Simultaneous analysis of multiple data types in pharmacogenomic studies using weighted sparse canonical correlation analysis.

Variation in drug response results from a combination of factors that include differences in gender, ethnicity, and environment, as well as genetic variation that may result in differences in mRNA and protein expression. This article presents two integrative analytic approaches that make use of both genome-wide SNP and mRNA expression data available on the same set of subjects: a step-wise integrative approach and a comprehensive analysis using sparse canonical correlation analysis (SCCA). In addition to applying standard SCCA, we present a novel modification of SCCA which allows different weighting for the various pair-wise relationships in the SCCA. These integrative approaches are illustrated with both simulated data and data from a pharmacogenomic study of the drug gemcitabine. Results from these analyses found little overlap in terms of genes detected, possibly detecting different biological mechanisms. In addition, we found the proposed weighted SCCA to outperform its unweighted counterpart in detecting associations between the genomic features and phenotype. Further research is needed to develop and assess new integrative methods for pharmacogenomic studies, as these types of analyses may uncover novel insights into the relationship between genomic variation and drug response.