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.

1 . A system for predicting clinical outcome for a patient diagnosed with cancer comprising a computing means; a user interface means that enables data entry, wherein said interface is coupled to said computing means, wherein said computing means is configured to perform microarray analysis and binary classification to generate a set of genes used in predicting clinical outcome.  
     
     
         2 . The system of  claim 1 , wherein the microarray analysis and is significance analysis of microarrays and the binary classification is support vector machine.  
     
     
         3 . The system of  claim 1 , wherein the computer is further configured to perform leave-one-out cross validation.  
     
     
         4 . The system of  claim 1 , wherein the computer comprises a database for storing the set of genes, said computer further configured to analyzing biological information from a patient against the set of genes to generate a predicted clinical outcome.  
     
     
         5 . The system of  claim 1 , wherein the patient is diagnosed with colon cancer.  
     
     
         6 . A classifier for predicting clinical outcome in a patient diagnosed with cancer comprising a computing means and a user interface, wherein said computing means comprises a storing means and a means for outputting processed data, wherein said storing means comprises a set of genes classified by outcome, wherein said interface is coupled to said computing means.  
     
     
         7 . The classifier of  claim 6 , wherein said set of genes consists of the following genes: N36176; AA149253; AA425320; AA775616; N72847; AA706226; AA976642; AA133215; AA457267; N50073; R38360; AA450205; AA148578; R38640; AA487274; N53172; AA045308; AA045075; N63366; R22340; AA437223; AA481250; AA045793; H87795; AA121806; AA284172; R68106; AA479270; AA432030; R10545; AA453508; A1149393; AA883496; AA167823; A1203139; H19822; W73732; AA777892; AA885478; AA932696; AA481507; H18953; AA709158; AA488652; N39584; H62801; H17638; R43684; N21630; T81317; R45595; T90789; and AA283062.  
     
     
         8 . The classifier of  claim 6 , wherein said set of genes consists of the following genes: AA045075; AA425320; AA437223; AA479270; AA486233; AA487274; AA488652; AA694500; AA704270; AA706226; AA709158; AA775616; AA777892; AA873159; AA969508; A1203139; A1299969; H17364; H17627; H19822; H23551; H62801; H85015; N21630; N36176; N72847; N92519; R27767; R34578; R38360; R43597; R43684; W73732; AA450205; A1081269; R59314; AA702174; A1002566; AA676797; AA453508; W93980; AA045308; AA953396; AA962236; AA418726; R43713; AA664240; AA477404; AA826237; AA007421; AA478952; W93980; AA045308; AA953396; AA962236; AA418726; R43713; AA664240; AA477404; AA826237; AA007421; AA478952; AA885096; H29032; R10545; AA448641; R38266; H17543; T81317; AA453790; R22340; AA987675; N51543; N74527; AA121778; AA258031; AA702422; T64924; R42984; R59360; R63816; T49061; AA016210; AA682585; AA705040; AA909959; A1240881; AA133215; AA699408; AA910771; A1362799; H51549; R06568; AA001604; AA132065; AA490493; AA633845; A1261561; H81024; N75004; W96216; AA045793; AA284172; AA411324; AA448261; AA479952; AA485752; AA504266; AA630376; AA634261; AA701167; AA703019; AA706041; AA773139; AA776813; AA862465; AA977711; A1288845; H15267; H18956; H73608; H99544; N45282; N48270; N59451; N95226; R37028; R66605; T51004; T51316; T72535; and W72103.  
     
     
         9 . The classifier of  claim 6 , wherein said set of genes consists of the following genes: AA007421; AA045075; AA045308; AA418726; AA425320; AA450205; AA453508; AA453790; AA477404; AA478952; AA479270; AA486233; AA487274; AA664240; AA676797; AA702174; AA706226; AA709158; AA775616; AA826237; AA873159; AA969508; AI002566; AI29969; H17364; H19822; H23551; N36176; N72847; R10545; R27767; R34578; R59314; W73732; AA448641; R59360; AA121778; H51549; H81024; AA490493; R42984; AA258031; AA133215; R63816; N95226; N74527; AA702422; A1261561; AA132065; A1362799; AA045793; AA284172; N51632; AA482110; AA485450; AA699408; N70777; AA993736; A1139498; N59721; AA431885; AA911661; AA775865; R30941; AA703019; AA777192; W72103; H15267; H17638; R60193; R92717; AA706041; AA411324; AA504266; AA932696; AA973494; N45100; AA418410; AA725641; AA954482; H45391; T86932; AA279188; AA485752; AA680132; AA977711; W93370; AA036727; AA071075; AA464612; AA481250; AA598659; AA682905; R17811; W93592; AA017301; AA046406; AA256304; AA416759; AA448261; AA452130; AA457528; AA460542; AA479952; AA481507; AA504342; AA598970; AA630376; AA634261; AA677254; AA757564; AA775888; AA844864; AA862465; AA989139; AI253017; A1394426; H99544; N41021; N45282; N46845; N48270; N59846; R16760; R44546; R92994; T51004; T56281; T70321; and W45025.  
     
     
         10 . The classifier of  claim 6 , wherein said set of genes consists of the following genes: N36176; AA149253; AA425320; AA775616; N72847; AA706226; AA883496.  
     
     
         11 . A method for predicting a clinical outcome for a patient diagnosed with cancer, said method comprising the steps of: 
 a) classifying at least one gene that correlates with a clinical outcome;    b) establishing a set of reference gene expression levels based on the at least one gene;    c) receiving biological information from the patient;    d) extrapolating from the biological information the level of intracellular expression of said at least one gene;    e) comparing said level of intracellular expression against said set of reference gene expression levels; and    f) predicting a clinical outcome based on the deviation of the intracellular level expression from that of the reference gene expression levels.    
     
     
         12 . The method of  claim 1 , wherein identification of said at least one gene is performed with any on or combination of the following: significance analysis of microarrays, cluster analysis, support vector technology, neural network, and leave-one-out cross validation.  
     
     
         13 . The method of  claim 1 , further comprising the step of estimating the accuracy of the predicted clinical outcome.  
     
     
         14 . The method of  claim 1 , wherein the biological information is a clinical specimen of bodily fluid or tissue.  
     
     
         15 . The method of  claim 14 , wherein the biological information is a clinical tumor sample.  
     
     
         16 . The method of  claim 1 , wherein the outcome being evaluated is for a patient diagnosed with colon cancer.  
     
     
         17 . The method of  claim 1 , wherein the predicted clinical outcome is the probability of patient survival at a predetermined date.  
     
     
         18 . The method of  claim 1 , further comprising the step of generating a treatment regimen based on the predicted clinical outcome.  
     
     
         19 . The method of  claim 1 , wherein the gene that is identified is one with the accession number selected from the group consisting of: N36176; AA149253; AA425320; AA775616; N72847; AA706226; AA976642; AA133215; AA457267; N50073; R38360; AA450205; AA148578; R38640; AA487274; N53172; AA045308; AA045075; N63366; R22340; AA437223; AA481250; AA045793; H87795; AA121806; AA284172; R68106; AA479270; AA432030; R10545; AA453508; A1149393; AA883496; AA167823; AI203139; H19822; W73732; AA777892; AA885478; AA932696; AA481507; H18953; AA709158; AA488652; N39584; H62801; H17638 R43684; N21630; T81317; R45595; T90789; and AA283062.