1. Breast cancer risks associated with missense variants in breast cancer susceptibility genes
- Author
-
Douglas F. Easton, Manuela Gago-Dominguez, Marina Bermisheva, Marike Gabrielson, Michael J. Madsen, Antoinette Hollestelle, Joe Dennis, Roger L. Milne, Per Hall, Andreas Hadjisavvas, Muriel A. Adank, Melissa C. Southey, Anthony Howell, Qin Wang, Thilo Dörk, Elza Khusnutdinova, Nicola J. Camp, Miguel de la Hoya, Pascal Guénel, Archie Campbell, Gord Glendon, Javier Benitez, Sgbcc Investigators, Manjeet K. Bolla, Jonine D. Figueroa, Paolo Radice, Maaike P.G. Vreeswijk, William G. Newman, Soo Hwang Teo, Anna Jakubowska, Paul D.P. Pharoah, Sara Margolin, Thomas U. Ahearn, Mitul Shah, Eric Hahnen, Matthias W. Beckmann, Dimitrios Mavroudis, Elinor J. Sawyer, Paolo Peterlongo, Artitaya Lophatananon, Melanie Gündert, Maria A. Loizidou, Xueling Sim, Irene L. Andrulis, Ignacio Briceño, Mehdi Manoochehri, Rita K. Schmutzler, Maija Suvanto, Sara Carvalho, Amanda B. Spurdle, Georgia Chenevix-Trench, Marjanka K. Schmidt, Graham G. Giles, Peter A. Fasching, Inge M. M. Lakeman, Vessela N. Kristensen, Leila Dorling, Annika Lindblom, Harald Surowy, Jan C. Oosterwijk, Diana Torres, Nur Aishah Taib, Xiaohong R. Yang, D. Gareth Evans, Päivi Auvinen, Heli Nevanlinna, Sabine Behrens, Arto Mannermaa, Christi J. van Asperen, Anna González-Neira, Ian Tomlinson, Thérèse Truong, Heiko Becher, J. Margriet Collée, Jenny Chang-Claude, Mikael Hartman, Michael T. Parsons, Jamie Allen, Henrik Flyger, Sue K. Park, Craig Luccarini, Sung-Won Kim, Stephan M. Heijl, Montserrat Garcia-Closas, Cristina Fortuno, Jingmei Li, Peter Devilee, Yon-Dschun Ko, Reiner Hoppe, Stig E. Bojesen, Regina Waltes, Frans B. L. Hogervorst, Alison M. Dunning, Kenneth Muir, Mikael Eriksson, Bas Vroling, Jan Lubinski, Jose E. Castelao, Stephen J. Chanock, Natalia Bogdanova, Anders Kvist, Michael Bremer, Emmanouil Saloustros, Kamila Czene, kConFab Investigators, Elaine F. Harkness, Audrey Y. Jung, and Ute Hamann
- Subjects
Genetics ,0303 health sciences ,PALB2 ,Biology ,medicine.disease ,Logistic regression ,Lower risk ,Breast cancer susceptibility genes ,03 medical and health sciences ,0302 clinical medicine ,Breast cancer ,030220 oncology & carcinogenesis ,medicine ,Missense mutation ,CHEK2 ,Gene ,030304 developmental biology - Abstract
BACKGROUNDProtein 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.METHODSCombining 59,639 breast cancer cases and 53,165 controls, we sampled training (80%) and validation (20%) sets to analyze rare missense variants in ATM (1,146 training variants), BRCA1 (644), BRCA2 (1,425), 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.RESULTSThe 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.CONCLUSIONSThese 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.
- Published
- 2021