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

J. Margriet Collée, Anna Jakubowska, Henrik Flyger, Elinor J. Sawyer, Marjanka K. Schmidt, Paolo Peterlongo, Ian Tomlinson, Michael J. Madsen, Xueling Sim, Maija Suvanto, Jamie Allen, Antoinette Hollestelle, Emmanouil Saloustros, Ute Hamann, Graham G. Giles, Heiko Becher, Pascal Guénel, D. Gareth Evans, Reiner Hoppe, Päivi Auvinen, Sgbcc Investigators, Arto Mannermaa, Regina Waltes, kConFab Investigators, Jan Lubinski, Douglas F. Easton, Maria A. Loizidou, Maaike P.G. Vreeswijk, Manjeet K. Bolla, Diana Torres, Sabine Behrens, Per Hall, Paul D.P. Pharoah, Kenneth Muir, Javier Benitez, Marina Bermisheva, Marike Gabrielson, Audrey Y. Jung, Peter A. Fasching, Frans B. L. Hogervorst, Thilo Dörk, Artitaya Lophatananon, Nur Aishah Taib, Michael T. Parsons, Anders Kvist, Matthias W. Beckmann, Cristina Fortuno, Montserrat Garcia-Closas, Mehdi Manoochehri, Alison M. Dunning, Nicola J. Camp, Jenny Chang-Claude, Elza Khusnutdinova, Melanie Gündert, Mikael Eriksson, Xiaohong R. Yang, Rita K. Schmutzler, Stephan M. Heijl, Sung-Won Kim, Jonine D. Figueroa, Leila Dorling, Bas Vroling, Sue K. Park, Yon-Dschun Ko, Michael Bremer, Elaine F. Harkness, Natalia Bogdanova, Roger L. Milne, Sara Carvalho, Ignacio Briceño, Inge M. M. Lakeman, Sara Margolin, Paolo Radice, Qin Wang, Jose E. Castelao, Eric Hahnen, Jan C. Oosterwijk, Peter Devilee, Jingmei Li, Kamila Czene, Irene L. Andrulis, William G. Newman, Dimitrios Mavroudis, Melissa C. Southey, Manuela Gago-Dominguez, Thomas U. Ahearn, Stig E. Bojesen, Harald Surowy, Anna González-Neira, Annika Lindblom, Christi J. van Asperen, Georgia Chenevix-Trench, Miguel de la Hoya, Craig Luccarini, Mikael Hartman, Andreas Hadjisavvas, Archie Campbell, Soo Hwang Teo, Amanda B. Spurdle, Anthony Howell, Vessela N. Kristensen, Joe Dennis, Heli Nevanlinna, Mitul Shah, Thérèse Truong, Muriel A. Adank, Gord Glendon, Stephen J. Chanock

doi:10.17863/cam.85841

<jats:title>Abstract</jats:title><jats:sec><jats:title>BACKGROUND</jats:title><jats:p>Protein truncating variants in<jats:italic>ATM, BRCA1, BRCA2, CHEK2</jats:italic>and<jats:italic>PALB2</jats:italic>are associated with increased breast cancer risk, but risks associated with missense variants in these genes are uncertain.</jats:p></jats:sec><jats:sec><jats:title>METHODS</jats:title><jats:p>Combining 59,639 breast cancer cases and 53,165 controls, we sampled training (80%) and validation (20%) sets to analyze rare missense variants in<jats:italic>ATM</jats:italic>(1,146 training variants),<jats:italic>BRCA1</jats:italic>(644),<jats:italic>BRCA2</jats:italic>(1,425),<jats:italic>CHEK2</jats:italic>(325) and<jats:italic>PALB2</jats:italic>(472). We evaluated breast cancer risks according to five<jats:italic>in-silico</jats:italic>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.</jats:p></jats:sec><jats:sec><jats:title>RESULTS</jats:title><jats:p>The most predictive<jats:italic>in-silico</jats:italic>algorithms were Helix (<jats:italic>BRCA1, BRCA2</jats:italic>and<jats:italic>CHEK2)</jats:italic>and CADD (<jats:italic>ATM</jats:italic>). Increased risks appeared restricted to functional protein domains for<jats:italic>ATM</jats:italic>(FAT and PIK domains) and<jats:italic>BRCA1</jats:italic>(RING and BRCT domains). For<jats:italic>ATM, BRCA1</jats:italic>and<jats:italic>BRCA2</jats:italic>, 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<jats:italic>CHEK2</jats:italic>, 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<jats:italic>CHEK2</jats:italic>protein truncating variants. There was little evidence for an association with risk for missense variants in<jats:italic>PALB2</jats:italic>. The best fitting models were well calibrated in the validation set.</jats:p></jats:sec><jats:sec><jats:title>CONCLUSIONS</jats:title><jats:p>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.</jats:p></jats:sec>

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

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