BACKGROUND AND OBJECTIVE: Chemotherapeutic drug treatment outcomes are genetically determined. Polymorphisms in genes encoding phase II drug metabolizing enzyme glutathione-S-transferase (GST) can possibly predict treatment outcomes, and can be of prognostic significance in breast cancer patients. The aim of this study was to determine the role of genetic variations in GST in predicting response to, and toxicity of, anthracycline-based chemotherapy in breast cancer patients. METHOD: Two hundred and seven patients treated with anthracycline-based chemotherapy were genotyped for GSTM1 and GSTT1 deletion polymorphisms, and GSTP1 Ile105Val (rs1695), by polymerase chain reaction (PCR)/ PCR-restriction fragment length polymorphism (RFLP). Genetic variations were correlated with tumor response to neo-adjuvant chemotherapy (NACT) in 100 patients, and with chemo-toxicity in 207 who received adjuvant chemotherapy or NACT, using Chi-square and logistic regression. Higher order gene-gene interactions with treatment outcomes were characterized by multifactor dimensionality reduction (MDR) analysis. RESULTS: In single-locus analysis, Ile/Val and Ile/Val+Val/Val genotypes of the GSTP1 Ile105Val (rs1695) polymorphism reached statistical significance with grade 2-4 anemia (P=0.019, P=0.027). On performing gene-gene interaction analysis, GSTM1 null-GSTP1 Ile/Val was significantly associated with response to NACT (P=0.032). On evaluating higher order gene-gene interaction models by MDR analysis, GSTM1 and GSTP1 Ile105Val; GSTM1 and GSTT1; and GSTT1 and GSTP1 Ile105Val showed significant association with treatment response, grade 2-4 anemia, and dose delay/reduction due to neutropenia (P=0.046, P=0.027, P=0.026), respectively. CONCLUSION: Multi-analytical strategies may serve as a better tool for characterization of pharmacogenetic-based breast cancer treatment outcomes.
BACKGROUND AND OBJECTIVE: Chemotherapeutic drug treatment outcomes are genetically determined. Polymorphisms in genes encoding phase II drug metabolizing enzyme glutathione-S-transferase (GST) can possibly predict treatment outcomes, and can be of prognostic significance in breast cancerpatients. The aim of this study was to determine the role of genetic variations in GST in predicting response to, and toxicity of, anthracycline-based chemotherapy in breast cancerpatients. METHOD: Two hundred and seven patients treated with anthracycline-based chemotherapy were genotyped for GSTM1 and GSTT1 deletion polymorphisms, and GSTP1Ile105Val (rs1695), by polymerase chain reaction (PCR)/ PCR-restriction fragment length polymorphism (RFLP). Genetic variations were correlated with tumor response to neo-adjuvant chemotherapy (NACT) in 100 patients, and with chemo-toxicity in 207 who received adjuvant chemotherapy or NACT, using Chi-square and logistic regression. Higher order gene-gene interactions with treatment outcomes were characterized by multifactor dimensionality reduction (MDR) analysis. RESULTS: In single-locus analysis, Ile/Val and Ile/Val+Val/Val genotypes of the GSTP1Ile105Val (rs1695) polymorphism reached statistical significance with grade 2-4 anemia (P=0.019, P=0.027). On performing gene-gene interaction analysis, GSTM1 null-GSTP1 Ile/Val was significantly associated with response to NACT (P=0.032). On evaluating higher order gene-gene interaction models by MDR analysis, GSTM1 and GSTP1Ile105Val; GSTM1 and GSTT1; and GSTT1 and GSTP1Ile105Val showed significant association with treatment response, grade 2-4 anemia, and dose delay/reduction due to neutropenia (P=0.046, P=0.027, P=0.026), respectively. CONCLUSION: Multi-analytical strategies may serve as a better tool for characterization of pharmacogenetic-based breast cancer treatment outcomes.
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Authors: Marc Jan Bonder; Silva Kasela; Mart Kals; Riin Tamm; Kaie Lokk; Isabel Barragan; Wim A Buurman; Patrick Deelen; Jan-Willem Greve; Maxim Ivanov; Sander S Rensen; Jana V van Vliet-Ostaptchouk; Marcel G Wolfs; Jingyuan Fu; Marten H Hofker; Cisca Wijmenga; Alexandra Zhernakova; Magnus Ingelman-Sundberg; Lude Franke; Lili Milani Journal: BMC Genomics Date: 2014-10-04 Impact factor: 3.969