibody is detected by a secondary antibody that is conjugated to a detection system, as described above.
  • the microtiter plate is then scanned and the raw intensity data may be converted into expression values using means known in the art.
  • An antibody microarray may also be used to measure the differential expression of a plurality of biomarkers.
  • a plurality of antibodies is arrayed and covalently attached to the surface of the microarray or biochip.
  • a protein extract containing the biomarker proteins of interest is generally labeled with a fluorescent dye.
  • the labeled biomarker proteins are incubated with the antibody microarray. After washes to remove the unbound proteins, the microarray is scanned.
  • the raw fluorescent intensity data may be converted into expression values using means known in the art.
  • Luminex multiplexing microspheres may also be used to measure the differential expression of a plurality of biomarkers.
  • These microscopic polystyrene beads are internally color-coded with fluorescent dyes, such that each bead has a unique spectral signature (of which there are up to 100). Beads with the same signature are tagged with a specific oligonucleotide or specific antibody that will bind the target of interest (i.e., biomarker mRNA or protein, respectively).
  • the target is also tagged with a fluorescent reporter.
  • there are two sources of color one from the bead and the other from the reporter molecule on the target.
  • the beads are then incubated with the sample containing the targets, of which up 100 may be detected in one well.
  • the small size/surface area of the beads and the three dimensional exposure of the beads to the targets allows for nearly solution-phase kinetics during the binding reaction.
  • the captured targets are detected by high-tech fluidics based upon flow cytometry in which lasers excite the internal dyes that identify each bead and also any reporter dye captured during the assay.
  • the data from the acquisition files may be converted into expression values using means known in the art.
  • In situ hybridization may also be used to measure the differential expression of a plurality of biomarkers.
  • This method permits the localization of mRNAs of interest in the cells of a tissue section.
  • the tissue may be frozen, or fixed and embedded, and then cut into thin sections, which are arrayed and affixed on a solid surface.
  • the tissue sections are incubated with a labeled antisense probe that will hybridize with an mRNA of interest.
  • the hybridization and washing steps are generally performed under highly stringent conditions.
  • the probe may be labeled with a fluorophore or a small tag (such as biotin or digoxigenin) that may be detected by another protein or antibody, such that the labeled hybrid may be detected and visualized under a microscope.
  • each antisense probe may be detected simultaneously, provided each antisense probe has a distinguishable label.
  • the hybridized tissue array is generally scanned under a microscope. Because a sample of tissue from a subject with cancer may be heterogeneous, i.e., some cells may be normal and other cells may be cancerous, the percentage of positively stained cells in the tissue may be determined. This measurement, along with a quantification of the intensity of staining, may be used to generate an expression value for each biomarker.
  • the number of biomarkers whose expression is measured in a sample of cells from a subject with cancer may vary. Since the risk score is based upon the differential expression of the biomarkers, a higher degree of accuracy should be attained when the expression of more biomarkers is measured; however, a large number of biomarkers in the gene signature would hamper the clinical usefulness. In a certain embodiment, the differential expression of a selected number of biomarkers may be measured.
  • expression information of the biomarkers may be analyzed by statistical and informatical methods to help provide prognosis prediction and treatment prescription.
  • Those methods may comprise processing the test data stored on a data storage device by using a tangible computer readable medium having computer usable program code executable to perform operations for the statistic analysis and prediction/prescription output, or for assisting risk score generation as described above.
  • the Kaplan-Meier method (also known as the product limit estimator) estimates the survival function from life-time data. In medical research, it might be used to measure the fraction of patients living for a certain amount of time after treatment.
  • a plot of the Kaplan-Meier method of the survival function is a series of horizontal steps of declining magnitude which, when a large enough sample is taken, approaches the true survival function for that population.
  • the value of the survival function between successive distinct sampled observations ("clicks") is assumed to be constant.
  • Kaplan-Meier curve An important advantage of the Kaplan-Meier curve is that the method can take into account "censored" data— losses from the sample before the final outcome is observed (for instance, if a patient withdraws from a study). On the plot, small vertical tick-marks indicate losses, where patient data has been censored. When no truncation or censoring occurs, the Kaplan-Meier curve is equivalent to the empirical distribution. [0071] A method might involve grouping patients into categories, for instance, those with Gene A profile and those with Gene B profile. In the graph, patients with Gene B die much more quickly than those with gene A. After two years, about 80% of the Gene A patients still survive, but less than half of Gene B patients still survive. B. Log-rank test
  • the log-rank test (sometimes called the Mantel-Cox test) is a hypothesis test to compare the survival distributions of two samples. It is a nonparametric test and appropriate to use when the data are right censored (technically, the censoring must be non- informative). It is widely used in clinical trials to establish the efficacy of new drugs compared to a control group (often a placebo) when the measurement is the time to event (such as a heart attack).
  • the log-rank test statistic compares estimates of the hazard functions of the two groups at each observed event time. It is constructed by computing the observed and expected number of events in one of the groups at each observed event time and then adding these to obtain an overall summary across all time points where there is an event.
  • the log-rank statistic can be derived as the score test for the Cox proportional hazards model comparing two groups. It is therefore asymptotically equivalent to the likelihood ratio test statistic based from that model.
  • Proportional hazards models are a sub-class of survival models in statistics. For the purposes of simplification, consider survival models to consist of two parts: the underlying hazard function, describing how hazard (risk) changes over time, and the effect parameters, describing how hazard relates to other factors - such as the choice of treatment, in a medical example.
  • the proportional hazards assumption is the assumption that effect parameters multiply hazard: for example, if taking drug X halves a hazard at time 0, it also halves the hazard at time 1, or time 0.5, or time t for any value of t.
  • the effect parameter(s) estimated by any proportional hazards model can be reported as hazard ratios.
  • Clustering is the assignment of objects into groups (called clusters) so that objects from the same cluster are more similar to each other than objects from different clusters. Often similarity is assessed according to a distance measure. Clustering is a common technique for statistical data analysis, which is used in many fields, including machine learning, data mining, pattern recognition, image analysis, and bioinformatics.
  • Hierarchical clustering builds (agglomerative), or breaks up (divisive), a hierarchy of clusters.
  • the traditional representation of this hierarchy is a tree (called a dendrogram), with individual elements at one end and a single cluster containing every element at the other.
  • Agglomerative algorithms begin at the leaves of the tree, whereas divisive algorithms begin at the root.
  • the former method builds the hierarchy from the individual elements by progressively merging clusters.
  • transcriptomics clustering is used to build groups of genes with related expression patterns (also known as coexpressed genes). Often such groups contain functionally related proteins, such as enzymes for a specific pathway, or genes that are co-regulated.
  • ESTs expressed sequence tags
  • DNA microarrays can be a powerful tool for genome annotation, a general aspect of genomics.
  • Biomarkers and a new "risk score" system that can predict the likelihood of recurrence or overall survival in colon cancer patients, as disclosed herein, may be used to identify patients who can benefit from a conventional single or combined modality therapy, prior to the treatment of the cancer. Further, methods provided herein may be used to identify patients who may not substantially benefit from a conventional single or combined modality therapy; such patients may benefit more from alternative treatment(s).
  • a conventional cancer therapy may be administered to a subject wherein the subject is identified or reported as having a good prognosis based on the assessment of a group of biomarkers as described herein.
  • An alternative cancer therapy may be administered to a subject, e.g., alone or in combination with a conventional cancer therapy, if the subject is identified as having a poor prognosis via a method or kit disclosed herein.
  • Conventional cancer therapies include one or more therapy, such as a chemotherapy, radiotherapy, and/or a surgery.
  • a subject with a colon cancer may be treated via a surgery, a chemotherapeutic, e.g., leucovorin, leucovorin, 5-FU, capecitabine, irinotecan, oxaliplatin, bevacizumab, cetuximab, or panitumumab, or one or more combination of the foregoing.
  • a chemotherapeutic e.g., leucovorin, leucovorin, 5-FU, capecitabine, irinotecan, oxaliplatin, bevacizumab, cetuximab, or panitumumab, or one or more combination of the foregoing.
  • Chemotherapies include, for example, cisplatin (CDDP), carboplatin, procarbazine, mechlorethamine, cyclophosphamide, camptothecin, ifosfamide, melphalan, chlorambucil, busulfan, nitrosurea, dactinomycin, daunorubicin, doxorubicin, bleomycin, plicomycin, mitomycin, etoposide (VP 16), tamoxifen, raloxifene, estrogen receptor binding agents, taxol, gemcitabien, navelbine, farnesyl-protein tansferase inhibitors, transplatinum, 5- fluorouracil, vincristin, vinblastine, and methotrexate, or any analog or derivative variant of the foregoing.
  • CDDP cisplatin
  • carboplatin carboplatin
  • procarbazine mechlorethamine
  • cyclophosphamide campto
  • Radiation therapies generally cause DNA damage and have been used extensively.
  • a radiation therapy may include administrationof ⁇ -rays, X-rays, and/or the directed delivery of a radioisotope to tumor cells.
  • Other forms of DNA damaging factors are also contemplated, such as microwaves and UV-irradiation. These factors may effect a broad range of damage on DNA, on the precursors of DNA, on the replication and repair of DNA, and on the assembly and maintenance of chromosomes.
  • Dosage ranges for X-rays range from daily doses of 50 to 200 roentgens for prolonged periods of time (3 to 4 wk), to single doses of 2000 to 6000 roentgens.
  • Dosage ranges for radioisotopes vary widely, and depend on the half-life of the isotope, the strength and type of radiation emitted, and the uptake by the neoplastic cells.
  • the terms "contacted” and "exposed,” when applied to a cell, are used herein to describe the process by which a therapeutic construct and a chemotherapeutic or radiotherapeutic agent are delivered to a target cell or are placed in direct juxtaposition with the target cell. To achieve cell killing or stasis, both agents are delivered to a cell in a combined amount effective to kill the cell or prevent it from dividing.
  • Curative surgery is a cancer treatment that may be used in conjunction with other therapies, such as a chemotherapy, radiotherapy, hormonal therapy, gene therapy, immunotherapy, and/or alternative therapies.
  • Curative surgery includes resection in which all or part of cancerous tissue is physically removed, excised, and/or destroyed.
  • Tumor resection refers to physical removal of at least part of a tumor.
  • treatment by surgery includes laser surgery, cryosurgery, electrosurgery, and microscopically-controlled surgery (Mohs' surgery). It is further contemplated that the present invention may be used in conjunction with removal of superficial cancers, precancers, or incidental amounts of normal tissue.
  • Laser therapy is the use of high-intensity light to destroy tumor cells. Laser therapy affects the cells only in the treated area. Laser therapy may be used to destroy cancerous tissue and relieve a blockage in the esophagus when the cancer cannot be removed by surgery. The relief of a blockage can help to reduce symptoms, especially swallowing problems.
  • Photodynamic therapy a type of laser therapy, involves the use of drugs that are absorbed by cancer cells; when exposed to a special light, the drugs become active and destroy the cancer cells.
  • a cavity may be formed in the body. Treatment may be accomplished by perfusion, direct injection or local application of the area with an additional anti-cancer therapy. Such treatment may be repeated, for example, every 1, 2, 3, 4, 5, 6, or 7 days, or every 1, 2, 3, 4, and 5 weeks or every 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, or 12 months. These treatments may be of varying dosages as well.
  • Alternative cancer therapies generally include any cancer therapy other than surgery, chemotherapy and radiation therapy.
  • Alternative cancer therapies include immunotherapy, gene therapy, hormonal therapy or a combination thereof.
  • Immunotherapeutics generally, rely on the use of immune effector cells and molecules to target and destroy cancer cells.
  • the immune effector may be, for example, an antibody specific for some marker on the surface of a tumor cell.
  • the antibody alone may serve as an effector of therapy or it may recruit other cells to actually affect cell killing.
  • the antibody also may be conjugated to a drug or toxin (chemotherapeutic, radionuclide, ricin A chain, cholera toxin, pertussis toxin, etc.) and serve merely as a targeting agent.
  • the effector may be a lymphocyte carrying a surface molecule that interacts, either directly or indirectly, with a tumor cell target.
  • Various effector cells include cytotoxic T cells and NK cells.
  • Gene therapy generally involves the insertion of polynucleotides, including DNA or RNA, into an individual's cells and tissues to treat a disease.
  • Antisense therapy is also a form of gene therapy.
  • a therapeutic polynucleotide may be administered before, after, or at the same time as a first cancer therapy. Delivery of a vector encoding a variety of proteins is encompassed within the invention. For example, cellular expression of the exogenous tumor suppressor oncogenes may exert their function to inhibit excessive cellular proliferation, such as p53, pi 6, and C-CAM.
  • Additional agents to be used to improve the therapeutic efficacy of treatment include immunomodulatory agents, agents that affect the upregulation of cell surface receptors and GAP junctions, cytostatic and differentiation agents, inhibitors of cell adhesion, or agents that increase the sensitivity of the hyperproliferative cells to apoptotic inducers.
  • Immunomodulatory agents include tumor necrosis factor; interferon alpha, beta, and gamma; IL-2 and other cytokines; F42K and other cytokine analogs; or MIP-1, MIP-lbeta, MCP-1, RANTES, and other chemokines.
  • cell surface receptors or their ligands such as Fas / Fas ligand, DR4 or DR5 / TRAIL
  • Fas / Fas ligand DR4 or DR5 / TRAIL
  • cytostatic or differentiation agents can be used in combination with the present invention to improve the anti-hyperproliferative efficacy of the treatments.
  • Inhibitors of cell adhesion are contemplated to improve the efficacy of the present invention.
  • cell adhesion inhibitors are focal adhesion kinase (FAKs) inhibitors and Lovastatin. It is further contemplated that other agents that increase the sensitivity of a hyperproliferative cell to apoptosis, such as the antibody c225, could be used in combination with the present invention to improve the treatment efficacy.
  • FAKs focal adhesion kinase
  • Lovastatin Lovastatin
  • Hormonal therapy may also be used in the present invention or in combination with any other cancer therapy previously described.
  • the use of hormones may be employed in the treatment of certain cancers, such as breast, prostate, ovarian, or cervical cancer, to lower the level or block the effects of certain hormones such as testosterone or estrogen. This treatment is often used in combination with at least one other cancer therapy as a treatment option or to reduce the risk of metastases.
  • kits for performing the diagnostic and prognostic methods of the invention can be prepared from readily available materials and reagents.
  • such kits can comprise any one or more of the following materials: enzymes, reaction tubes, buffers, detergent, primers, and probes.
  • these kits allow a practitioner to obtain samples of neoplastic cells in blood, tears, semen, saliva, urine, tissue, serum, stool, sputum, cerebrospinal fluid, and supernatant from cell lysate.
  • these kits include the needed apparatus for performing R A extraction, RT-PCR, and gel electrophoresis. Instructions for performing the assays can also be included in the kits.
  • kits may comprise a plurality of agents for assessing the differential expression of a plurality of biomarkers, wherein the kit is housed in a container.
  • the kits may further comprise instructions for using the kit for assessing expression, means for converting the expression data into expression values and/or means for analyzing the expression values to generate scores that predict survival or prognosis.
  • the agents in the kit for measuring biomarker expression may comprise a plurality of PCR probes and/or primers for qRT-PCR and/or a plurality of antibody or fragments thereof for assessing expression of the biomarkers.
  • the agents in the kit for measuring biomarker expression may comprise an array of polynucleotides complementary to the mRNAs of the biomarkers of the invention. Possible means for converting the expression data into expression values and for analyzing the expression values to generate scores that predict survival or prognosis may be also included.
  • BRB-ArrayTools (linus.nci.nih.gov/BRB-ArrayTools.html) were used primarily for statistical analysis of gene expression data (Simon et ah, 2007), and all other statistical analyses were performed in the R language environment (www.r-project.org). All gene expression data were generated by using the Affymetrix U133 version 2.0 platform except for the Max Planck Institute cohort. Data from the Max Planck Institute were generated by using the Affymetrix U133A platform. Raw data were downloaded from public databases and normalized using a robust multi-array averaging method (Irizarry et al., 2003). Genes that were differentially expressed among the 2 classes were identified using a random-variance t- test.
  • the robustness of the classifier was estimated by misclassification rate determined during the leave-one-out cross-validation (LOOCV) in the training set. For each prediction, training of the classifier was done independently and the misclassification rate was calculated during each training. When applied to the independent validation sets (VMP and Melbourne cohorts), prognostic significance was estimated by Kaplan-Meier plots and log-rank tests between 2 predicted subgroups of patients. After LOOCV, sensitivity and specificity of prediction models were estimated by the fraction of samples correctly predicted. Multivariate Cox proportional hazard regression analysis was used to evaluate independent prognostic factors associated with survival, and gene signature, tumor stage, and pathologic characteristics were used as covariates. Cox proportional hazard regression model was also used to analyze interaction between subgroups and adjuvant chemotherapy treatment. A P value of less than 0.05 was considered to indicate statistical significance, and all tests were 2-tailed.
  • IngenuityTM Pathways Analysis (IPA, Ingenuity Systems®, Redwood City, CA) was used for gene set enrichment analysis and gene network analysis. Gene set enrichment analysis was carried out to identify the most significant gene sets associated with disease process, molecular and cellular functions, and normal physiological and development condition in 114 prognostic genes as described in instruction from Ingenuity Systems. The significance of over-represented gene sets was estimated by the right-tailed Fisher's Exact Test. Gene network analysis was carried out by using a global molecular network developed from information contained in the IngenuityTM Knowledge Base. Seven hundred fifty-five gene features were mapped to the Ingenuity Knowledge Base. Identified gene networks were ranked according to scores provided by IPA.
  • the score is the likelihood of a set of genes being found in the networks due to random chance. For example, a score of 3 indicates that there is a 1/1000 chance that the focus genes are in a network due to random chance.
  • CCP compound covariate predictor
  • the following paragraphs describe the calculations used in BRB-ArrayTools for predictive classification using the compound covariate predictor (CCP), diagonal linear discriminant analysis, and the Bayesian CCP. Cross-validation or bootstrap re-sampling is performed in order to provide a proper estimate of the prediction accuracy of the classifiers. A completely specified classifier is developed on the training set and used to classify the cases in the test set. The paragraphs below describe the classifier development algorithms that are applied in each training set.
  • CCP compound covariate predictor
  • X ij denote the log expression for gene j in sample i of the training set. For each sample of the training set we compute the compound covariate value
  • Selected denotes the pre-defined prognostic genes for that training set. If C is closer to than to then the case is predicted to be class 1 and the reverse if it is closer to . This is using as the threshold of classification.
  • the DLDA predictor is similar to the CCP but the weight for the importance of gene j which is t j for the CCP replaced by t j /s j where is the pooled estimate of intra-class variance
  • the predictive index C i is computed for all of the training set samples i
  • Bayesian CCP For a given training set one computes the C i compound covariate values for all training set samples i and the class means and as described above for the CCP.
  • the Bayesian Compound Covariate method was developed by GW Wright et al. (A gene expression-based method to diagnose clinically distinct subgroups of diffuse large B cell lymphoma, Wright et al., 2003).
  • the implementation in BRB- ArrayTools uses a pooled-sample variance estimate V in order to improve stability of classification when the number of samples is small.
  • the prediction rule is defined by the inner sum of the weights (w i ) and expression (x i ) of genes. A sample is classified to the class A if the sum is greater than the threshold; that is, ⁇ i w i x i > threshold.
  • the threshold for the Compound Covariate predictor is -90.282
  • Adjuvant chemotherapy data were available for 328 of the 390 patients from the 3 cohorts.
  • the inventors next determined the association of the new prognostic gene expression signature with response to chemotherapy.
  • stage III 111
  • patients with stage III disease were subdivided into 2 subtypes (A or B), and the difference in DFS was independently assessed.
  • stage II patients at high risk of recurrence whom might benefit from 5-fluorouracil-based adjuvant chemotherapy.
  • This analysis was limited to patients with stage III cancer because the number of patients with stage II disease who received chemotherapy was too small in the current study cohort.
  • the use of new predictive gene signatures may help reduce the number of patients needed in prospective clinical trials to estimate the benefit of chemotherapy in stage II patients by identifying patients at higher risk in advance of treatment.
  • SRC family kinases Activation of SRC family kinases in poorer prognostic subtype is in good agreement with previous studies showing that SRC or its related kinase activity increases in colorectal tumors relative to adjacent mucosa, with the highest activity observed in metastases and correlates inversely with patient survival (Yeatman, 2004; Lieu and Kopetz, 2010). Therefore, these genes overexpressed in patients with subtype B well reflect the aggressiveness of colorectal cancer cells. SRC -targeted agents, that are now in advanced clinical development for patients with solid tumors, might be good candidates for targeted therapy for patients in subtype B (Saad and Lipton, 2010).
  • This new signature may overcome current limitations of biomarkers of colorectal cancer.
  • MSI microsatellite instability
  • EGFR inhibitors Although the predictive values of MSI to adjuvant chemotherapy and of KRAS mutations to the use of EGFR inhibitors have been established, these markers are only useful as negative markers for treatments (Karapetis et al., 2008; Ribic et al., 2003). Thus, these markers fail to predict which patients will benefit from treatments.

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Abstract

L'invention concerne des procédés et des compositions pour le pronostic et la classification d'un cancer, en particulier le cancer du côlon. Par exemple, l'invention concerne, dans certains aspects, des procédés de pronostic du cancer du côlon employant l'analyse de l'expression de marqueurs biologiques sélectionnés. En particulier, un score de risque peut être mis au point pour obtenir un pronostic du cancer.
PCT/US2012/058453 2011-10-03 2012-10-02 Score de risque pronostique de cancer du côlon basé sur des marqueurs Ceased WO2013052480A1 (fr)

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Cited By (21)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
WO2015018308A1 (fr) * 2013-08-06 2015-02-12 BGI Shenzhen Co.,Limited Biomarqueurs pour le cancer colorectal
EP3056576A3 (fr) * 2007-10-23 2016-09-28 Clinical Genomics Pty Ltd Procédé permettant de diagnostiquer des néoplasmes
CN106771203A (zh) * 2016-12-07 2017-05-31 江西三惠生物科技有限公司 用于直肠癌体外诊断试剂盒及其检测方法
WO2017184059A1 (fr) * 2016-04-20 2017-10-26 Hiloprobe Ab Gènes marqueurs pour la classification du cancer colorectal, procédé d'évaluation de métastase des ganglions lymphatiques pour le pronostic du cancer colorectal et kit associé
WO2018077225A1 (fr) * 2016-10-28 2018-05-03 Mao Ying Genetech Inc. Procédé d'identification du siège primaire d'un cancer métastatique et système associé
WO2019015549A1 (fr) * 2017-07-17 2019-01-24 Mao Ying Genetech Inc. Procédé et système d'identification de type cellulaire
JP2019516981A (ja) * 2016-05-10 2019-06-20 イミュノヴィア・アーベー 方法、アレイ、およびその使用
WO2019222618A1 (fr) 2018-05-17 2019-11-21 The Trustees Of Columbia University In The City Of New York Méthodes de traitement, prévention et détection du pronostic du cancer colorectal
WO2019226851A1 (fr) * 2018-05-24 2019-11-28 University Of Pittsburgh-Of The Commonwealth System Of Higher Education Prédiction de récidive d'un cancer à partir de données d'imagerie cellulaire et sous-cellulaire à multiples paramètres spatiaux.
WO2019229302A1 (fr) * 2018-05-29 2019-12-05 Turun Yliopisto L1td1 en tant que biomarqueur prédictif du cancer du côlon
CN111602054A (zh) * 2017-10-24 2020-08-28 下一代融合技术研究院 一种从血液诊断癌症的方法
WO2020206136A3 (fr) * 2019-04-02 2020-10-29 Board Of Regents, The University Of Texas System Ensembles de codes de classificateurs de sous-types moléculaires consensus de cancer colorectal et leurs méthodes d'utilisation
WO2021164492A1 (fr) * 2020-02-19 2021-08-26 伯克利南京医学研究有限责任公司 Application d'un groupe de gènes liés au pronostic du cancer du côlon
EP3762493A4 (fr) * 2018-03-08 2021-11-24 University of Notre Dame du Lac Systèmes et procédés d'évaluation d'un sous-type moléculaire de cancer colorectal et risque de récurrence et détermination et administration de protocoles de traitement basés sur ces derniers
CN113999908A (zh) * 2021-11-05 2022-02-01 中山大学附属第六医院 一种用于预测结直肠癌预后风险的试剂盒及其预测装置和预测模型的训练方法
US11320436B2 (en) 2020-07-16 2022-05-03 Immunovia Ab Methods, arrays and uses thereof
CN115141887A (zh) * 2022-08-18 2022-10-04 南方医科大学南方医院 基于分泌细胞富集特征的结肠癌预后及辅助化疗获益的评分模型、构建方法及应用
CN115206440A (zh) * 2022-07-15 2022-10-18 中国人民解放军总医院第五医学中心 一种基于kras突变结肠癌基因的预后模型及其应用
US11525832B2 (en) 2007-03-27 2022-12-13 Immunovia Ab Protein signature/markers for the detection of adenocarcinoma
CN116926193A (zh) * 2023-06-06 2023-10-24 北京肿瘤医院(北京大学肿瘤医院) 肿瘤免疫治疗预后评价制剂及靶向ano1的试剂在制备改善肿瘤预后药物中的应用
WO2024117974A1 (fr) * 2022-11-29 2024-06-06 Agency For Science, Technology And Research Procédé pour caractériser les cellules in situ

Citations (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20090298701A1 (en) * 2008-05-14 2009-12-03 Baker Joffre B Predictors of patient response to treatment with egf receptor inhibitors
US20100009905A1 (en) * 2006-03-24 2010-01-14 Macina Roberto A Compositions and Methods for Detection, Prognosis and Treatment of Colon Cancer
WO2011094483A2 (fr) * 2010-01-29 2011-08-04 H. Lee Moffitt Cancer Center And Research Institute, Inc. Signatures géniques immunitaires dans le cancer

Patent Citations (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20100009905A1 (en) * 2006-03-24 2010-01-14 Macina Roberto A Compositions and Methods for Detection, Prognosis and Treatment of Colon Cancer
US20090298701A1 (en) * 2008-05-14 2009-12-03 Baker Joffre B Predictors of patient response to treatment with egf receptor inhibitors
WO2011094483A2 (fr) * 2010-01-29 2011-08-04 H. Lee Moffitt Cancer Center And Research Institute, Inc. Signatures géniques immunitaires dans le cancer

Non-Patent Citations (1)

* Cited by examiner, † Cited by third party
Title
OH ET AL.: "Prognostic gene expression signature associated with two molecularly distinct subtypes of colorectal cancer", GUT, vol. 61, no. 9, September 2012 (2012-09-01), pages 1291 - 1298 *

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US10526659B2 (en) 2013-08-06 2020-01-07 Bgi Shenzhen Co., Limited Biomarkers for colorectal cancer
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