Breast cancer risks associated with missense variants in breast cancer susceptibility genes

Abstract

Background: Protein truncating variants in ATM, BRCA1, BRCA2, CHEK2, and PALB2 are associated with increased breast cancer risk, but risks associated with missense variants in these genes are uncertain.

Methods: We analyzed data on 59,639 breast cancer cases and 53,165 controls from studies participating in the Breast Cancer Association Consortium BRIDGES project. We sampled training (80%) and validation (20%) sets to analyze rare missense variants in ATM (1146 training variants), BRCA1 (644), BRCA2 (1425), CHEK2 (325), and PALB2 (472). We evaluated breast cancer risks according to five in silico prediction-of-deleteriousness algorithms, functional protein domain, and frequency, using logistic regression models and also mixture models in which a subset of variants was assumed to be risk-associated.

Results: The most predictive in silico algorithms were Helix (BRCA1, BRCA2 and CHEK2) and CADD (ATM). Increased risks appeared restricted to functional protein domains for ATM (FAT and PIK domains) and BRCA1 (RING and BRCT domains). For ATM, BRCA1, and BRCA2, data were compatible with small subsets (approximately 7%, 2%, and 0.6%, respectively) of rare missense variants giving similar risk to those of protein truncating variants in the same gene. For CHEK2, data were more consistent with a large fraction (approximately 60%) of rare missense variants giving a lower risk (OR 1.75, 95% CI (1.47-2.08)) than CHEK2 protein truncating variants. There was little evidence for an association with risk for missense variants in PALB2. The best fitting models were well calibrated in the validation set.

Conclusions: These results will inform risk prediction models and the selection of candidate variants for functional assays and could contribute to the clinical reporting of gene panel testing for breast cancer susceptibility.

Keywords: Breast cancer; Genetic epidemiology; Missense variants; Risk prediction.

Fig. 1

Odds ratios and alpha estimates for each of five genes in population training samples. A ATM. Odds ratios for breast cancer risk from logistic regression models. Alpha is the estimated proportion of risk associated variants from mixture models, based on variants in control samples. ATM risk categories: variants lying within the FAT or PI3K/PI4K protein domains with CADD score in the fifth quintile (FAT/PIK + CADD5); variants lying within the FAT or PI3K/PI4K protein domains with CADD score in any of the first four quintiles (FAT/PIK + CADD1-4); variants lying outside the FAT and PI3K/PI4K protein domains (Outside FAT/PIK). B BRCA1. Odds ratios for breast cancer risk from logistic regression models. Alpha is the estimated proportion of risk associated variants from mixture models, based on variants in control samples. BRCA1 risk categories: variants lying within the RING or BRCT domains with a high Helix score (RING/BRCT + Helix-high); variants lying with the RING or BRCT domains with a low Helix score (RING/BRCT + Helix-low); variants lying outside the RING and BRCT domains (Outside RING/BRCT). C BRCA2. Odds ratios for breast cancer risk from logistic regression models. Alpha is the estimated proportion of risk associated variants from mixture models, based on variants in control samples. BRCA2 risk categories: variants with a high Helix score (Helix-high); variants with a low Helix score (Helix-low). D CHEK2. Odds ratios for breast cancer risk from logistic regression models. Alpha is the estimated proportion of risk associated variants from mixture models, based on variants in control samples. CHEK2 risk categories: variants with a high Helix score (Helix-high); variants with a low Helix score (Helix-low). E PALB2. Odds ratios for breast cancer risk from logistic regression models. Alpha is the estimated proportion of risk associated variants from mixture models, based on variants in control samples. PALB2 risk categories: carriers of any missense variant (Carriers)

Fig. 2

Case and control carriers across all samples for each observed missense variant by gene. A ATM. ATM risk categories: variants lying within the FAT or PI3K/PI4K protein domains with CADD score in fifth quintile (FAT/PIK + CADD5); variants lying within the FAT or PI3K/PI4K protein domains with CADD score in any of first four quintiles (FAT/PIK + CADD1-4); variants lying outside the FAT and PI3K/PI4K protein domains (Outside FAT/PIK). B BRCA1. BRCA1 risk categories: variants lying within the RING or BRCT domains with a high Helix score (RING/BRCT + Helix-high); variants lying with the RING or BRCT domains with a low Helix score (RING/BRCT + Helix-low); variants lying outside the RING and BRCT domains (Outside RING/BRCT). C BRCA2. BRCA2 risk categories: variants with a high Helix score (Helix-high); variants with a low Helix score (Helix-low). D CHEK2. CHEK2 risk categories: variants with a high Helix score (Helix-high); variants with a low Helix score (Helix-low). E PALB2. PALB2 risk categories: carriers of any missense variant (Carriers)

Fig. 3

Breast cancer risk estimates from composite gene model in validation samples. Black marks indicate corresponding ORs from training models. Risk categories: ATM FAT/PIK + CADD5: ATM variants lying within the FAT or PI3K/PI4K protein domains with CADD score in fifth quintile; ATM FAT/PIK + CADD1-4: ATM variants lying within the FAT or PI3K/PI4K protein domains with CADD score in any of first four quintiles; ATM outside FAT/PIK: variants lying outside the FAT and PI3K/PI4K protein domains; BRCA1 RING/BRCT + Helix-high: BRCA1 variants lying within the RING or BRCT domains with a high Helix score; BRCA1 RING/BRCT + Helix-low: BRCA1 variants lying with the RING or BRCT domains with a low Helix score; BRCA1 outside RING/BRCT: BRCA1 variants lying outside the RING and BRCT domains; BRCA2 Helix-high: BRCA2 variants with a high Helix score; BRCA2 Helix-low: BRCA2 variants with a low Helix score; CHEK2 Helix-high: CHEK2 variants with a high Helix score; CHEK2 Helix-low: CHEK2 variants with a low Helix score; PALB2 carriers: carriers of any missense variant in PALB2