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World Journal of Surgery
Published in: World Journal of Surgery 7/2016

01-07-2016 | Original Scientific Report

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

Authors: Gaurav Agarwal, Sonam Tulsyan, Punita Lal, Balraj Mittal

Published in: World Journal of Surgery | Issue 7/2016

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Abstract

Background

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.

Methods

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 at <0.025.

Results

For ABCB1 1236C>T 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 609C>T 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 609C>T, and ABCB1 1236C>T polymorphisms yielded the highest testing accuracy for response to NACT (CVT = 0.62). However, for grade 2–4 toxicity, CYP2C19*2 and ABCB1 3435C>T polymorphisms yielded the best interaction model (CVT = 0.57).

Conclusion

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.
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Metadata
Title
Generalized Multifactor Dimensionality Reduction (GMDR) Analysis of Drug-Metabolizing Enzyme-Encoding Gene Polymorphisms may Predict Treatment Outcomes in Indian Breast Cancer Patients
Authors
Gaurav Agarwal
Sonam Tulsyan
Punita Lal
Balraj Mittal
Publication date
01-07-2016
Publisher
Springer International Publishing
Published in
World Journal of Surgery / Issue 7/2016
Print ISSN: 0364-2313
Electronic ISSN: 1432-2323
DOI
https://doi.org/10.1007/s00268-015-3263-6

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