Genome-Wide molecular epidemiology of treatment outcome and cancer risk

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DESCRIPTION (provided by applicant): The goal of this mentored career development award application is to consolidate my diverse training experiences in laboratory and clinical pharmacology, human genetics, and clinical investigation into a focused research program in "Genome-wide molecular epidemiology of treatment outcome and cancer risk". The purpose of this program will be to discover and test the biological function of novel germline genetic variation that might serve as markers of 1) severe toxicity and survival in cancer patients treated with chemotherapy, and 2) cancer susceptibility. These markers will be identified through genome-wide (GW) association studies (GWAS), and my previous training and research experience do not pertain to GW investigations. To undertake research that combines GW data with molecular, genetic, epidemiology and bioinformatics techniques, I will be required to apply these technologies to genomic population data. This program will be constructed through a series of clinical, epidemiology, and laboratory studies. I propose to use unbiased genomic approaches for the discovery of novel candidate genes as markers of outcome of chemotherapy. The same approaches will be also applied to identify novel candidates of cancer susceptibility by benefiting from publicly available resources of GW information from control individuals without cancer. Enrichment of the information generated from the GW data will be derived from additional genotyping and/or resequencing of genomic regions that showed significant associations in the GWAS. It is very likely that the significant single nucleotide polymorphisms (SNPs) in the GWAS will not have an established molecular function, and several strategies will be used to support the observed clinical associations. The proposed research plan and subsequent investigations will substantially increase the understanding of the pleiotropic effects of heritable genetic information in humans. This work will provide a knowledge framework for designing controlled replication studies of treatment outcome and cancer risk using highly selected informative markers. These applications could inform interventions designed to establish the risk-benefit ratio of cancer chemotherapy and to reduce the prevalence of cancer in the population.