Pharmacogenomic discovery using cell-based models

Abstract

Quantitative variation in response to drugs in human populations is multifactorial; genetic factors probably contribute to a significant extent. Identification of the genetic contribution to drug response typically comes from clinical observations and use of classic genetic tools. These clinical studies are limited by our inability to control environmental factors in vivo and the difficulty of manipulating the in vivo system to evaluate biological changes. Recent progress in dissecting genetic contribution to natural variation in drug response through the use of cell lines has been made and is the focus of this review. A general overview of current cell-based models used in pharmacogenomic discovery and validation is included. Discussion includes the current approach to translate findings generated from these cell-based models into the clinical arena and the use of cell lines for functional studies. Specific emphasis is given to recent advances emerging from cell line panels, including the International HapMap Project and the NCI60 cell panel. These panels provide a key resource of publicly available genotypic, expression, and phenotypic data while allowing researchers to generate their own data related to drug treatment to identify genetic variation of interest. Interindividual and interpopulation differences can be evaluated because human lymphoblastoid cell lines are available from major world populations of European, African, Chinese, and Japanese ancestry. The primary focus is recent progress in the pharmacogenomic discovery area through ex vivo models.

Fig. 1.

Analysis methods for cell-based models in pharmacogenomics. Schema of how cell models can be applied to pharmacogenomics research. Each circle describes variables that can be measured within cell lines and the overlap describes the methods applied to finding relationships between the two variables. The use of cell lines within pedigrees will allow for heritability and linkage analysis. In addition, candidate gene or genome-wide association (GWA) studies can be used for examining genotype-phenotype relationships in cells of both unrelated and related persons. Gene expression data can be analyzed with pharmacological endpoints in cells to determine expression profiles that are associated with sensitivity or resistance to a drug. The role of genetic variants in regulating gene expression, regardless of drug sensitivity, can also be examined in eQTL and allelic imbalance studies in cell-based models. Integrating genetic, expression, and pharmacological phenotypes can be combined in a “triangle model” to determine genetic markers that are associated with cellular phenotype through their effect on gene expression.