Generalized Multifactor Dimensionality Reduction (GMDR) Analysis of Drug-Metabolizing Enzyme-Encoding Gene Polymorphisms may Predict Treatment Outcomes in Indian Breast Cancer Patients

Prediction of response and toxicity of chemotherapy can help personalize the treatment and choose effective yet non-toxic treatment regimen for a breast cancer patient. Interplay of variations in various drug-metabolizing enzyme (DME)-encoding genes results in variable response and toxicity of chemotherapeutic drugs. Generalized multi-analytical (GMDR) approach was used to determine the influence of the combination of variants of genes encoding phase 0 (SLC22A16); phase I (CYP450, NQO1); phase II (GSTs, MTHFR, UGT2B15); and phase III (ABCB1) DMEs along with confounding factors on the response and toxicity of chemotherapeutic drugs in breast cancer patients.In an Indian breast cancer patient cohort (n = 234), response to neo-adjuvant chemotherapy (n = 111) and grade 2-4 toxicity to chemotherapy were recorded. Patients were genotyped for 19 polymorphisms selected in four phases of DMEs by PCR or PCR-RFLP or Taqman allelic discrimination assay. Binary logistic regression and GMDR analysis was performed. Bonferroni test for multiple comparisons was applied, and p value was considered to be significant at0.025.For ABCB1 1236CT polymorphism, CT genotype was found to be significantly associated with response to NACT in uni-variate and multi-variate analysis (p = 0.018; p = 0.013). The TT genotype of NQO1 609CT had a significant association with (absence of) grade 2-4 toxicity in uni-variate analysis (p = 0.021), but a non-significant correlation in multi-variate analysis. In GMDR analysis, interaction of CYP3A5*3, NQO1 609CT, and ABCB1 1236CT polymorphisms yielded the highest testing accuracy for response to NACT (CVT = 0.62). However, for grade 2-4 toxicity, CYP2C19*2 and ABCB1 3435CT polymorphisms yielded the best interaction model (CVT = 0.57).This pharmacogenetic study suggests a role of higher order gene-gene interaction of DME-encoding genes, along with confounding factors, in determination of treatment outcomes and toxicity in breast cancer patients. This can be used as a potential objective tool for individualizing breast cancer chemotherapy with high efficacy and low toxicity.