Genotyping panel for assessing response to cancer chemotherapy
- Equal contributors
1 Program in Pharmacogenomics, Department of Pharmacology, Comprehensive Cancer Center, College of Medicine and Public Health, The Ohio State University, 5072 Graves Hall, 333 West 10th Avenue, Columbus, OH 43210-1239, USA
2 Department of Pathology, College of Medicine and Public Health, The Ohio State University, 680 Ackerman Road, Columbus, Ohio 43202, USA
3 Division of Human Genetics, College of Medicine and Public Health, The Ohio State University, 2050 Kenny Road, 8th floor tower, Columbus, OH 43221, USA
BMC Medical Genomics 2008, 1:24 doi:10.1186/1755-8794-1-24Published: 11 June 2008
Variants in numerous genes are thought to affect the success or failure of cancer chemotherapy. Interindividual variability can result from genes involved in drug metabolism and transport, drug targets (receptors, enzymes, etc), and proteins relevant to cell survival (e.g., cell cycle, DNA repair, and apoptosis). The purpose of the current study is to establish a flexible, cost-effective, high-throughput genotyping platform for candidate genes involved in chemoresistance and -sensitivity, and treatment outcomes.
We have adopted SNPlex for genotyping 432 single nucleotide polymorphisms (SNPs) in 160 candidate genes implicated in response to anticancer chemotherapy.
The genotyping panels were applied to 39 patients with chronic lymphocytic leukemia undergoing flavopiridol chemotherapy, and 90 patients with colorectal cancer. 408 SNPs (94%) produced successful genotyping results. Additional genotyping methods were established for polymorphisms undetectable by SNPlex, including multiplexed SNaPshot for CYP2D6 SNPs, and PCR amplification with fluorescently labeled primers for the UGT1A1 promoter (TA)nTAA repeat polymorphism.
This genotyping panel is useful for supporting clinical anticancer drug trials to identify polymorphisms that contribute to interindividual variability in drug response. Availability of population genetic data across multiple studies has the potential to yield genetic biomarkers for optimizing anticancer therapy.