Abstract
Background Chemotherapeutic drug clinical outcomes are genetically determined as there is a large heterogeneity in the response to, and toxicity of, chemotherapeutic agents in breast cancer patients. Polymorphisms in genes encoding Phase I – Cytochrome P450 (CYP450) and NAD(P)H dehydrogenase quinine (NQO1); phase II glutathioneStransferase (GST) and methylene tetra hydrofolate reductase (MTHFR); phase IIIp-glycoprotein (ABCB1) and solute transporter (SLC22A16) drug metabolizing enzymes can possibly predict clinical 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 drug metabolizing enzymes in predicting response and toxicity in breast cancer patients, using multi-analytical approaches.
Highlights
Chemotherapeutic drug clinical outcomes are genetically determined as there is a large heterogeneity in the response to, and toxicity of, chemotherapeutic agents in breast cancer patients
Materials & methods Two hundred and four North Indian cases of histology proven invasive breast carcinoma treated at the our institute were genotyped for 17 polymorphisms by Polymerase Chain Reaction (PCR) or PCR-Restriction Fragment Length Polymorphism (RFLP) or Taqman allelic discrimination assay
Heterozygous (CT) and variant (TT) genotype of ABCB1 1236C>T polymorphism was found to be significant with breast cancer response to NACT
Summary
Chemotherapeutic drug clinical outcomes are genetically determined as there is a large heterogeneity in the response to, and toxicity of, chemotherapeutic agents in breast cancer patients. Polymorphisms in genes encoding Phase I – Cytochrome P450 (CYP450) and NAD(P)H dehydrogenase quinine (NQO1); phase II - glutathioneStransferase (GST) and methylene tetra hydrofolate reductase (MTHFR); phase III- p-glycoprotein (ABCB1) and solute transporter (SLC22A16) drug metabolizing enzymes can possibly predict clinical 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 drug metabolizing enzymes in predicting response and toxicity in breast cancer patients, using multi-analytical approaches
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