Statistical Genetics of Dose Response Traits

National Institute Of Environmental Health Sciences

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Abstract

Dissecting the genetic etiology of cancer drug response using a lymphoblastoid cell line model has been a longstanding research goal. This year, I have continued my work with my former PhD student Farida Akhtari, and collaborations at North Carolina State University, and UNC Chapel Hill. We have made substantial progress in both methodological aspects, and with applied results. The use of ex-vivo model systems to provide a level of forecasting for in-vivo characteristics remains an important need for cancer therapeutics. The use of lymphoblastoid cell lines (LCLs) is an attractive approach for pharmacogenomics and toxicogenomics, due to their scalability, efficiency, and cost-effectiveness. There is little data on the impact of demographic or clinical covariates on LCL response to chemotherapy. Paclitaxel sensitivity was determined in LCLs from 93 breast cancer patients from the University of North Carolina Lineberger Comprehensive Cancer Center Breast Cancer Database to test for potential associations and/or confounders in paclitaxel dose-response assays. Measures of paclitaxel cell viability were associated with patient data included treatment regimens, cancer status, demographic and environmental variables, and clinical outcomes. We used multivariate analysis of variance to identify the in-vivo variables associated with ex-vivo dose-response. In this unique dataset that includes both in-vivo and ex-vivo data from breast cancer patients, race (P = 0.0049) and smoking status (P = 0.0050) were found to be significantly associated with ex-vivo dose-response in LCLs. Racial differences in clinical dose-response have been previously described, but the smoking association has not been reported. Our results indicate that in-vivo smoking status can influence ex-vivo dose-response in LCLs, and more precise measures of covariates may allow for more precise forecasting of clinical effect. In addition, understanding the mechanism by which exposure to smoking in-vivo effects ex-vivo dose-response in LCLs may open up new avenues in the quest for better therapeutic prediction. We also recently completed a high throughput screen of 44 anti-cancer drugs in this model. Cancer patients exhibit a broad range of inter-individual variability in response and toxicity to several widely used anticancer drugs. Genetic association mapping can be used to understand the genetic etiology of cancer drug response by identifying genes related to differential response. To identify novel genes that influence the response of 44 FDA-approved anticancer drugs widely used to treat various different types of cancer, we screened 680 lymphoblastoid cell lines from the racially and ethnically diverse 1000 Genomes Project with these drugs. Our genome-wide association mapping identified several novel genetic variants associated with the response of a broad range of anticancer drugs. We conducted further analyses and functional validation for one of the genes from our association mapping results, NAD H quinone dehydrogenase, to identify the mechanism of action by which it influences drug response. Ongoing work is continuing on several fronts. We are also continuing the GWAS mapping to a new class of drugs monoclonal antibody treatment.