TY - JOUR

T1 - Breast cancer risks associated with missense variants in breast cancer susceptibility genes

AU - Dorling, Leila

AU - Carvalho, Sara

AU - Allen, Jamie

AU - Parsons, Michael T

AU - Fortuno, Cristina

AU - González-Neira, Anna

AU - Heijl, Stephan M.

AU - Adank, Muriel A.

AU - Ahearn, Thomas U

AU - Andrulis, Irene L.

AU - Auvinen, Päivi

AU - Becher, Heiko

AU - Beckmann, Matthias W.

AU - Behrens, Sabine

AU - Bermisheva, Marina

AU - Bogdanova, Natalia V.

AU - Bojesen, Stig E.

AU - Bolla, Manjeet K.

AU - Bremer, Michael

AU - Briceno, Ignacio

AU - Camp, Nicola J

AU - Campbell, Archie

AU - Castelao, Jose E.

AU - Chang-Claude, Jenny

AU - Chanock, Stephen J.

AU - Chenevix-Trench, Georgia

AU - Collée, J Margriet

AU - Czene, Kamila

AU - Dennis, Joe

AU - Dörk, Thilo

AU - Eriksson, Mikael

AU - Evans, D. Gareth

AU - Fasching, Peter A.

AU - Figueroa, Jonine

AU - Flyger, Henrik

AU - Gabrielson, Marike

AU - Gago-Dominguez, Manuela

AU - García-Closas, Montserrat

AU - Giles, Graham G

AU - Glendon, Gord

AU - Guénel, Pascal

AU - Gündert, Melanie

AU - Hadjisavvas, Andreas

AU - Hahnen, Eric

AU - Hall, Per

AU - Hamann, Ute

AU - Harkness, Elaine F.

AU - Hartman, Mikael

AU - Hogervorst, Frans B L

AU - Hollestelle, Antoinette

AU - Hoppe, Reiner

AU - Howell, Anthony

AU - Jakubowska, Anna

AU - Jung, Audrey

AU - Khusnutdinova, Elza

AU - Kim, Sung-Won

AU - Ko, Yon-Dschun

AU - Kristensen, Vessela N

AU - Lakeman, Inge M. M.

AU - Li, Jingmei

AU - Lindblom, Annika

AU - Loizidou, Maria A.

AU - Lophatananon, Artitaya

AU - Lubiński, Jan

AU - Luccarini, Craig

AU - Madsen, Michael J.

AU - Mannermaa, Arto

AU - Manoochehri, Mehdi

AU - Margolin, Sara

AU - Mavroudis, Dimitrios

AU - Milne, Roger L

AU - Taib, Nur Aishah Mohd

AU - Muir, Kenneth

AU - Nevanlinna, Heli

AU - Newman, William G.

AU - Oosterwijk, Jan C

AU - Park, Sue K

AU - Peterlongo, Paolo

AU - Radice, Paolo

AU - Saloustros, Emmanouil

AU - Sawyer, Elinor J.

AU - Schmutzler, Rita K.

AU - Shah, Mitul

AU - Sim, Xueling

AU - Southey, Melissa C

AU - Surowy, Harald

AU - Suvanto, Maija

AU - Tomlinson, Ian

AU - Torres, Diana

AU - Truong, Thérèse

AU - van Asperen, Christi J

AU - Waltes, Regina

AU - Wang, Qin

AU - Yang, Xiaohong R

AU - Pharoah, Paul D P

AU - Schmidt, Marjanka K.

AU - Benitez, Javier

AU - Vroling, Bas

AU - Dunning, Alison M.

AU - Teo, Soo Hwang

AU - Kvist, Anders

AU - de la Hoya, Miguel

AU - Devilee, Peter

AU - Spurdle, Amanda B

AU - Vreeswijk, Maaike P G

AU - Easton, Douglas F.

N1 - Publisher Copyright: © 2022, The Author(s).

PY - 2022

Y1 - 2022

N2 - 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.

AB - 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.

KW - Breast cancer

KW - Genetic epidemiology

KW - Missense variants

KW - Risk prediction

U2 - 10.1186/s13073-022-01052-8

DO - 10.1186/s13073-022-01052-8

M3 - Journal article

C2 - 35585550

AN - SCOPUS:85130251315

VL - 14

JO - Genome Medicine

JF - Genome Medicine

SN - 1756-994X

M1 - 51

ER -