Journal Article

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

Hartman, Mikael, Suvanto, Maija, Margolin, Sara, Waltes, Regina, Hogervorst, Frans B. L., Gündert, Melanie, Luccarini, Craig, Jung, Audrey, Sawyer, Elinor J., Figueroa, Jonine, Yang, Xiaohong R., Torres, Diana, Guénel, Pascal, Beckmann, Matthias W., Hall, Per, Lindblom, Annika, Mohd Taib, Nur Aishah, Khusnutdinova, Elza, Newman, William G., Bermisheva, Marina, Sim, Xueling, Jakubowska, Anna, Benitez, Javier, de la Hoya, Miguel, González-Neira, Anna, Harkness, Elaine F., Schmidt, Marjanka K., Castelao, Jose E., Surowy, Harald, Adank, Muriel A., Wang, Qin, García-Closas, Montserrat, Giles, Graham G., Andrulis, Irene L., Vreeswijk, Maaike P. G., Bogdanova, Natalia V., Lubiński, Jan, van Asperen, Christi J., Glendon, Gord, Nevanlinna, Heli, Kvist, Anders, Collée, J. Margriet, Schmutzler, Rita K., Czene, Kamila, Devilee, Peter, Spurdle, Amanda B., Gabrielson, Marike, Camp, Nicola J., Tomlinson, Ian, Howell, Anthony, Flyger, Henrik, Li, Jingmei, Bolla, Manjeet K., Carvalho, Sara, Ahearn, Thomas U., Southey, Melissa C., Hahnen, Eric, Bremer, Michael, Hamann, Ute, Eriksson, Mikael, Manoochehri, Mehdi, Dennis, Joe

2022

Overview

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.

• Publisher: BioMed Central,BioMed Central Ltd,Springer Nature B.V,BMC
• Subject: Algorithms / Annotations / Basic Medicine / Bioinformatics / Biomedical and Life Sciences / Biomedicine / BRCA1 protein / BRCA2 protein / Breast cancer / Breast Neoplasms - genetics / Cancer / Cancer and Oncology / Cancer och onkologi / Cancer Research / Case-Control Studies / Clinical Medicine / Consortia / Datasets / Disease susceptibility / Epidemiology / Family medical history / Female / Genes / Genetic aspects / Genetic epidemiology / Genetic Predisposition to Disease / Genetics / Health aspects / Human Genetics / Humans / Klinisk medicin / Life Sciences / Medical and Health Sciences / Medical Genetics and Genomics (including Gene Therapy) / Medicin och hälsovetenskap / Medicine/Public Health / Medicinsk genetik och genomik (Här ingår: Genterapi) / Medicinska och farmaceutiska grundvetenskaper / Metabolomics / Missense variants / Mutation, Missense / Oncology, Experimental / Population / Prediction models / Proteins / Quality control / Regression analysis / Risk factors / Risk prediction / Systems Biology