Statistical Genetics of Dose Response Traits

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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. Combination therapy is quite common in modern chemotherapy treatment since drugs often work synergistically, and it is an important progression in the use of the LCL model to expand work for drug combinations. Lymphoblastoid cell lines are a highly successful model for evaluating the genetic etiology of cancer drug response, but applications using this model have typically focused on single drugs. Combination therapy is quite common in modern chemotherapy treatment since drugs often work synergistically, and it is an important progression in the use of the LCL model to expand work for drug combinations. We demonstrated that synergy occurs and can be quantified in LCLs across a range of clinically important drug combinations. Here we used cell lines from extended pedigrees to demonstrate that there is a substantial heritable component to synergistic drug response. This demonstration supports the premise of expanding the use of the LCL model to perform association mapping for combination therapies. This study also presents a number of statistical/computational challenges. With my recently graduated PhD student Dr. Jun Ma we developed an approach that addresses challenges in nonlinear dose-response relationships. Nonlinear dose-response relationships exist extensively in the cellular, biochemical, and physiologic processes that are affected by varying levels of biological, chemical, or radiation stress. Nonlinear dose-response relationships exist extensively in the cellular, biochemical, and physiologic processes that are affected by varying levels of biological, chemical, or radiation stress. Therefore, we propose the use of an EA for dose-response modeling for a range of potential response model functional forms. This new method can not only fit the most commonly used nonlinear dose-response models (eg, exponential models and 3-, 4-, and 5-parameter logistic models) but also select the best model if no model assumption is made, which is especially useful in the case of high-throughput curve fitting. An R package to implement the method was developed. Also, with Dr. Farida, we used the multivariate analysis of variance method to identify in vivo variables associated with in vitro dose response in a dataset from collaborators at UNC Chapel Hill. In a new study that includes both in vivo and in vitro data from breast cancer patients, race and smoking status were found to be significantly associated with in vitro dose response in LCLs. The smoking status of the donor individuals, from whom the LCLs are created, is usually unknown and hence not controlled for in dose response analyses in LCLs. Further research is required to understand the mechanism by which exposure to smoking in vivo affects in vitro dose response in LCLs. 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.