The overarching goal of this project is to determine host epidemiologic and genetic factors that will bepredictive of efficacy and toxicity of platinum-based chemotherapy or combined with thoracic radiotherapy inNSCLC patients. We will construct a well-characterized cohort of 1,200 NSCLC patients (Stages III and IV -receiving first-line platinum-based chemotherapy definitive thoracic radiotherapy). This cohort will then bestudied for epidemiologic, clinical and a large number of rationally selected germline polymorphisms tocorrelate with the clinical outcome to allow us to construct predictive risk models for clinical efficacy andtoxicity. We estimate there will be ~ 600 patients treated by platinum-based chemotherapy alone, and 600Stage III patients receiving platinum-based chemotherapy plus definitive thoracic radiotherapy. There arethree specific aims: 1) we will identify novel genetic loci that predict efficacy and toxicity to platinum-basedchemotherapy and radiotherapy in all 1,200 patients. We will adopt a pathway-based genotyping andanalyzing approach to evaluate frequencies .of about 8,000 SNPs in genes involved in pathways relevant toplatinum and radiation response. We will examine individual SNP main effects, haplotypes, and thecumulative effect of SNPs in modulating efficacy and toxicity. Our hypothesis is that specific genotypesthat alter the metabolism or action of platinum agents or relevant to the genotoxic effects ofradiotherapy may impact the efficacy and toxicity of patients to these therapies. 2) we will applymachine-learning tools to identify gene-gene and gene-environment interactions influencing NSCLCoutcome. We will develop algorithms to identify subgroups with differing platinum or radiotherapy treatmentefficacy or toxicity. Our hypothesis is that therapeutic response is modulated by common, lowpenetrance polymorphisms, and that these polymorphisms interact with each other and/or hostfactors in determining response to therapy. 3) we will construct predictive risk models for survival andtoxicity by integrating clinical and epidemiologic data with the genetic data from this project,and additionalinformation from other R01 studies devoted to these cohorts such as a series of phenotypic assays. Wehypothesize that the addition of genetic markers to the standard clinical and epidemiologic variableswill improve the prediction of survival and toxicity of the final risk assessment models. We willcompare the prediction accuracy among all patients, patients receiving chemotherapy alone, and patientstreated by combined modality. The risk models resulting from this project may permit clinicians to identifypatients before the start of therapy who are most and least likely to benefit or to develop toxicity and willhave immense clinical benefit in terms of planning chemotherapy and radiotherapy for individual patients.
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