|Grant Number:||5R01CA161608-02 Interpret this number|
|Primary Investigator:||Motsinger-Reif, Alison|
|Organization:||North Carolina State University Raleigh|
|Project Title:||Genetic Etiology of Cancer Drug Response|
DESCRIPTION (provided by applicant): Important progress continues to be made in the treatment of most common cancers, but therapeutic benefit remains difficult to predict and severe or fatal adverse events occur frequently. The Human Genome Project has fueled the notion that genetic information can produce effective and cost-efficient selection of therapies for individual patients, but validated genetic signatures that predict response to most chemotherapy regimens remain to be identified. Numerous genes potentially influence drug response, but current candidate-gene approaches are limited by the requirement of a priori knowledge about the genes involved and the moderate size of most clinical trials often limits the power of in vitro genome wide association studies (GWAS) for cancer pharmacogenomics discovery. In response to these limitations, we have undertaken a thorough, pharmacogenomic assessment of cytotoxic effect of the majority of FDA approved anti-cancer compounds using an ex vivo model system to determine the heritability of drug-induced cell killing to prioritize drugs for pharmacogenomic mapping. These results are an important first step, and while high heritability of a trait does not guarantee successful association mapping results, it represents an important first step and the results will be used to prioritize drugs with high heritabilities for genome-wide association mapping. In the current proposal, GWAS mapping of cytotoxic agents will be performed in a European American population, and then replication GWAS mapping will be performed in an East Asian population. In addition to discovering and validating genetic variants that predict drug response, the wealth of data collected will be used to dissect the underlying etiology of drug response traits, including assessing the relative contribution of genetic, environmental, and interaction components of variation. These results will provide crucial insight to prioritize genetic variants for follow-up in precious clinical population resources, and potentially reveal new insight into the overall etiology of drug responses.
Pharmacogenomic assessment of Mexican and Peruvian populations.
Authors: Marsh S, King CR, Van Booven DJ, Revollo JY, Gilman RH, McLeod HL
Source: Pharmacogenomics, 2015 Apr;16(5), p. 441-8.
An investigation of gene-gene interactions in dose-response studies with Bayesian nonparametrics.
Authors: Beam AL, Motsinger-Reif AA, Doyle J
Source: BioData Min, 2015;8, p. 6.
EPub date: 2015 Feb 6.
Population-based in vitro hazard and concentration-response assessment of chemicals: the 1000 genomes high-throughput screening study.
Authors: Abdo N, Xia M, Brown CC, Kosyk O, Huang R, Sakamuru S, Zhou YH, Jack JR, Gallins P, Xia K, Li Y, Chiu WA, Motsinger-Reif AA, Austin CP, Tice RR, Rusyn I, Wright FA
Source: Environ Health Perspect, 2015 May;123(5), p. 458-66.
EPub date: 2015 Jan 13.
Bevacizumab and the risk of arterial and venous thromboembolism in patients with metastatic, castration-resistant prostate cancer treated on Cancer and Leukemia Group B (CALGB) 90401 (Alliance).
Authors: Patel JN, Jiang C, Hertz DL, Mulkey FA, Owzar K, Halabi S, Ratain MJ, Friedman PN, Small EJ, Carducci MA, Mahoney JF, Kelley MJ, Morris MJ, Kelly WK, McLeod HL
Source: Cancer, 2015 Apr 1;121(7), p. 1025-31.
EPub date: 2014 Nov 21.
Bayesian neural networks for detecting epistasis in genetic association studies.
Authors: Beam AL, Motsinger-Reif A, Doyle J
Source: BMC Bioinformatics, 2014 Nov 21;15, p. 368.
EPub date: 2014 Nov 21.
Beyond IC50 s: Towards Robust Statistical Methods for in vitro Association Studies.
Authors: Beam A, Motsinger-Reif A
Source: J Pharmacogenomics Pharmacoproteomics, 2014 Mar 1;5(1), p. 1000121.
Lymphoblastoid cell lines models of drug response: successes and lessons from this pharmacogenomic model.
Authors: Jack J, Rotroff D, Motsinger-Reif A
Source: Curr Mol Med, 2014;14(7), p. 833-40.
An adaptive permutation approach for genome-wide association study: evaluation and recommendations for use.
Authors: Che R, Jack JR, Motsinger-Reif AA, Brown CC
Source: BioData Min, 2014;7, p. 9.
EPub date: 2014 Jun 14.
Application of next generation sequencing to CEPH cell lines to discover variants associated with FDA approved chemotherapeutics.
Authors: Hariani GD, Lam ET, Havener T, Kwok PY, McLeod HL, Wagner MJ, Motsinger-Reif AA
Source: BMC Res Notes, 2014 Jun 12;7, p. 360.
EPub date: 2014 Jun 12.
Genome-wide association and pharmacological profiling of 29 anticancer agents using lymphoblastoid cell lines.
Authors: Brown CC, Havener TM, Medina MW, Jack JR, Krauss RM, McLeod HL, Motsinger-Reif AA
Source: Pharmacogenomics, 2014 Feb;15(2), p. 137-46.
Cancer pharmacogenomics: early promise, but concerted effort needed.
Authors: McLeod HL
Source: Science, 2013 Mar 29;339(6127), p. 1563-6.
Multivariate methods and software for association mapping in dose-response genome-wide association studies.
Authors: Brown CC, Havener TM, Medina MW, Krauss RM, McLeod HL, Motsinger-Reif AA
Source: BioData Min, 2012 Dec 12;5(1), p. 21.
EPub date: 2012 Dec 12.
A genome-wide association analysis of temozolomide response using lymphoblastoid cell lines shows a clinically relevant association with MGMT.
Authors: Brown CC, Havener TM, Medina MW, Auman JT, Mangravite LM, Krauss RM, McLeod HL, Motsinger-Reif AA
Source: Pharmacogenet Genomics, 2012 Nov;22(11), p. 796-802.
Loss of power in two-stage residual-outcome regression analysis in genetic association studies.
Authors: Che R, Motsinger-Reif AA, Brown CC
Source: Genet Epidemiol, 2012 Dec;36(8), p. 890-4.
EPub date: 2012 Aug 31.
A comparison of association methods for cytotoxicity mapping in pharmacogenomics.
Authors: Brown C, Havener TM, Everitt L, McLeod H, Motsinger-Reif AA
Source: Front Genet, 2011;2, p. 86.
EPub date: 2011 Dec 14.