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  Methods for predicting cancer outcome and gene signatures for use thereinUSPTO Application #: 20060195266Title: Methods for predicting cancer outcome and gene signatures for use therein Abstract: The present invention pertains to specific gene signatures for cancer that are used to predict survival and novel processes for identifying such gene signatures. In one embodiment, gene signatures for human colorectal cancer are identified and outcomes are linked to the specific gene signatures using significance analysis of microarrays (SAM) and support vector machines (SVM) to provide a prognosis/survival classifier. (end of abstract) Agent: Saliwanchik Lloyd & Saliwanchik A Professional Association - Gainesville, FL, US Inventor: Timothy J. Yeatman USPTO Applicaton #: 20060195266 - Class: 702019000 (USPTO) Related Patent Categories: Data Processing: Measuring, Calibrating, Or Testing, Measurement System In A Specific Environment, Biological Or Biochemical The Patent Description & Claims data below is from USPTO Patent Application 20060195266. Brief Patent Description - Full Patent Description - Patent Application Claims
CROSS-REFERENCE TO A RELATED APPLICATION [0001] This application claims the benefit of U.S. Provisional Application Ser. No. 60/547,871, filed Feb. 25, 2004, which is hereby incorporated by reference in its entirety. BACKGROUND OF THE INVENTION [0002] In the last decade, scientists have labored to complete a high-quality, comprehensive sequence of the human genome. With its recent completion, a large number of genomic data sets have been made available in public databases. The available data, however, does not provide explanations regarding which aspects of human biology affect which genes. Researchers are just beginning to explore genomic function. [0003] Several technological advances have made it possible to accurately measure cellular constituents and therefore derive profiles. For example, new techniques provide the ability to monitor the expression level of a large number of transcripts at any one time (see, for example, Schena et al., "Quantitative monitoring of gene expression patterns with a complementary DNA micro-array," Science, 270:467-470 (1995); Lockhart et al., "Expression monitoring by hybridization to high-density oligonucleotide arrays," Nature Biotechnology, 14:1675-1680 (1996); and Blanchard et al., "Sequence to array: Probing the genome's secrets," Nature Biotechnology, 14:1649 (1996)). In organisms for which the complete genome is known, it is possible to analyze the transcripts of all genes within the cell. With other organisms, such as humans, for which there is an increasing knowledge regarding the genome, it is possible to simultaneously monitor large numbers of the genes within the cell. [0004] One aspect of human biology/genomic function that is of great interest to the medical research community is cancer. Currently, genetic samples have been taken from patients having various stages of various types of cancer. Such samples have provided an extensive genetic data collection. To provide a system of organization, such genetic data are collected in DNA microarrays, which are sometimes commonly referred to as biochips, DNA chips, gene arrays, gene chips, and genome chips. [0005] DNA microarrays exploit a phenomenon known as base-pairing or hybridization. To form the array, genetic samples are arranged in an orderly manner (typically in a rectangular grid) on a substrate. Examples of commonly used substrates include microplates and blotting membranes. Many modern microarrays include an array of oligonucleotide or peptide nucleic acid (PNA) probes, and the array is synthesized either in situ (on-chip) or by conventional synthesis followed by on-chip immobilization. The array on the chip is exposed to labeled sample DNA, hybridized, and the identity/abundance of complementary sequences are determined. [0006] There are two major uses of DNA microarray technology. The first involves identification of the gene sequence. The second involves determination of expression level of genes, generally referred to as the abundance of the genes. In particular, expression or abundance of a gene is a measure of a relative level of activity of the gene in replication or translation in the presence of the probe. By analyzing the abundance of various genes in people of various conditions, a relationship between the genetic state of a person, in terms of relative levels of activity of various genes of that person, and that person's condition is assessed. To conduct such analysis, such arrays of expression levels include metadata describing characteristics of the people whose genetic material is sampled and additional metadata which identifies specific genes whose expression levels are represented in such arrays. [0007] The use of microarrays are already being used for a number of beneficial purposes including, for example, identifying biomarkers of cancer (Welsh, J B et al., "Large-scale delineation of secreted protein biomarkers overexpressed in cancer tissue and serum," PNAS, 100(6):3410-3415 (March 2003)), creating gene expression-based classifications of cancers (Alzadeh, A A et al., "Distinct types of diffuse large B-cell lymphoma identified by gene expression profiling," Nature, 403:513-11 (2000); and Garber, M E et al., "Diversity of gene expression in adenocarcinoma of the lung," Proc Natl Acad Sci USA, 98:13784-9 (2001)), and in drug discovery (Marton, M J et al., "Drug target validation and identification of secondary drug target effects using Microarrays," Nat Med, 4(11):1293-301 (1998); and Gray, N S et al., "Exploiting chemical libraries, structure, and genomics in the search for kinase inhibitors," Science, 281:533-538 (1998)). One tool that has been applied to microarrays to decipher and compare genome expression patterns in biological systems is Significance Analysis of Microarrays, or SAM (Tusher, V. et al., "Significance analysis of microarrays applied to ionizing radiation response," Proceedings of the National Academy of Sciences, 2001. First published Apr. 17, 2001, 10.1073/pnas.091062498). This statistical method was developed as a cluster tool for use in identifying genes with statistically significant changes in expression. SAM has been used for a variety of purposes, including identifying potential drugs that would be effective in treating various conditions associated with specific gene expressions (Bunney W E, et al., "Microarray technology: a review of new strategies to discover candidate vulnerability genes in psychiatric disorders," Am J Psychiatry, 160(4):657-66 (April 2003)). [0008] The known SVM or (Support Vector Machine) (as described in Michael P. et al., "Knowledge-based analysis of microarray gene expression data by using support vector machines," Proceedings of the National Academy of Sciences, 97(1):262-67 (2000)) is a correlation tool shown to perform well in multiple areas of biological analysis, including evaluating microarray expression data (Brown et al, "Knowledge-based analysis of microarray gene expression data by using support vector machines," Proc Natl Acad Sci USA, 97:262-267 (2000)), detecting remote protein homologies (Jaakkola, T. et al., "Using the Fisher kernel method to detect remote protein homologies," Proceedings of the 7.sup.th International Conference on Intelligent Systems for Molecular Biology, AAAI Press, Menlo Park, Calif. (1999)), and recognizing translation initiation sites (Zien, A. et al., "Engineering support vector machine kernels that recognize translation initiation sites," Bioinformatics, 16(9):799-807 (2000)). When used for classification, SVMs separate a given set of binary labeled training data with a hyper-plane that is maximally distant from set of data (the "maximal margin hyper-plane"). Where no linear separation is possible, SVMs utilize the technique of "kernels" to automatically realize a non-linear mapping to a feature space (Furey, T. S. et al., "Support vector machine classification and validation of cancer tissue samples using microarray expression data," Bioinformatics, 16(10):906-914 (2000)). [0009] Ranked as the third most commonly diagnosed cancer and the second leading cause of cancer deaths in the United States (American Cancer Society, "Cancer facts and figures," Washington, D.C.: American Cancer Society (2000)), colon cancer is a deadly disease afflicting nearly 130,000 new patients yearly in the United States. Colon cancer is the only cancer that occurs with approximately equal frequency in men and women. There are several potential risk factors for the development of colon and/or rectal cancer. Known factors for the disease include older age, excessive alcohol consumption, sedentary lifestyle (Reddy, B. S., "Dietary fat and its relationship to large bowel cancer," Cancer Res., 41:3700-3705 (1981)), and genetic predisposition (Potter, J D "Colorectal cancer: molecules and populations," J Natl Cancer Institute, 91:916-932 (1999)). [0010] Several molecular pathways have been linked to the development of colon cancer (see, for example, Leeman M F, et al., "New insights into the roles of matrix metalloproteinases in colorectal cancer development and progression," J Pathol., 201(4):528-34 (2003); Kanazawa, T et al., "Does early polypoid colorectal cancer with depression have a pathway other than adenoma-carcinoma sequence?," Tumori., 89(4):408-11 (2003); and Notarnicola, M. et al., "Genetic and biochemical changes in colorectal carcinoma in relation to morphologic characteristics," Oncol Rep., 10(6):1987-91 (2003)), and the expression of key genes in any of these pathways may be affected by inherited or acquired mutation or by hypermethylation. A great deal of research has been performed with regard to identifying genes for which changes in expression may provide an early indicator of colon cancer or a predisposition for the development of colon cancer. Unfortunately, no research has yet been conducted on identifying specific genes associated with colorectal cancer and specific outcomes to provide an accurate prediction of prognosis. [0011] Survival of patients with colon and/or rectal cancer depends to a large extent on the stage of the disease at diagnosis. Devised nearly seventy years ago, the modified Dukes' staging system for colon cancer, discriminates four stages (A, B, C, and D), primarily based on clinicopathologic features such as the presence or absence of lymph node or distant metastases. Specifically, colonic tumors are classified by four Dukes' stages: A, tumor within the intestinal mucosa; B, tumor into muscularis mucosa; C, metastasis to lymph nodes and D, metastasis to other tissues. Of the systems available, the Dukes' staging system, based on the pathological spread of disease through the bowel wall, to lymph nodes, and to distant organ sites such as the liver, has remained the most popular. Despite providing only a relative estimate for cure for any individual patient, the Dukes' staging system remains the standard for predicting colon cancer prognosis, and is the primary means for directing adjuvant therapy. [0012] The Dukes' staging system, however, has only been found useful in predicting the behaviour of a population of patients, rather than an individual. For this reason, any patient with a Dukes A, B, or C lesion would be predicted to be alive at 36 months while a patient staged as Dukes D would be predicted to be dead. Unfortunately, application of this staging system results in the potential over-treatment or under-treatment of a significant number of patients. Further, Dukes' staging can only be applied after complete surgical resection rather than after a pre-surgical biopsy. [0013] Microarray technology, as described above, has permitted development of multi-organ cancer classifiers (Giordano, T. J. et al., "Organ-specific molecular classification of primary lung, colon, and ovarian adenocarcinomas using gene expression profiles," Am J Pathol, 159:1231-8 (2001); Ramaswamy, S. et al., "Multiclass cancer diagnosis using tumor gene expression signatures," Proc Natl Acad Sci USA, 98:15149-54 (2001); and Su, A. I. et al., "Molecular classification of human carcinomas by use of gene expression signatures," Cancer Res, 61:7388-93 (2001)), identification of tumor subclasses (Dyrskjot, L. et al., "Identifying distinct classes of bladder carcinoma using microarrays," Nat Genet, 33:90-6 (2003); Bhattacharjee, A. et al., "Classification of human lung carcinomas by mRNA expression profiling reveals distinct adenocarcinoma subclasses," Proc Natl Acad Sci USA, 98:13790-5 (2001); Garber, M. E. et al., "Diversity of gene expression in adenocarcinoma of the lung," Proc Natl Acad Sci USA, 98:13784-9. (2001); and Sorlie, T. et al., "Gene expression patterns of breast carcinomas distinguish tumor subclasses with clinical implications," Proc Natl Acad Sci USA, 98:10869-74 (2001)), discovery of progression markers (Sanchez-Carbayo, M. et al., "Gene Discovery in Bladder Cancer Progression using cDNA Microarrays," Am J Pathol, 163:505-16 (2003); and Frederiksen, C M, et al., "Classification of Dukes' B and C colorectal cancers using expression arrays," J Cancer Res Clin Oncol, 129:263-71 (2003)); and prediction of disease outcome (Henshall, S M et al., "Survival analysis of genome-wide gene expression profiles of prostate cancers identifies new prognostic targets of disease relapse," Cancer Res, 63:4196-203 (2003); Shipp, M A et al., "Diffuse large B-cell lymphoma outcome prediction by gene-expression profiling and supervised machine learning," Nat Med, 8:68-74 (2002); Beer, D G et al., "Gene-expression profiles predict survival of patients with lung adenocarcinoma," Nat Med, 8:816-24 (2002); Pomeroy, S L et al., "Prediction of central nervous system embryonal tumor outcome based on gene expression," Nature, 415:436-42 (2002); van 't Veer, L J et al., "Gene expression profiling predicts clinical outcome of breast cancer: Nature, 415:530-6. (2002); Vasselli, J R et al., "Predicting survival in patients with metastatic kidney cancer by gene-expression profiling in the primary tumor," Proc Natl Acad Sci USA, 100:6958-63 (2003); and Takahashi, M. et al., "Gene expression profiling of clear cell renal cell carcinoma: gene identification and prognostic classification," Proc Natl Acad Sci USA, 98:9754-9 (2001)) in many types of cancer. [0014] Classification of patient prognosis by microarray analysis has promise in predicting the long-term outcome of any one individual based on the gene expression profile of the tumor at diagnosis. Inherent to this approach is the hypothesis that every tumor contains informative gene expression signatures, at the time of diagnosis, which can direct the biological behaviour of the tumor over time. To date, however, little success has been achieved in developing a classifier that will predict colon cancer outcome equivalent to or better than that which is possible using the standard clinicopathologic staging systems (i.e., Dukes' stage system). What is needed is a particularly effective mechanism for analyzing genomic array data to provide a classifier that accurately predicts cancer outcomes, in particular, colon cancer outcomes. BRIEF SUMMARY OF THE INVENTION [0015] The present invention provides systems and methods for predicting outcomes in patients diagnosed with cancer. Specifically, the subject invention utilizes molecular staging with gene expression profiles to stage patients with cancer. In a specific embodiment, the present invention provides a gene expression profile based classifier that provides a means for accurately predicting colon cancer outcome. [0016] In accordance with an aspect of the invention, genes are classified according to degree of correlation with a clinical outcome for a cancer of interest (such as colon cancer). These genes are used to establish a set of reference gene expression levels (also referred to herein as a "classifier"). Biological information regarding the patient is received and used to extrapolate intracellular gene expression. The intracellular gene expression levels are compared to those in the classifier to predict clinical outcome. [0017] In one embodiment of the invention, a method is provided in which the specific gene signatures for colon cancer are identified. To do so, frozen tumor specimens form patients with known outcomes are collected and frozen. The outcomes are linked to a specific core set of genes that are weighted in importance by (1) selecting genes of interest by applying microarray analysis; (2) producing a classifier using support vector machines (SVM); and (3) cross-validating the genes of interest and the classifier by comparing them against an independent set of test data. In a preferred embodiment, significance analysis of microarrays (SAM) is utilized to select genes of interest. [0018] Genome wide microarray analyses can produce large datasets that can be pattern-matched to clinicopathologic parameters such as patient outcomes and prognosis. Accordingly, the subject invention identifies gene expression signatures that would predict colon cancer outcome more accurately than the well-accepted Dukes' staging system. [0019] In one embodiment, a group of colon cancer patients was examined to develop a survival classifier, which was subsequently validated using an entirely independent test set of data derived on a different microarray platform at a different performance site. The classifier of the subject invention was ultimately based on a core set of genes selected for their correlation to survival. A number of the genes in the core set demonstrated intrinsic biological significance for colon cancer progression. [0020] With the ability to predict cancer outcomes/prognosis using the subject invention, appropriate treatment protocols can be selected for patients. For example, patients assessed using the subject invention and identified to have poor outcomes may be treated more aggressively or with specific agents (i.e., anti-sense agents, RNA inhibition agents, small molecule inhibitors of the cancer activity, gene therapy, etc.). Accordingly, an important contribution of the prognosis/survival classifier of the present invention is the ability to identify those Dukes' stage B and C cases for which chemotherapy may be beneficial. DESCRIPTION OF THE FIGURES Continue reading... Full patent description for Methods for predicting cancer outcome and gene signatures for use therein Brief Patent Description - Full Patent Description - Patent Application Claims Click on the above for other options relating to this Methods for predicting cancer outcome and gene signatures for use therein patent application. ###  How KEYWORD MONITOR works... a FREE service from FreshPatents1. Sign up (takes 30 seconds). 2. Fill in the keywords to be monitored. 3. Each week you receive an email with patent applications related to your keywords. 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