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 at <0.025.



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).



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.
Departments of Endocrine & Breast Surgery, Sanjay Gandhi Postgraduate Institute of Medical Sciences, Raebareli Road, Lucknow, 226014, India. gaurav@sgpgi.ac.in.
Gaurav
0000-0003-4194-2670
G
Agarwal
Genetics, Sanjay Gandhi Postgraduate Institute of Medical Sciences, Raebareli Road, Lucknow, 226014, India.
Sonam
S
Tulsyan
Radiation Oncology, Sanjay Gandhi Postgraduate Institute of Medical Sciences, Raebareli Road, Lucknow, 226014, India.
Punita
P
Lal
Genetics, Sanjay Gandhi Postgraduate Institute of Medical Sciences, Raebareli Road, Lucknow, 226014, India.
Balraj
B
Mittal
D018435:ATP Binding Cassette Transporter, Subfamily B / Q000235:genetics; D000328:Adult; D000368:Aged; D001943:Breast Neoplasms / Q000188:drug therapy* / Q000235:genetics; D017024:Chemotherapy, Adjuvant; D065731:Cytochrome P-450 CYP2C19 / Q000235:genetics; D005260:Female; D005838:Genotype; D006801:Humans; D016015:Logistic Models; D008875:Middle Aged; D058011:Multifactor Dimensionality Reduction; D020641:Polymorphism, Single Nucleotide
C513055:ABCB1 protein, human; D018435:ATP Binding Cassette Transporter, Subfamily B; C045793:CYP2C19 protein, human; D065731:Cytochrome P-450 CYP2C19