Genome-wide association and pharmacological profiling of 29 anticancer agents using lymphoblastoid cell lines. (February 2014) - Brown, Chad C; Havener, Tammy M; Medina, Marisa W; Jack, John R; Krauss, Ronald M; McLeod, Howard L; Motsinger-Reif, Alison A
Aim: Association mapping with lymphoblastoid cell lines (LCLs) is a promising approach in pharmacogenomics research, and in the current study we utilized LCLs to perform association mapping for 29 chemotherapy drugs.Materials & methods: Currently, we use LCLs to perform genome-wide association mapping of the cytotoxic response of 520 European–Americans to 29 different anticancer drugs; the largest LCL study to date. A novel association approach using a multivariate analysis of covariance design was employed with the software program MAGWAS, testing for differences in the dose–response profiles between genotypes without making assumptions about the response curve or the biologic mode of association. Additionally, by classifying 25 of the 29 drugs into eight families according to structural and mechanistic relationships, MAGWAS was used to test for associations that were shared across each drug family. Finally, a unique algorithm using multivariate responses and multiple linear regressions across pairs of response curves was used for unsupervised clustering of drugs.Results: Among the single-drug studies, suggestive associations were obtained for 18 loci, 12 within/near genes. Three of these, MED12L, CHN2 and MGMT, have been previously implicated in cancer pharmacogenomics. The drug family associations resulted in four additional suggestive loci (three contained within/near genes). One of these genes, HDAC4, associated with the DNA alkylating agents, shows possible clinical interactions with temozolomide. For the drug clustering analysis, 18 of 25 drugs clustered into the appropriate family.Conclusion: This study demonstrates the utility of LCLs in identifying genes that have clinical importance in drug response and for assigning unclassified agents to specific drug families, and proposes new candidate genes for follow-up in a large number of chemotherapy drugs. Original submitted 16 August 2013; Revision submitted 16 October 2013