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WO2010060055A1 - Prédiction du risque et du succès d’un traitement du cancer - Google Patents

Prédiction du risque et du succès d’un traitement du cancer Download PDF

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Publication number
WO2010060055A1
WO2010060055A1 PCT/US2009/065570 US2009065570W WO2010060055A1 WO 2010060055 A1 WO2010060055 A1 WO 2010060055A1 US 2009065570 W US2009065570 W US 2009065570W WO 2010060055 A1 WO2010060055 A1 WO 2010060055A1
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Prior art keywords
recurrence
cancer
genes
expression
likelihood
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Anil Potti
Katherine Garman
Joseph R. Nevins
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Duke University
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Duke University
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    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N33/00—Investigating or analysing materials by specific methods not covered by groups G01N1/00 - G01N31/00
    • G01N33/48—Biological material, e.g. blood, urine; Haemocytometers
    • G01N33/50—Chemical analysis of biological material, e.g. blood, urine; Testing involving biospecific ligand binding methods; Immunological testing
    • G01N33/53—Immunoassay; Biospecific binding assay; Materials therefor
    • G01N33/574—Immunoassay; Biospecific binding assay; Materials therefor for cancer
    • G01N33/57407—Specifically defined cancers
    • G01N33/57419—Specifically defined cancers of colon
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N33/00—Investigating or analysing materials by specific methods not covered by groups G01N1/00 - G01N31/00
    • G01N33/48—Biological material, e.g. blood, urine; Haemocytometers
    • G01N33/50—Chemical analysis of biological material, e.g. blood, urine; Testing involving biospecific ligand binding methods; Immunological testing
    • G01N33/53—Immunoassay; Biospecific binding assay; Materials therefor
    • G01N33/574—Immunoassay; Biospecific binding assay; Materials therefor for cancer
    • G01N33/57484—Immunoassay; Biospecific binding assay; Materials therefor for cancer involving compounds serving as markers for tumor, cancer, neoplasia, e.g. cellular determinants, receptors, heat shock/stress proteins, A-protein, oligosaccharides, metabolites

Definitions

  • methods for predicting the likelihood of recurrence of a cancer are provided.
  • the methods are suitable for predicting recurrence of colon cancer.
  • the expression levels of a set of genes in a cancer cell from the subject are determined and compared to a reference.
  • the set of genes assessed includes at least five genes from the recurrence predictor set of Table 1.
  • the results are predictive of a likelihood of recurrence of the cancer
  • methods of developing a treatment plan for an individual with colon cancer are provided.
  • the predicted likelihood of recurrence of a cancer may be used to develop a treatment plan for the individual with the colon cancer.
  • the treatment plan may include administering an effective amount of a chemotherapeutic agent to the individual with the cancer which is predicted to recur.
  • kits including a gene chip for predicting likelihood of recurrence comprising nucleic acids capable of detecting at least five genes from Table 1 and instructions for predicting recurrence of colon cancer.
  • computer readable mediums including gene expression profiles of reference cancers having known recurrence phenotypes and corresponding likelihood of recurrence information.
  • the gene expression profiles include at least five genes from Table 1 are provided.
  • FIG. 1 is a depiction of the clinical application of the recurrence predictor set.
  • FIG. 2 A is a diagram showing the method used to develop the recurrence predictor set for colon cancer.
  • FIG. 2B (top) shows a heat-map of the samples used to develop the recurrence predictor set with blue and red representing extremes of expression.
  • FIG. 2B (middle) is a graph showing the recurrence score (prediction) of patients that remained disease free as compared to those who had recurrent disease.
  • FIG. 2(bottom) is a graph showing the ROC curve identifying the recurrence score of 0.76 as the optimal cut-point to be used in classifying samples in the validation set.
  • the area under the curve (AUC) is indicated to be 0.94, which confirms the robustness of the recurrence predictor set.
  • FIG. 3 is a graph showing the accuracy of the recurrence predictor set using the score of 0.76 as a cut-point, the accuracy was 90.3% in a leave -one -out cross validation analysis.
  • FIG. 4 is a set of graphs showing the accuracy and recurrence scores separated by the stage of the disease. The recurrence predictor predicted recurrence for both stage I and stage II colon cancer.
  • FIG. 5 is a set of graphs further showing validation of the recurrence predictor set.
  • FIG. 5 A shows an independent validation of the model comparing individual and mean recurrence scores for a distinct group of samples.
  • FIG. 5C is a graph showing the Kaplan-Meier survival analysis demonstrating the time to recurrence in the two groups. The top line represents those with a low recurrence score and the bottom line represents those with a high recurrence score.
  • FIG. 6 is a graph showing that RT-PCR analysis of the top ten differentially expressed genes demonstrated concordance for 9 of 10 genes with the microarray data. The data are presented as a comparison between the gene coefficients (specific to each gene in the Bayesian model) of the candidate genes and the log of the RQ values for the respective genes in the RT-PCR experiments.
  • FIG. 7 is a connectivity map computationally matching drugs likely to be effective based on a core gene expression profile and a list of drugs identified in the analysis based on the recurrence predictor set.
  • FIG. 8 is a graph showing the recurrence score of 14 colon cancer cell lines plotted against the metagene score generated using the recurrence predictor set.
  • FIG. 9 is a set of graphs showing the in vitro validation of candidate chemotherapeutic agent sensitivity.
  • FIG. 9A left panel shows the mutational events seen in the colon cancer cell lines sorted based on the recurrence probability generated based on the recurrence predictor set (blue: lowest risk; red: highest risk).
  • FIG. 9A right panel shows the results of chemotherapeutic treatments based on the recurrence predictor set (circles: low; triangles: high). The cell lines with high scores were more sensitive to treatments with celecoxib, LY294002 (PBkinase inhibitor) and retinol.
  • FIG. 9B left panel shows the change in recurrence score after exposure to chemotherapeutic agents.
  • FIG. 9B right panel shows that traditional chemotherapy agents (5 -FU and oxaliplatin) do not show significantly greater predilection for inhibiting growth in cell lines with a high recurrence score.
  • FIG. 9C is a histogram showing that all of the cell lines demonstrate a decrease in recurrence score post-treatment, indicating a reversal of the high-risk phenotype after exposure to LY294002 or celecoxib, with DLD-I showing the greatest sensitivity to reversal and COLO-23 showing the least effect. The effects of 5 -FU and oxaliplatin were inconsistent.
  • FIG. 9C is a histogram showing that all of the cell lines demonstrate a decrease in recurrence score post-treatment, indicating a reversal of the high-risk phenotype after exposure to LY294002 or celecoxib, with DLD-I showing the greatest sensitivity to reversal and COLO-23 showing the least effect.
  • COX2 and PBkinase inhibitors are valuable as initial agents for treating early stage colon cancers at high risk of recurrence.
  • the inventors have discovered a method for identifying those colon cancers, suitably early stage colon cancers, which are likely to recur using gene expression analysis as described below.
  • the analysis allows classification of the cancer into a low risk of recurrence versus a high risk of recurrence.
  • Treatment plans including administration of chemotherapeutic agents may be developed for those with a high risk of recurrence.
  • the inventors identified gene expression patterns within colonic tumors or cell lines that predict which cancers are likely to recur after surgical resection.
  • the recurrence predictor set developed from these studies is provided in Table 1. These predictions may be used to develop treatment plans for individual cancer patients. For example, those patients whose tumors are likely to recur may be treated with chemotherapeutic agents after surgery to prevent recurrence.
  • the invention also provides integrating gene expression profiles that predict recurrence with administration of chemotherapeutic agents as a strategy for developing personalized treatment plans for individual patients. Treatment plans may result in individuals having a complete response, a partial response or an incomplete response as defined below. Treatment plans may result in treatment of the cancer as described below.
  • a “complete response” (CR) to treatment of cancer is defined as a complete disappearance of all measurable and assessable disease. Complete responders show no signs of cancer recurrence after seven years. An individual who exhibits a complete response is known as a "complete responder.”
  • IR incomplete response
  • PR partial response
  • SD stable disease
  • PD progressive disease
  • An incomplete response includes individuals in which the colon cancer recurs.
  • Effective amount refers to an amount of a chemotherapeutic agent that is sufficient to treat the cancer.
  • An effective amount may exert a prophylactic or therapeutic effect in the subject, i.e., that amount which will stop or reduce the growth of the cancer or cause the cancer to become smaller in size compared to the cancer before treatment or compared to a suitable control or that stops recurrence of the cancer after treatment e.g., surgical resection, chemotherapy or radiation therapy.
  • an effective amount will be known or understood or can be determined by those skilled in the art.
  • the result of administering an effective amount of a chemotherapeutic agent may lead to effective treatment of the patient.
  • an effective amount is an amount sufficient to exert cytotoxic effects on cancerous cells.
  • Predicting and “prediction” as used herein includes, but is not limited to, generating a statistically based indication of whether a particular cancer such as a colon cancer is likely to recur after treatment, e.g., surgical resection and further whether a chemotherapeutic agent will be effective to prevent recurrence and/or treat the cancer. This does not mean that the event will happen with 100% certainty.
  • a “patient” refers to an “individual” who is under the care of a treating physician.
  • the present invention may be practiced using any suitable technique, including techniques known to those skilled in the art. Such techniques are available in the literature or in scientific treatises, such as Molecular Cloning: A Laboratory Manual, second edition (Sambrook et al., 1989) and Molecular Cloning: A Laboratory Manual, third edition (Sambrook and Russel, 2001), (jointly referred to herein as "Sambrook); Current Protocols in Molecular Biology (F. M.
  • Methods of staging cancers, predicting aggressiveness and predicting the likelihood of recurrence of a cancer are provided herein. Specifically, the methods rely on using a comparison of a gene expression profile of the cancer to a recurrence predictor set of gene expression profiles to predict the likelihood of recurrence of cancer after surgical resection.
  • the recurrence predictor set is, or may be derived from, a set of gene expression profiles obtained from control samples.
  • the controls samples may be cell lines, tumor samples, etc. with a known recurrence phenotype.
  • the comparison of the expression of a specific set of genes in the cancer to the same set of genes in samples known to recur or not to recur allows prediction of the likelihood of recurrence of the cancer.
  • the prediction may indicate that the cancer is unlikely to recur or it may predict that the cancer may recur.
  • the method may also provide an indication of how likely the cancer is to recur without chemotherapeutic intervention.
  • the methods described herein provide an indication of whether the cancer in the patient is likely to recur or not after primary treatment such as surgical resection, chemotherapy or radiation therapy.
  • the prediction may be more accurate than predictions made using population-based approaches from clinical studies which have been used to stage colon cancers.
  • the methods allow identification of cancers estimated to require further medical intervention so that the patients with these cancers are treated with chemotherapeutics after surgical resection.
  • the methods also may identify which chemotherapeutic agent may be most useful. This results in a more cost-effective, targeted therapy for the cancer patient and avoids side effects from chemotherapeutic agents for patients with cancers that are not likely to recur.
  • Table 1 also provides the relative "weights" of each of the individual genes that make up the recurrence predictor set.
  • the weights demonstrate that some genes are more strongly indicative of recurrence of a cancer. Predictions based on the complete set of genes are expected to provide the most accurate predictions regarding likelihood of the cancer recurring. Those of skill in the art will understand based on the weights of each gene in the recurrence predictor set that some genes are more predictive of outcome than others and thus that the entire recurrence predictor set need not be used to develop a useful prediction.
  • a treatment plan can be developed incorporating chemotherapeutic agents for individuals with cancers likely to recur.
  • the recurrence predictor may be used to predict responsiveness to chemotherapeutic agents and an effective amount of the chemotherapeutic agent(s) may be administered to the individual with the cancer.
  • the methods do not guarantee that the individuals will be responsive to the selected chemotherapeutic agent or that such treatment will stop recurrence, but the methods will increase the probability that the selected treatment will be effective to treat the cancer.
  • combination therapy is often suitable.
  • Treatment or treating a cancer includes, but is not limited to, reduction in cancer growth or tumor burden, enhancement of an anti-cancer immune response, induction of apoptosis of cancer cells, inhibition of angiogenesis, enhancement of cancer cell apoptosis, inhibition of metastases or inhibition of recurrence.
  • Administration of an effective amount of a chemotherapeutic agent to a subject may be carried out by any means known in the art including, but not limited to, intraperitoneal, intravenous, intramuscular, subcutaneous, transcutaneous, oral, nasopharyngeal or transmucosal absorption.
  • the specific amount or dosage administered in any given case will be adjusted in accordance with the specific cancer being treated, the condition, including the age and weight, of the subject, and other relevant medical factors known to those of skill in the art.
  • the sample of the cancer used to determine the expression levels of a set of genes may be directly from a tumor that was surgically removed.
  • the sample of the cancer could be from cells obtained in a biopsy or other tumor sample such as by aspiration or other methods available to those skilled in the art.
  • RNA e.g., total RNA
  • microarray Affymetrix Human Ul 33 A chip.
  • RT-PCR RT-PCR
  • microarrays include, but are not limited to, Stratagene (e.g., Universal Human Microarray), Genomic Health (e.g., Oncotype DX chip), Clontech (e.g., AtlasTM Glass Microarrays), and other types of Affymetrix microarrays.
  • the microarray may be made by a researcher or obtained from an educational institution.
  • customized microarrays which include the particular set of genes that are particularly suitable for prediction, can be used.
  • the expression profile of the set of genes may be obtained by any other means, including those known to those of skill in the art, e.g., Northern blots, real time rt-PCR, Western blots for the expressed proteins or protein assays.
  • the reference may be a set of cancer cells with known recurrence phenotype, a set of samples obtained from patients with known recurrence phenotype, or a numerical algorithm capable of predicting recurrence phenotype based on data obtained from such references.
  • Table 1 describes the recurrence predictor set. Development of the recurrence predictor set described herein is provided in the Examples. The use of the recurrence predictor set of genes in its entirety is contemplated; however, it is also possible to use subsets of the predictor set.
  • a subset of at least 2, 5, 10, 15, 20, 25, 30, 35 or 40 or more genes from Table 1 can be used for predictive purposes.
  • 10, 15, 20, 25, 30, 35, 40, 45, or 50 genes from Table 1 could be used as a recurrence predictor set. Indeed any number of the genes could be used in a predictor set.
  • Table 1 also provides the weights of each gene in the predictor set. The weight indicates how tightly correlated the expression of the gene is with recurrence or lack thereof by the cancer. Numbers farther from 0 are more predictive than those close to 0.
  • recurrence predictor set as detailed in the Examples to predict whether an individual or patient with cancer is likely to have recurrent cancer. If the individual has cancer likely to recur, then a treatment plan may be designed in which therapeutic agents will be administered in an effective amount after surgical resection of the cancer. If cancer is predicted to be unlikely to recur than chemotherapy may be unnecessary.
  • a treatment plan may be designed in which therapeutic agents will be administered in an effective amount after surgical resection of the cancer. If cancer is predicted to be unlikely to recur than chemotherapy may be unnecessary.
  • the expression profile of the genes in the cancer may be useful in determining which chemotherapeutic agents may be effective. As demonstrated in the Examples, the genes in the recurrence predictor set may be used to predict which chemotherapeutic agents are effective.
  • the gene expression profile of the cancer may be tested against additional predictor sets to allow development of a treatment plan with the best likelihood of treating the individual with the cancer.
  • an individual testing for a high likelihood of recurrence can be evaluated for responsiveness to one or more chemotherapeutic agents. See U.S. Patent Publication Nos. 20070172844, 20070294067, 20090186024 and 20090105167, each of which is incorporated herein by reference in its entirety.
  • the methods are performed outside of the human body.
  • Example cancers with a high likelihood of recurrence were further treated with a chemotherapeutic agent.
  • the genes in the recurrence predictor set were also correlated with susceptibility to certain chemotherapeutic agents, such that these agents may be more effective than others to treat these recurrent cancers.
  • the agents identified include a retinal analog, (Tetinon) a PI3K inhibitor (LY-294002), sulindae and a Cox inhibitor (celecoxib).
  • High recurrence risk cell lines treated with these agents demonstrated increased sensitivity to these agents as compared to chemotherapeutic agents currently used clinically for colon cancer, namely 5-FU and oxaliplatin.
  • the expression of genes within the recurrence predictor set in a cancer may be used to develop a treatment plan for an individual with cancer. Those skilled in the art can determine dosages and treatment regimes for individuals.
  • the teachings herein provide a gene expression model that predicts recurrence of colon cancer.
  • the Examples provide evidence that recurrent cancers may be treated by administrating chemotherapeutic agents after surgical resection.
  • the gene expression model was developed by using Bayesian binary regression analysis to identify genes highly correlated with recurrence.
  • the developed models were validated in a leave -one-out cross validation.
  • the methods described herein also include treating an individual afflicted with cancer.
  • This method involves administering an effective amount of a chemotherapeutic agent to those individuals predicted to have recurrent cancer.
  • a chemotherapeutic agent is administered in an effective amount after surgical resection.
  • the therapeutic agent is administered before or concurrently with surgical resection.
  • One aspect of the invention provides a method for predicting, estimating, aiding in the prediction of, or aiding in the estimation of, the likelihood of cancer recurrence in a subject afflicted with cancer.
  • the methods of the application are performed outside of the human body.
  • One method comprises (a) determining the expression level of multiple genes in a tumor sample from the subject; (b) defining the value of one or more metagenes from the expression levels of step (a), wherein each metagene is defined by extracting a single dominant value using singular value decomposition (SVD) from a recurrence predictor set; and (c) averaging the predictions of one or more statistical tree models applied to the values of the metagenes, wherein each model includes one or more nodes, each node representing a metagene, each node including a statistical predictive probability of tumor sensitivity to the therapeutic agent, thereby estimating the likelihood of cancer recurrence in a subject afflicted with cancer.
  • SSD singular value decomposition
  • Another method comprises (a) determining the expression level of multiple genes in a tumor sample from the subject; (b) defining the value of one or more metagenes from the expression levels of step (a), wherein each metagene is defined by extracting a single dominant value using singular value decomposition (SVD) from a recurrence predictor set; and (c) averaging the predictions of one or more binary regression models applied to the values of the metagenes, wherein each model includes a statistical predictive probability of tumor recurrence thereby estimating the likelihood of cancer recurrence in a subject afflicted with colon cancer.
  • SSD singular value decomposition
  • the methods predict the likelihood of cancer recurrence in a subject afflicted with cancer with at least 60% accuracy. In another embodiment, the methods predict the likelihood of cancer recurrence in a subject afflicted with cancer with at least 70% accuracy. In another embodiment, the methods predict the likelihood of cancer recurrence in a subject afflicted with cancer with at least 80% accuracy. In another embodiment, the methods predict the likelihood of cancer recurrence in a subject afflicted with cancer with at least 90% accuracy. In another embodiment, the methods predict the likelihood of cancer recurrence in a subject afflicted with cancer with at least 60%, 70%, 75%, 80%, 85%, 90% or 95% accuracy when tested against a validation sample.
  • the methods predict the efficacy of a therapeutic agent in treating a subject afflicted with cancer with at least 60%, 70%, 75%, 80%, 85%, 90% or 95% accuracy when tested against a set of training samples.
  • the methods predict the likelihood of cancer recurrence in a subject afflicted with cancer with at least 60%, 70%, 75%, 80%, 85%, 90% or 95% accuracy when tested on human primary tumors ex vivo or in vivo.
  • Accuracy is the ability of the methods to predict whether a cancer will recur or not.
  • the methods predict the likelihood of cancer recurrence in a subject with cancer with at least 60%, 70%, 75%, 80%, 85%, 90%, 95%, 97%, 98%, 99% or 100% sensitivity.
  • the methods predict the likelihood of cancer recurrence in a subject afflicted with cancer with at least 60%, 70%, 75%, 80%, 85%, 90%, 95%, 97%, 98%, 99% or 100% sensitivity when tested against a validation sample.
  • the methods predict the likelihood of cancer recurrence in a subject afflicted with cancer with at least 60%, 70%, 75%, 80%, 85%, 90%, 95%, 97%, 98%, 99% or 100% sensitivity when tested against a set of training samples.
  • the methods predict the likelihood of cancer recurrence in a subject afflicted with cancer with at least 60%, 70%, 75%, 80%, 85%, 90%, 95%, 97%, 98%, 99% or 100% sensitivity when tested on human primary tumors ex vivo or in vivo. Sensitivity measures the ability of the methods to predict all cancers that will recur.
  • the methods comprise determining the expression level of genes in a tumor sample from the subject. In one embodiment, the methods comprise the step of surgically removing a tumor sample from the subject, obtaining a tumor sample from the subject, or providing a tumor sample from the subject.
  • the sample may be derived from cells from the cancer, or cancerous cells.
  • the sample may contain nucleic acids from the cancer. Any method may be used to remove the sample from the patient and prepare nucleic acids or proteins for expression analysis. In one embodiment, at least 40%, 50%, 60%, 70%, 80% or 90% of the cells in the sample are cancer cells. In preferred embodiments, samples having greater than 50% cancer cell content are used.
  • the sample is a live tumor sample. In another embodiment, the sample is a frozen sample. In one embodiment, the sample is one that was frozen within less than 5, 4, 3, 2, 1, 0.75, 0.5, 0.25, 0.1, or 0.05 hours after extraction from the patient. Frozen samples include those stored in liquid nitrogen or at a temperature of about -80 0 C or below.
  • the expression of the genes may be determined using any method known in the art for assaying gene expression. Gene expression may be determined by measuring mRNA or protein levels for the genes. In one embodiment, an mRNA transcript of a gene may be detected for determining the expression level of the gene. Based on the sequence information provided by the GenBank TM database entries, the genes can be detected and expression levels measured using techniques well known to one of ordinary skill in the art, including but not limited to rtPCR, Northern blot analysis and microarray analysis. For example, sequences within the sequence database entries corresponding to polynucleotides of the genes can be used to construct probes for detecting mRNAs by, e.g., Northern blot hybridization analyses.
  • the hybridization of the probe to a gene transcript in a subject biological sample can be also carried out on a DNA array.
  • the use of an array is suitable for detecting the expression level of a plurality of the genes.
  • the sequences can be used to construct primers for specifically amplifying the polynucleotides in, e.g., amplification-based detection methods such as reverse-transcription based polymerase chain reaction (RT-PCR).
  • RT-PCR reverse-transcription based polymerase chain reaction
  • mRNA levels can be assayed by quantitative RT-PCR.
  • the expression level of the genes can be analyzed based on the biological activity or quantity of proteins encoded by the genes. Methods for determining the quantity of the protein include immunoassay methods such as Western blot analysis.
  • the targets for Affymetrix DNA microarray analysis were prepared according to the manufacturer's instructions.
  • determining the expression level (or obtaining a first gene expression profile) of multiple genes in a tumor sample from the subject comprises extracting nucleic acids from the sample from the subject.
  • the nucleic acid sample is an mRNA sample.
  • the expression level of the nucleic acid is determined by hybridizing the nucleic acid, or amplification products thereof, to a DNA microarray. Amplification products may be generated, for example, with reverse transcription, optionally followed by PCR amplification of the products.
  • the predictive methods of the invention comprise determining the expression level of all the genes in the cluster that define the recurrence metagene. In one embodiment, the predictive methods of the invention comprise determining the expression level of at least 50%, 60%, 70%, 80%, 90%, 95%, 98%, 99% of the genes in each of the clusters that defines the colon cancer recurrence metagene.
  • a metagene is a cluster or set of genes which may be used to predict the likelihood of recurrence of a cancer.
  • At least 50%, 60%, 70%, 80%, 90%, 95%, 98%, 99% of the genes whose expression levels are used in order to predict recurrence are genes listed in Table 1.
  • At least 50%, 60%, 70%, 80%, 90%, 95%, 98%, 99% of the genes listed in Table 1 are used to predict recurrence of a cancer.
  • Table 1 shows the genes in the cluster that are used to define metagenes and indicate the likelihood of recurrence.
  • the predictive methods of the invention comprise defining the value of one or more metagenes from the expression levels of the genes.
  • a metagene value is defined by extracting a single dominant value from a cluster of genes associated with the likelihood of recurrence of the cancer.
  • the dominant single value is obtained using single value decomposition (SVD).
  • the cluster of genes of each metagene or at least of one metagene comprises at least 3, 4, 5, 6, 7, 8, 9, 10, 12, 15, 18, 20 or 25 genes.
  • the predictive methods of the invention comprise defining the value of at least one metagene wherein the genes in the cluster of genes from which the metagene is defined, share at least 50%, 60%, 70%, 80%, 90%, 95% or 98% of genes in common to the genes in Table 1. In one embodiment, the predictive methods of the invention comprise defining the value of at least two metagenes, wherein the genes in the cluster of genes from which each metagene is defined share at least 50%, 60%, 70%, 80%, 90%, 95% or 98% of genes in common to the genes in Table 1.
  • the predictive methods of the invention comprise defining the value of a metagene from a cluster of genes, wherein at least 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19 or 20 genes in the cluster are selected from the genes listed in Table 1.
  • the clusters of genes that define each metagene were identified using supervised classification methods of analysis as previously described. See, for example, West, M. et al. Proc Natl Acad Sci USA 98, 11462-11467 (2001).
  • the dominant principal components from such a set of genes defines a relevant phenotype-related metagene, and regression models, such as binary regression models, were used to assign the relative probability of recurrence of the cancer.
  • the methods comprise averaging the predictions of one or more statistical tree models applied to the metagene values, wherein each model includes one or more nodes, each node representing a metagene, each node including a statistical predictive probability of recurrence of the cancer.
  • the statistical tree models may be generated using the methods described herein for the generation of tree models. General methods of generating tree models may also be found in the art (See for example Pitman et al, Biostatistics 2004;5:587-601; Denison et al. Biometrika 1999;85:363-77; Nevins et al. Hum MoI Genet 2003;12:R153-7; Huang et al.
  • the methods comprise deriving a prediction from a single statistical tree model, wherein the model includes one or more nodes, each node representing a metagene, each node including a statistical predictive probability of recurrence of the cancer.
  • the tree may comprise at least 2, 3, 4, or 5 nodes.
  • the methods comprise averaging the predictions of one or more statistical tree models applied to the metagene values, wherein each model includes one or more nodes, each node representing a metagene, each node including a statistical predictive probability of recurrence of the cancer. Accordingly, the invention provides methods that use mixed trees, where a tree may contain at least two nodes, where each node represents a metagene representative of the likelihood of cancer recurrence.
  • the statistical predictive probability was derived from a Bayesian analysis.
  • the Bayesian analysis included a sequence of Bayes factor based tests of association to rank and select predictors that define a node binary split, the binary split including a predictor/threshold pair.
  • Bayesian analysis is an approach to statistical analysis that is based on the Bayes law, which states that the posterior probability of a parameter p is proportional to the prior probability of parameter p multiplied by the likelihood of p derived from the data collected.
  • This methodology represents an alternative to the traditional (or frequentist probability) approach: whereas the latter attempts to establish confidence intervals around parameters, and/or falsify a-priori null-hypotheses, the Bayesian approach attempts to keep track of how a priori expectations about some phenomenon of interest can be refined, and how observed data can be integrated with such a priori beliefs, to arrive at updated posterior expectations about the phenomenon.
  • Bayesian analysis has been applied to numerous statistical models to predict outcomes of events based on available data. These include standard regression models, e.g. binary regression models, as well as to more complex models that are applicable to multi-variate and essentially non-linear data. Another such model is commonly known as the tree model which is essentially based on a decision tree.
  • Decision trees can be used in clarification, prediction and regression.
  • a decision tree model is built starting with a root mode, and training data partitioned to what are essentially the "children" nodes using a splitting rule. For instance, for clarification, training data contains sample vectors that have one or more measurement variables and one variable that determines that class of the sample.
  • Various splitting rules may be used.
  • a statistical predictive tree model to which Bayesian analysis is applied may consistently deliver accurate results with high predictive capabilities. Other statistical models known to those of skill in the art may be used.
  • Gene expression signatures that reflect the activity of a given pathway may be identified using supervised classification method of analysis previously described (e.g., West, M. et al. Proc Natl Acad Sci USA 98, 11462-11467, 2001).
  • the analysis selects a set of genes whose expression levels are most highly correlated with the classification of tumor samples into likely to recur versus unlikely to recur.
  • the dominant principal components from such a set of genes then defines a relevant phenotype-related metagene, and regression models assign the relative probability of recurrence of the cancer.
  • each statistical tree model generated by the methods described herein comprises 2, 3, 4, 5, 6 or more nodes.
  • the resulting model predicts cancer recurrence with at least 60%, 70%, 80%, 85%, or 90% or higher accuracy.
  • the model predicts cancer recurrence with greater accuracy than clinical variables.
  • the clinical variables are selected from age of the subject, gender of the subject, tumor size of the sample, stage of cancer disease and histological subtype of the sample.
  • the cluster of genes that define each metagene comprise at least 3, 4, 5, 6, 7, 8, 9, 10, 12 or 15 genes.
  • the correlation-based clustering is Markov chain correlation-based clustering or K-means clustering.
  • arrays can contain the profiles of 5, 10, 15, 20, 25, 30, 40, 50, 75, 100, 150, 200 or more genes as disclosed in Table 1. Accordingly, arrays for detection of recurrence can be customized for diagnosis or treatment of colon cancer.
  • the array can be packaged as part of kit comprising the customized array itself and a set of instructions for how to use the array to determine an individual's likelihood of recurrence.
  • reagents and kits for practicing one or more of the above described methods.
  • the subject reagents and kits thereof may vary greatly.
  • Reagents of interest include reagents specifically designed for use in production of the above described metagene values.
  • One type of such reagent is an array probe of nucleic acids, such as a DNA chip, in which the genes defining the metagenes in the recurrence predictive tree models are represented.
  • array probe of nucleic acids such as a DNA chip
  • a variety of different array formats are known in the art, with a wide variety of different probe structures, substrate compositions and attachment technologies.
  • Representative array structures of interest include those described in U.S. Pat. Nos.
  • DNA chip is conveniently used to compare the expression levels of a number of genes at the same time.
  • DNA chip-based expression profiling can be carried out, for example, by the method as disclosed in "Microarray Biochip Technology" (Mark Schena, Eaton Publishing, 2000).
  • a DNA chip comprises immobilized high-density probes to detect a number of genes.
  • the expression levels of many genes can be estimated at the same time by a single-round analysis.
  • the expression profile of a specimen can be determined with a DNA chip.
  • a DNA chip may comprise probes, which have been spotted thereon, to detect the expression level of the metagene-defining genes of the present invention, i.e. the genes described in Table 1.
  • a probe may be designed for each marker gene selected, and spotted on a DNA chip.
  • Such a probe may be, for example, an oligonucleotide comprising 5-50 nucleotide residues. Methods for synthesizing such oligonucleotides on DNA chips are known to those skilled in the art. Longer DNAs can be synthesized by PCR or chemically.
  • Methods for spotting long DNA, which is synthesized by PCR or the like, onto a glass slide are also known to those skilled in the art.
  • a DNA chip that is obtained by the methods described above can be used for estimating the recurrence of a cancer in a subject afflicted with cancer according to the present invention.
  • DNA microarray and methods of analyzing data from microarrays are well- described in the art, including in DNA Microarrays: A Molecular Cloning Manual. Ed. by Bowtel and Sambrook (Cold Spring Harbor Laboratory Press, 2002); Microarraysfor an Integrative Genomics by Kohana (MIT Press, 2002); A Biologist's Guide to Analysis of DNA Micraarray Data, by Knudsen (Wiley, John & Sons, Incorporated, 2002); DNA Microarrays: A Practical Approach, Vol. 205 by Schema (Oxford University Press, 1999); and Methods of Microarray Data Analysis II, ed. by Lin et al. (Kluwer Academic Publishers, 2002) all of which are incorporated herein by reference.
  • One aspect of the invention provides a kit comprising: (a) any of the gene chips described herein; and (b) one of the computer-readable mediums described herein.
  • the arrays include probes for at least 2, 3, 4, 5, 6, 7, 8, 9, 10, 15, 20, 25, 30, 40, or 50 of the genes listed in Table 1.
  • the number of genes that are from Table 1 that are represented on the array is at least 5, at least 10, at least 25 or at least 50, including all of the genes listed in the table.
  • the subject arrays include probes for additional genes not listed in the table, in certain embodiments the number % of additional genes that are represented does not exceed about 50%, 40%, 30%, 20%, 15%, 10%, 8%, 6%, 5%, 4%, 3%, 2% or 1%.
  • a great majority of genes in the collection are genes that define the metagenes of the invention, whereby great majority is meant at least about 75%, usually at least about 80% and sometimes at least about 85, 90, 95% or higher, including embodiments where 100% of the genes in the collection are metagene-defming genes.
  • the arrays for use in the invention may include a majority of probes that are not listed in Table 1.
  • kits of the subject invention may include the above described arrays or gene chips.
  • the kits may further include one or more additional reagents employed in the various methods, such as primers for generating target nucleic acids, dNTPs and/or rNTPs, which may be either premixed or separate, one or more uniquely labeled dNTPs and/or rNTPs, such as biotinylated or Cy3 or Cy5 tagged dNTPs, gold or silver particles with different scattering spectra, or other post synthesis labeling reagent, such as chemically active derivatives of fluorescent dyes, enzymes, such as reverse transcriptases, DNA polymerases, RNA polymerases, and the like, various buffer mediums, e.g.
  • hybridization and washing buffers prefabricated probe arrays, labeled probe purification reagents and components, like spin columns, etc.
  • signal generation and detection reagents e,g. streptavidin-alkaline phosphatase conjugate, chemifluorescent or chemiluminescent substrate, and the like.
  • the subject kits further include instructions for practicing the subject methods. These instructions may be present in the subject kits in a variety of forms, one or more of which may be present in the kit.
  • One form in which these instructions may be present is as printed information on a suitable medium or substrate, e.g., a piece or pieces of paper on which the information is printed, in the packaging of the kit, in a package insert, etc.
  • Yet another means would be a computer readable medium, e.g., diskette, CD, etc., on which the information has been recorded.
  • Yet another means that may be present is a website address which may be used via the internet to access the information at a remote site. Any convenient means of conveying instructions may be present in the kits.
  • kits also include packaging material such as, but not limited to, ice, dry ice, styrofoam, foam, plastic, cellophane, shrink wrap, bubble wrap, paper, cardboard, starch peanuts, twist ties, metal clips, metal cans, drierite, glass, and rubber.
  • packaging material such as, but not limited to, ice, dry ice, styrofoam, foam, plastic, cellophane, shrink wrap, bubble wrap, paper, cardboard, starch peanuts, twist ties, metal clips, metal cans, drierite, glass, and rubber.
  • the invention also contemplates computer readable media that comprises reference gene expression profiles.
  • Such media can contain all or part of the gene expression profiles of the genes listed in Table 1 that comprise the recurrence predictor set.
  • the media can be a list of the genes or contain the raw data for running a user's own statistical calculation, such as the methods disclosed herein.
  • Another aspect of the invention provides a program product (i.e., software product) for use in a computer device that executes program instructions recorded in a computer- readable medium to perform one or more steps of the methods described herein, such as for estimating the likelihood of cancer recurrence in a subject afflicted with cancer.
  • a program product i.e., software product
  • One aspect of the invention provides a computer readable medium having computer readable program codes embodied therein, the computer readable medium program codes performing one or more of the following functions: defining the value of one or more metagenes from the expression levels of genes in known recurrent of non-recurrent cancers defining a metagene value by extracting a single dominant value using singular value decomposition (SVD) from a cluster of genes associated with tumor recurrence; averaging the predictions of one or more statistical tree models applied to the values of the metagenes; or averaging the predictions of one or more binary regression models applied to the values of the metagenes, wherein each model includes a statistical predictive probability of tumor recurrence.
  • SSD singular value decomposition
  • kits comprising the program product or the computer readable medium, optionally with a computer system.
  • a system comprising: a computer; a computer readable medium, operatively coupled to the computer, the computer readable medium program codes performing one or more of the following functions: defining the value of one or more metagenes from the expression levels genes; defining a metagene value by extracting a single dominant value using singular value decomposition (SVD) from a cluster of genes associated with tumor recurrence; averaging the predictions of one or more statistical tree models applied to the values of the metagenes; or averaging the predictions of one or more binary regression models applied to the values of the metagenes, wherein each model includes a statistical predictive probability of tumor recurrence.
  • SSD singular value decomposition
  • the program product comprises: a recordable medium; and a plurality of computer-readable instructions executable by the computer device to analyze data from the array hybridization steps, to transmit array hybridization from one location to another, or to evaluate genome-wide location data between two or more genomes.
  • Computer readable media include, but are not limited to, CD-ROM disks (CD-R, CD-RW), DVD-RAM disks, DVD-RW disks, floppy disks and magnetic tape.
  • kits comprising the program products described herein.
  • kits may also optionally contain paper and/or computer-readable format instructions and/or information, such as, but not limited to, information on DNA microarrays, on tutorials, on experimental procedures, on reagents, on related products, on available experimental data, on using kits, on chemotherapeutic agents including their toxicity, and on other information.
  • the kits optionally also contain in paper and/or computer-readable format information on minimum hardware requirements and instructions for running and/or installing the software.
  • the kits optionally also include, in a paper and/or computer readable format, information on the manufacturers, warranty information, availability of additional software, technical services information, and purchasing information.
  • kits optionally include a video or other viewable medium or a link to a viewable format on the internet or a network that depicts the use of the software, and/or use of the kits.
  • the kits also include packaging material such as, but not limited to, styrofoam, foam, plastic, cellophane, shrink wrap, bubble wrap, paper, cardboard, starch peanuts, twist ties, metal clips, metal cans, drierite, glass, and rubber.
  • the analysis of data, as well as the transmission of data steps, can be implemented by the use of one or more computer systems. Computer systems are readily available.
  • the processing that provides the displaying and analysis of image data for example can be performed on multiple computers or can be performed by a single, integrated computer or any variation thereof.
  • the components contained in the computer system are those typically found in general purpose computer systems used as servers, workstations, personal computers, network terminals, and the like. In fact, these components are intended to represent a broad category of such computer components that are well known in the art.
  • a general purpose computer system for performing the functions of the software according to an illustrative embodiment of the invention includes a central processing unit (CPU), a memory, and an interconnect bus.
  • the CPU may include a single microprocessor or a plurality of microprocessors for configuring computer system as a multi-processor system.
  • the memory illustratively includes a main memory and a read only memory.
  • the computer also includes the mass storage device 1508 having, for example, various disk drives, tape drives, etc.
  • the main memory also includes dynamic random access memory (DRAM) and high-speed cache memory. In operation, the main memory stores at least portions of instructions and data for execution by the CPU.
  • DRAM dynamic random access memory
  • the mass storage may include one or more magnetic disk or tape drives or optical disk drives, for storing data and instructions for use by the CPU.
  • At least one component of the mass storage system preferably in the form of a disk drive or tape drive, stores one or more databases, such as databases containing of transcriptional start sites, genomic sequence, promoter regions, or other information.
  • the mass storage system may also include one or more drives for various portable media, such as a floppy disk, a compact disc read only memory (CD-ROM), or an integrated circuit non-volatile memory adapter (i.e., PC-MCIA adapter) to input and output data and code to and from the computer system.
  • portable media such as a floppy disk, a compact disc read only memory (CD-ROM), or an integrated circuit non-volatile memory adapter (i.e., PC-MCIA adapter) to input and output data and code to and from the computer system.
  • CD-ROM compact disc read only memory
  • PC-MCIA adapter integrated circuit non-volatile memory adapter
  • the computer system may also include one or more input/output interfaces for communications, shown by way of example, as interface for data communications via a network.
  • the data interface may be a modem, an Ethernet card or any other suitable data communications device.
  • the data interface may provide a relatively high-speed link to a network, such as an intranet, internet, or the
  • the communication link to the network may be, for example, optical, wired, or wireless (e.g., via satellite or cellular network).
  • the computer system may include a mainframe or other type of host computer system capable of Web-based communications via the network.
  • the computer system also includes suitable input/output ports or use the interconnect bus for interconnection with a local display and keyboard or the like serving as a local user interface for programming and/or data retrieval purposes.
  • server operations personnel may interact with the system for controlling and/or programming the system from remote terminal devices via the network.
  • probes (202619_s_at and 202620_s_at) that represent splice variants for PLOD2.
  • RT-PCR validation of the top 10 differentially expressed candidate genes demonstrated that 9 of 10 genes (all except HNT) identified to be most differentially expressed in our genomic model could also be validated by using RT-PCR (FIG. 6).
  • FOG. 6 RT-PCR
  • Recurrence Signature Identifies Therapeutic Opportunities.
  • Using gene expression methodologies to understand the molecular mechanisms involved in cancer progression may be helpful beyond prognosis because this knowledge may lead to the study of drugs that target relevant, deregulated pathways in an individual patient. More importantly, we may be able to identify specific, effective agents from a repertoire of currently existing drugs.
  • Connectivity Map a project developed at the Broad Institute, to assemble a reference collection of gene-expression profiles from cells that have been treated with a variety of drugs. This effort established links between gene expression profiles and drugs (20).
  • Connectivity Map a project developed at the Broad Institute, to assemble a reference collection of gene-expression profiles from cells that have been treated with a variety of drugs. This effort established links between gene expression profiles and drugs (20).
  • 9A shows the stratification of the cell lines by recurrence score and their respective mutational events [KRAS, p53, BRAF, PI3K, CTNNB2 (beta-catenin), APC and CDKN2A], demonstrating that the risk categories determined by our model of recurrence do not segregate based on any one mutation and simulates the genetic heterogeneity seen in clinical practice.
  • DWD Distance-Weighted Discrimination
  • a probit function enabled us to generate a probability of recurrence for each sample, referred to as the "Recurrence Score.”
  • An optimal threshold recurrence score value of 0.76 was chosen based on a receiver operated characteristic (ROC) analysis, and was used as the predefined 'cut-point', to dichotomize samples into low risk (Recurrence Score ⁇ 0.76) and high risk (Recurrence Score > 0.76).
  • RNA extracted from each of the seven high risk and seven low risk cell lines are reverse transcribed into cDNA by using random primers.
  • Taqman gene expression assays including 18s used as the manufacturing control
  • gene targets were analyzed by assessing Ct values after normalization to GAPDH to compare quantitative expression values. Further details on patient selection, RNA extraction, preprocessing of gene expression data, and statistical analysis are available as SI Methods .
  • This training set was further filtered based on P values obtained from multiple t tests, and 91 specific genes with significant survival effects (P ⁇ 0.001) were selected for Bayesian binary regression analyses.
  • Bayesian fitting of binary probit regression models to the training data then permits an assessment of the relevance of the metagene signatures in within-sample classification, and estimation and uncertainty assessments for the binary regression weights mapping metagenes to probabilities.
  • a metagene predictor consisting of 50 genes was developed using the aforementioned methodologies.
  • GATHER http://gather.genome.duke-.edu/
  • GATHER is a tool that integrates various forms of available metadata to elucidate the biological context within molecular signatures produced from high-throughput data.
  • GATHER also has the capacity to discover novel functions of gene groups by integrating annotations from evolutionary homologs and other genes related through protein interactions or literature networks.
  • GATHER further annotates the characteristics of the genes with respect to data sets from multiple systems, helping synthesize evidence to develop or reinforce hypotheses.
  • the accuracy with which GATHER can infer novel functions of signatures is interpreted through a Bayesian statistical model.
  • an optimal threshold recurrence score value of 0.76 was chosen based on a receiver operated characteristic (ROC) analysis, and was used as the predefined 'cut-point', to dichotomize samples into low risk (Recurrence Score ⁇ 0.76) and high risk (Recurrence Score ⁇ 0.76).
  • ROC receiver operated characteristic
  • a binary probit regression model is estimated using Bayesian methods.
  • the initial training set and the validation sets (obtained from refs. 1 and 2) were normalized using the DWD method.
  • Standard Kaplan-Meier mortality curves and their statistical significance were generated from the predictive probability values of patients using Graph- Pad software.
  • the survival curves were compared using the log-rank test. This test generates a two-tailed P value testing the null hypothesis that the survival curves are identical in the overall populations. Therefore, the null hypothesis is that the populations have no differences in survival.
  • Each probe set ID in given Affymetrix gene chips were first mapped to the corresponding LocusID by parsing local copies of LocusLink and UniGene databases to identify any inherent relationship between the GenBank accession number associated with each probe set sequence and its corresponding LocusID. This was followed by matching probe sets from different gene chips that share the same LocusID.
  • ComBat Method When combining data sets from different platforms and different experiments, non-biological experimental variation or "batch effects" are most commonly faced by researchers. It is inappropriate to combine data sets without adjusting for batch effects. To reduce the systematic differences from different data sets and integrate gene expression from all data sets, ComBat method (http://statistics.byu.edu/johnson/ ComBat/) was applied.
  • ComBat method applies either parametric or non-parametric empirical Bayes framework for adjusting data for batch effects that is robust to outliers in a given data set.
  • the location (mean) and scale (variance) model parameters are specifically estimated by pooling information across genes in each batch to shrink the batch effect parameter estimated toward the overall mean of the batch effect estimates. This method was applied to data sets consisting of normal colon samples and tumor samples separately.
  • Colon Cancer Cell Lines Fourteen colon cancer cell lines (COLO- 320 HSR, DLD-I, HCTl 15, HCTl 16, HT29, LS174T, LS180, RKO, SW48, SW403, SWl 116, SW1417, SWl 463, and WiDr) that were commercially available were grown as recommended by the supplier (American Type Culture Collection).
  • celecoxib was obtained from LKT Laboratories Inc.
  • the LY294002 was obtained from Cayman Chemical
  • the retinol, and sulindac were obtained from Sigma- Aldrich.
  • 5 -FU and oxaliplatin were obtained from the Duke University pharmacy.
  • To classify cell lines we measured genome-wide expression in the 14 colon cancer cell lines using the Affymetrix Ul 33 A Plus 2.0 GeneChip. Total RNA was extracted from the cells with RNeasy kits (Qiagen).
  • RNA quality was assessed with the use of a bioanalyzer (Agilent 2100 model).
  • Hybridization targets were prepared from the total RNA according to standard Affymetrix protocols.
  • 'Recurrence Scores' were generated for the cell lines and the predefined (from the training set) threshold value was then used to dichotomize the cell lines into low and high risk phenotypes.
  • In vitro cell proliferation assays were used to demonstrate the mean percent sensitivity when the highest concentration of drug (celecoxib, retinol, LY294002, sulindac, 5 -FU, and oxaliplatin) was used in each cell line as the basis for comparisons of sensitivity, between high and low recurrence risk groups, for each of the drugs tested.
  • gene expression profiles were re-assessed to determine if the high-risk phenotype had been reversed.
  • the cell proliferation assays for the 14 colon cancer cell lines profiled by gene array analyses included growth inhibition measurements using standard colorimetric assays. Cells were plated in 96-well assay plates at a density of 5,000 cells per well. After incubating for 24 h at 37°C, drugs were added to each well at specific concentrations. Cells were grown in the presence of drugs for an additional 96 h.
  • Celecoxib was used at concentrations of 0.1, 5, 25, 50, and 100; LY294002, a PBKinase inhibitor was used at concentrations of 0.1, 1, 10, 20 and 200; retinol was used at concentrations of 0, 0.1, 1, 5, 10, and 50; sulindac was used at concentrations of 0, 0.1, 1, 10, 100, and 1,000; 5-FU and oxaliplatin were used at concentrations of 0, 0.1, 1, 5, 10, and 20. All concentrations were micromolar. Sensitivity to celecoxib, LY294002, retinol, sulindac, 5-FU, and oxaliplatin was determined by quantifying the percent reduction in growth at 96 h using the standard MTT Cell Proliferation Kit from Roche Applied Science. A Perkin-Elmer Victor 3 Multilabel Plate Reader was used to determine UV absorbance. All experiments were repeated at least three times.
  • RNA was extracted using the Qiashredder and Qiagen Rneasy Mini kits. Quality of the RNA was checked by an Agilent 2100 Bioanalyzer. The targets for Affymetrix DNA microarray analysis were prepared according to the manufacturer's instructions. Biotin-labeled cRNA, produced by in vitro transcription, was fragmented and hybridized to the Affymetrix HG-Ul 33 A Plus 2.0 GeneChip arrays at 45°C for 16 h and then washed and stained using the GeneChip Fluidics.
  • the arrays were scanned by a GeneArray Scanner and patterns of hybridization were detected as light emitted from the fluorescent reporter groups incorporated into the target and hybridized to oligonucleotide probes. All analyses were performed in a MIAME (minimal information about a microarray experiment)- compliant fashion, as defined in the guidelines established by MGED. RT-PCR Analysis. The top 10 differentially expressed genes from among the 50-gene model were chosen for further validation using real-time PCR. Briefly, the methods involved Taqman custom arrays, a 384-well micro fluidic card that enables 384 real-time PCR reactions to be performed simultaneously without the use of liquid-handling robots or multichannel pipettes. The array is designed for a two-step RT-PCR.
  • RNA extracted from each of the seven high risk and seven low risk cell lines are reverse transcribed into cDNA using random primers from the High Capacity cDNA Reverse Transcription Kit.
  • One thousand ng of total RNA were transcribed in a 25 ⁇ l reaction.
  • 25 ⁇ l of RNase/DNase-free water was added.
  • PCR Taqman gene expression assays (including 18 s used as the manufacturing control) were preloaded into each of the wells of the array. Sample-specific PCR mix was generated by adding 50 ⁇ l of the Taqman Universal Master Mix to the 50 ⁇ l of cDNA plus water reactions.
  • WoodLD et al. (2007) The genomic landscapes ofhumanbreast and colorectal cancers. Science 318:1108-1113.

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Abstract

La présente invention concerne des procédés pour prédire la probabilité de récurrence d’un cancer en utilisant des profils d’expression génique. La présente invention concerne en outre des procédés pour développer des plans de traitement pour des individus ayant un cancer. L’invention concerne également des kits comprenant des puces à ADN et des instructions pour prédire la récurrence d’un cancer et un support lisible sur ordinateur comprenant des informations de récurrence.
PCT/US2009/065570 2008-11-21 2009-11-23 Prédiction du risque et du succès d’un traitement du cancer Ceased WO2010060055A1 (fr)

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US10072293B2 (en) 2011-03-31 2018-09-11 The Procter And Gamble Company Systems, models and methods for identifying and evaluating skin-active agents effective for treating dandruff/seborrheic dermatitis
US10196691B2 (en) 2011-01-25 2019-02-05 Almac Diagnostics Limited Colon cancer gene expression signatures and methods of use
US10214777B2 (en) 2010-09-15 2019-02-26 Almac Diagnostics Limited Molecular diagnostic test for cancer
CN112365948A (zh) * 2020-10-27 2021-02-12 沈阳东软智能医疗科技研究院有限公司 癌症分期预测系统
CN112599190A (zh) * 2020-12-17 2021-04-02 重庆大学 一种基于混合分类器来识别耳聋相关基因的方法

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US20070172844A1 (en) * 2005-09-28 2007-07-26 University Of South Florida Individualized cancer treatments
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Publication number Priority date Publication date Assignee Title
US10214777B2 (en) 2010-09-15 2019-02-26 Almac Diagnostics Limited Molecular diagnostic test for cancer
US10378066B2 (en) 2010-09-15 2019-08-13 Almac Diagnostic Services Limited Molecular diagnostic test for cancer
US10196691B2 (en) 2011-01-25 2019-02-05 Almac Diagnostics Limited Colon cancer gene expression signatures and methods of use
US10072293B2 (en) 2011-03-31 2018-09-11 The Procter And Gamble Company Systems, models and methods for identifying and evaluating skin-active agents effective for treating dandruff/seborrheic dermatitis
US9920357B2 (en) 2012-06-06 2018-03-20 The Procter & Gamble Company Systems and methods for identifying cosmetic agents for hair/scalp care compositions
CN112365948A (zh) * 2020-10-27 2021-02-12 沈阳东软智能医疗科技研究院有限公司 癌症分期预测系统
CN112365948B (zh) * 2020-10-27 2023-07-18 沈阳东软智能医疗科技研究院有限公司 癌症分期预测系统
CN112599190A (zh) * 2020-12-17 2021-04-02 重庆大学 一种基于混合分类器来识别耳聋相关基因的方法
CN112599190B (zh) * 2020-12-17 2024-04-05 重庆大学 一种基于混合分类器来识别耳聋相关基因的方法

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