Analysis of Fcγ receptor IIIa and IIa polymorphisms: lack of correlation with outcome in trastuzumab-treated breast cancer patients.

PURPOSE: The mechanisms by which trastuzumab imparts clinical benefit remain incompletely understood. Antibody-dependent cellular cytotoxicity via interactions with Fcγ receptors (FcγR) on leukocytes may contribute to its antitumor effects. Single-nucleotide polymorphisms (SNP) in FCGR3A and FCGR2A genes lead to amino acid substitutions at positions 158 and 131, respectively, and affect binding of antibodies to FcγR such that 158V/V and 131H/H bind with highest affinity. This study aimed to determine whether high-affinity SNPs are associated with disease-free survival (DFS) among patients with HER2-positive nonmetastatic breast cancer. EXPERIMENTAL DESIGN: Genomic DNA was isolated from 1,286 patients enrolled in a trial of adjuvant trastuzumab-based chemotherapy. Genotyping was conducted using Sanger sequencing and Sequenom mass spectrometry. RESULTS: Patient samples (N = 1,189) were successfully genotyped for FCGR3A and 1,218 for FCGR2A. Compared with the overall results of the BCIRG006 study, in the subset of patients genotyped in this analysis, a less robust improvement in DFS was observed for the trastuzumab arms than control arm (HR, 0.842; P = 0.1925). When stratified for prognostic features, the HR in favor of trastuzumab was consistent with that of the overall study (HR, 0.74; P = 0.036). No correlation between DFS and FCGR3A/2A genotypes was seen for trastuzumab-treated patients (158V/V vs. V/F vs. F/F, P = 0.98; 131H/H vs. H/R vs. R/R, P = 0.76; 158V/V and/or 131H/H vs. others, P = 0.67). CONCLUSION: This analysis evaluating the association between FCGR3A/2A genotypes and trastuzumab efficacy in HER2-positive breast cancer did not show a correlation between FCGR3A-V/F and FCGR2A-H/R SNPs and DFS in patients treated with trastuzumab.

Predictive models for breast cancer susceptibility from multiple single nucleotide polymorphisms.

Hereditary predisposition and causative environmental exposures have long been recognized in human malignancies. In most instances, cancer cases occur sporadically, suggesting that environmental influences are critical in determining cancer risk. To test the influence of genetic polymorphisms on breast cancer risk, we have measured 98 single nucleotide polymorphisms (SNPs) distributed over 45 genes of potential relevance to breast cancer etiology in 174 patients and have compared these with matched normal controls. Using machine learning techniques such as support vector machines (SVMs), decision trees, and naïve Bayes, we identified a subset of three SNPs as key discriminators between breast cancer and controls. The SVMs performed maximally among predictive models, achieving 69% predictive power in distinguishing between the two groups, compared with a 50% baseline predictive power obtained from the data after repeated random permutation of class labels (individuals with cancer or controls). However, the simpler naïve Bayes model as well as the decision tree model performed quite similarly to the SVM. The three SNP sites most useful in this model were (a) the +4536T/C site of the aldosterone synthase gene CYP11B2 at amino acid residue 386 Val/Ala (T/C) (rs4541); (b) the +4328C/G site of the aryl hydrocarbon hydroxylase CYP1B1 at amino acid residue 293 Leu/Val (C/G) (rs5292); and (c) the +4449C/T site of the transcription factor BCL6 at amino acid 387 Asp/Asp (rs1056932). No single SNP site on its own could achieve more than 60% in predictive accuracy. We have shown that multiple SNP sites from different genes over distant parts of the genome are better at identifying breast cancer patients than any one SNP alone. As high-throughput technology for SNPs improves and as more SNPs are identified, it is likely that much higher predictive accuracy will be achieved and a useful clinical tool developed.