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Common breast cancer risk loci predispose to distinct tumor subtypes

Published on Aug 15, 2019in bioRxiv
· DOI :10.1101/733402
Thomas U. Ahearn8
Estimated H-index: 8
(NIH: National Institutes of Health),
Thomas U. Ahearn8
Estimated H-index: 8
(NIH: National Institutes of Health)
+ 155 AuthorsNilanjan Chatterjee77
Estimated H-index: 77
Sources
Abstract
Background: Genome-wide association studies have identified over 170 common breast cancer susceptibility loci, many of them with differential associations by estrogen receptor (ER). How these variants are related to other tumor features is unclear. Methods: Analyses included 106,571 invasive breast cancer cases and 95,762 controls of European ancestry with data on 178 genotyped or imputed single nucleotide polymorphisms (SNPs). We used two-stage polytomous logistic regression models to evaluate SNPs in relation to multiple tumor features (ER, progesterone receptor (PR), human epidermal growth factor receptor 2 (HER2) and grade) adjusting for each other, and to intrinsic-like subtypes. Results: Nearly half of the SNPs (85 of 178) were associated with at least one tumor feature (false discovery rate <5%). Case-case comparisons identified ER and grade as the most common heterogeneity sources, followed by PR and HER2. Case-control comparisons among these 85 SNPs with intrinsic-like subtypes identified 65 SNPs strongly or exclusively associated at P<0.05 with luminal-like subtypes, 5 SNPs associated with all subtypes at differing strengths, and 15 SNPs primarily associated with non-luminal tumors, especially triple-negative (TN) disease. The I157T CHEK2 variant (rs17879961) was associated in opposite directions with luminal A-like (odds ratio (OR; 95% confidence interval (CI))=1.44 (1.31 to 1.59); P=9.26x10-14) and TN (OR (95% CI)=0.61 (0.47 to 0.80); P=2.55x10-4). Conclusion: About half of the breast cancer susceptibility loci discovered in overall and ER-specific risk analyses have differential associations with clinical tumor features. These findings provide insights into the genetic predisposition of breast cancer subtypes and can inform subtype-specific risk prediction.
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