Common germline polymorphisms associated with breast cancer-specific survival

Abstract

Introduction: Previous studies have identified common germline variants nominally associated with breast cancer survival. These associations have not been widely replicated in further studies. The purpose of this study was to evaluate the association of previously reported SNPs with breast cancer-specific survival using data from a pooled analysis of eight breast cancer survival genome-wide association studies (GWAS) from the Breast Cancer Association Consortium.

Methods: A literature review was conducted of all previously published associations between common germline variants and three survival outcomes: breast cancer-specific survival, overall survival and disease-free survival. All associations that reached the nominal significance level of P value <0.05 were included. Single nucleotide polymorphisms that had been previously reported as nominally associated with at least one survival outcome were evaluated in the pooled analysis of over 37,000 breast cancer cases for association with breast cancer-specific survival. Previous associations were evaluated using a one-sided test based on the reported direction of effect.

Results: Fifty-six variants from 45 previous publications were evaluated in the meta-analysis. Fifty-four of these were evaluated in the full set of 37,954 breast cancer cases with 2,900 events and the two additional variants were evaluated in a reduced sample size of 30,000 samples in order to ensure independence from the previously published studies. Five variants reached nominal significance (P <0.05) in the pooled GWAS data compared to 2.8 expected under the null hypothesis. Seven additional variants were associated (P <0.05) with ER-positive disease.

Conclusions: Although no variants reached genome-wide significance (P <5 x 10(-8)), these results suggest that there is some evidence of association between candidate common germline variants and breast cancer prognosis. Larger studies from multinational collaborations are necessary to increase the power to detect associations, between common variants and prognosis, at more stringent significance levels.

Figure 1

Quantile-quantile plot of results from look-up of previously reported associations in genome-wide association studies. Tests were one-sided with direction assumed from previous association.

Figure 2

Power (%) to detect true associations with survival time across a range of minor allele frequencies and numbers of events. (a) Power (%) to detect true associations with survival time over a range of effect sizes at increasing orders of significance given a minor allele frequency of 0.3 and 2,900 events. We used an imputation r² = 0.8 to account for suboptimal imputation. (b) Power (%) to detect true associations with survival time for increasing numbers of events, at increasing orders of significance, given a minor allele frequency of 0.3 and an effect size of 1.1. We used an imputation r² = 0.8 to account for suboptimal imputation.