Open Access Highly Accessed Research article

Bioinformatic analyses identifies novel protein-coding pharmacogenomic markers associated with paclitaxel sensitivity in NCI60 cancer cell lines

Lawson Eng12, Irada Ibrahim-zada123, Hamdi Jarjanazi123, Sevtap Savas123, Mehran Meschian1, Kathleen I Pritchard45 and Hilmi Ozcelik123*

Author Affiliations

1 Fred A. Litwin Centre for Cancer Genetics, Samuel Lunenfeld Research Institute, Mount Sinai Hospital, Toronto, ON, Canada

2 Department of Laboratory Medicine and Pathobiology, University of Toronto, Toronto, ON, Canada

3 Department of Pathology and Laboratory Medicine, Mount Sinai Hospital, Toronto, ON, Canada

4 Sunnybrook Odette Cancer Centre, Sunnybrook Health Sciences Centre, Toronto, ON, Canada

5 Department of Medicine, University of Toronto, Toronto, ON, Canada

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BMC Medical Genomics 2011, 4:18  doi:10.1186/1755-8794-4-18

Published: 11 February 2011

Abstract

Background

Paclitaxel is a microtubule-stabilizing drug that has been commonly used in treating cancer. Due to genetic heterogeneity within patient populations, therapeutic response rates often vary. Here we used the NCI60 panel to identify SNPs associated with paclitaxel sensitivity. Using the panel's GI50 response data available from Developmental Therapeutics Program, cell lines were categorized as either sensitive or resistant. PLINK software was used to perform a genome-wide association analysis of the cellular response to paclitaxel with the panel's SNP-genotype data on the Affymetrix 125 k SNP array. FastSNP software helped predict each SNP's potential impact on their gene product. mRNA expression differences between sensitive and resistant cell lines was examined using data from BioGPS. Using Haploview software, we investigated for haplotypes that were more strongly associated with the cellular response to paclitaxel. Ingenuity Pathway Analysis software helped us understand how our identified genes may alter the cellular response to paclitaxel.

Results

43 SNPs were found significantly associated (FDR < 0.005) with paclitaxel response, with 10 belonging to protein-coding genes (CFTR, ROBO1, PTPRD, BTBD12, DCT, SNTG1, SGCD, LPHN2, GRIK1, ZNF607). SNPs in GRIK1, DCT, SGCD and CFTR were predicted to be intronic enhancers, altering gene expression, while SNPs in ZNF607 and BTBD12 cause conservative missense mutations. mRNA expression analysis supported these findings as GRIK1, DCT, SNTG1, SGCD and CFTR showed significantly (p < 0.05) increased expression among sensitive cell lines. Haplotypes found in GRIK1, SGCD, ROBO1, LPHN2, and PTPRD were more strongly associated with response than their individual SNPs.

Conclusions

Our study has taken advantage of available genotypic data and its integration with drug response data obtained from the NCI60 panel. We identified 10 SNPs located within protein-coding genes that were not previously shown to be associated with paclitaxel response. As only five genes showed differential mRNA expression, the remainder would not have been detected solely based on expression data. The identified haplotypes highlight the role of utilizing SNP combinations within genomic loci of interest to improve the risk determination associated with drug response. These genetic variants represent promising biomarkers for predicting paclitaxel response and may play a significant role in the cellular response to paclitaxel.