Bayesian hierarchical nonlinear models for pharmacogenomic cytotoxicity studies

点击翻译按钮获取中文摘要

DESCRIPTION (provided by applicant): In the past five years, there has been an increased interest in "individualized medicine" and thus pharmacogenomics in cancer research has moved to the forefront as well. Pharmacogenetics is the study of the role of inheritance in individual variation in response to drugs, nutrients and other xenobiotics. In this post-genomic era, pharmacogenetics has evolved into pharmacogenomics, a discipline that has been heralded as one of the first major clinical applications of the striking advances that have occurred and continue to occur in human genomic science. Pharmacogenomic research has also been shifting from a focus on single genes and single SNPs to intragene haplotypes as well as pathway-based and genome-wide association studies. Pathway- based studies involve genes for which we already know function, while genome-wide association studies can help us to identify additional genes outside of known pathways of drug transport, drug metabolism and drug targets. As a result, these two approaches - pathway-based and genome-wide - are complementary. Statistical methods that combine various sources of data would likely provide novel insights, yet are lacking in pharmacogenomic studies. For example, the Mayo Pharmacogenomics Research Network (PGRN) group has been conducting multiple studies of the effect of anti- cancer drugs on lymphoblastoid cell lines to begin to define the effect of common genetic variation on drug response phenotypes. These phenotypes have included cytotoxicity measures at multiple concentrations, basal mRNA expression array data, post-drug treated mRNA expression data, metabolite data, resequencing data for genes in known drug pathways, and genome-wide genetic information in the form of single nucleotide polymorphisms (SNPs) across the genome. Currently, investigation of the genetic variation in these cell lines with drug concentration endpoints (cytotoxicity) is often completed by either analyzing a drug concentration endpoint measured at a single drug dosage or a summary measure of the dose-response curve (i.e., dose that inhibits 50% of cell growth, IC50). A more comprehensive analysis of the impact of genetic variation on all aspects of the dose- response curve may lend insight into the pharmacogenomics of a particular drug. A statistical approach that could account for disparate "layers" of genomic and cytotoxicity dose-response data is based on Bayesian methodology. Over the last few decades, applications of Bayesian methods by Markov Chain Monte Carlo (MCMC) have increased with the advancement of computers and computational methods, particular with application to genetic data. In this current grant application, we intend to develop novel pharmacogenomic models for the analysis of cytotoxicity data collected from the "Human Variation Panel" and pancreatic cancer patient cell lines treated with gemcitabine. These novel models will assist researchers in generating hypotheses that will lead to better understanding of the complex nature of the relationship between genotype and drug response, leading eventually to the development of "individualized therapy" for cancer patients. PUBLIC HEALTH RELEVANCE: In this current proposal, the applicant proposes to combine many sources of data collected from the "Human Variation Panel" cell lines and pancreatic cancer patients into statistical models using Bayesian methods that will help the investigator to generate hypotheses that will lead to better understanding of the complex nature of the relationship between genotype and drug response, leading eventually to the development of "personalized therapy" for cancer patients. This research will fill a void in the cutting-edge field of the development and application of new genetic statistical methods in pharmacogenomics in which genotype - phenotype correlation studies are conducted using repeated measurements on cell-lines, at different drug concentrations, and a variety of genomic data types.