Genotyping Performance between Saliva and Blood-Derived Genomic DNAs on the DMET Array: A Comparison

The Affymetrix Drug Metabolism Enzymes and Transporters (DMET) microarray is the first assay to offer a large representation of SNPs conferring genetic diversity across known pharmacokinetic markers. As a convenient and painless alternative to blood, saliva samples have been reported to work well for genotyping on the high density SNP arrays, but no reports to date have examined this application for saliva-derived DNA on the DMET platform. Genomic DNA extractions from saliva samples produced an ample quantity of genomic DNA for DMET arrays, however when human amplifiable DNA was measured, it was determined that a large percentage of this DNA was from bacteria or fungi. A mean of 37.3% human amplifiable DNA was determined for saliva-derived DNAs, which results in a significant decrease in the genotyping call rate (88.8%) when compared with blood-derived DNAs (99.1%). More interestingly, the percentage of human amplifiable DNA correlated with a higher genotyping call rate, and almost all samples with more than 31.3% human DNA produced a genotyping call rate of at least 96%. SNP genotyping results for saliva derived DNA (n = 39) illustrated a 98.7% concordance when compared with blood DNA. In conclusion, when compared with blood DNA and tested on the DMET array, saliva-derived DNA provided adequate genotyping quality with a significant lower number of SNP calls. Saliva-derived DNA does perform very well if it contains greater than 31.3% human amplifiable DNA

Genetic variation in metabolizing enzyme and transporter genes: Comprehensive assessment in 3 major East Asian subpopulations with comparison to Caucasians and Africans

10.1177/0091270009355161Journal of Clinical Pharmacology508929-940JCPC

An exploratory study by DMET array identifies a germline signature associated with imatinib response in gastrointestinal stromal tumor

Imatinib represents the standard therapy for gastrointestinal stromal tumor (GIST) patients with metastatic/unresectable disease. Despite the excellent results achieved with its introduction, the majority of patients quite invariably experience disease progression. The aim of this study was to understand the contribution of germline DNA polymorphisms in discriminating between imatinib clinical response [evaluated as progression free survival (PFS)] and toxicity. In particular, a discovery cohort (34 GIST with a KIT exon 11 primary mutation, and no toxicity) was analyzed through DMET array that interrogates 1936 variants in 231 genes of the ADME process. We further confirmed the genotype of selected variants in an extended cohort of 49 patients (the original cohort and 15 new cases, all with exon 11 primary mutation), identifying 6 SNPs\u2014 ABCB4 rs1202283, ABCC2 rs2273697, ABCG1 rs1541290, CYP11B1 rs7003319, CYP7B1 rs6987861, and NQO1 rs10517\u2014significantly associated with response to imatinib. Three SNPs, ABCB4 rs1202283, ABCC2 rs2273697, and NQO1 rs10517, which had a significant association after adjusted multivariate analysis, were included in a genetic prediction model. We confirmed that these SNPs could stratify the cohort of 49 patients according to the risk of developing progression under imatinib treatment. In conclusion, we identified a genetic signature of response to imatinib therapy in GIST patients able to stratify patients at low and high risk to progress, according to their genotype