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.
| Original language | English |
|---|---|
| Article number | 51 |
| Journal | Genome Medicine |
| Volume | 14 |
| Number of pages | 17 |
| ISSN | 1756-994X |
| DOIs | |
| Publication status | Published - 2022 |
Bibliographical note
Publisher Copyright:© 2022, The Author(s).
Keywords
- Breast cancer
- Genetic epidemiology
- Missense variants
- Risk prediction
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Breast cancer risks associated with missense variants in breast cancer susceptibility genes. / Dorling, Leila; Carvalho, Sara; Allen, Jamie; Parsons, Michael T; Fortuno, Cristina; González-Neira, Anna; Heijl, Stephan M.; Adank, Muriel A.; Ahearn, Thomas U; Andrulis, Irene L.; Auvinen, Päivi; Becher, Heiko; Beckmann, Matthias W.; Behrens, Sabine; Bermisheva, Marina; Bogdanova, Natalia V.; Bojesen, Stig E.; Bolla, Manjeet K.; Bremer, Michael; Briceno, Ignacio; Camp, Nicola J; Campbell, Archie; Castelao, Jose E.; Chang-Claude, Jenny; Chanock, Stephen J.; Chenevix-Trench, Georgia; Collée, J Margriet; Czene, Kamila; Dennis, Joe; Dörk, Thilo; Eriksson, Mikael; Evans, D. Gareth; Fasching, Peter A.; Figueroa, Jonine; Flyger, Henrik; Gabrielson, Marike; Gago-Dominguez, Manuela; García-Closas, Montserrat; Giles, Graham G; Glendon, Gord; Guénel, Pascal; Gündert, Melanie; Hadjisavvas, Andreas; Hahnen, Eric; Hall, Per; Hamann, Ute; Harkness, Elaine F.; Hartman, Mikael; Hogervorst, Frans B L; Hollestelle, Antoinette; Hoppe, Reiner; Howell, Anthony; Jakubowska, Anna; Jung, Audrey; Khusnutdinova, Elza; Kim, Sung-Won; Ko, Yon-Dschun; Kristensen, Vessela N; Lakeman, Inge M. M.; Li, Jingmei; Lindblom, Annika; Loizidou, Maria A.; Lophatananon, Artitaya; Lubiński, Jan; Luccarini, Craig; Madsen, Michael J.; Mannermaa, Arto; Manoochehri, Mehdi; Margolin, Sara; Mavroudis, Dimitrios; Milne, Roger L; Taib, Nur Aishah Mohd; Muir, Kenneth; Nevanlinna, Heli; Newman, William G.; Oosterwijk, Jan C; Park, Sue K; Peterlongo, Paolo; Radice, Paolo; Saloustros, Emmanouil; Sawyer, Elinor J.; Schmutzler, Rita K.; Shah, Mitul; Sim, Xueling; Southey, Melissa C; Surowy, Harald; Suvanto, Maija; Tomlinson, Ian; Torres, Diana; Truong, Thérèse; van Asperen, Christi J; Waltes, Regina; Wang, Qin; Yang, Xiaohong R; Pharoah, Paul D P; Schmidt, Marjanka K.; Benitez, Javier; Vroling, Bas; Dunning, Alison M.; Teo, Soo Hwang; Kvist, Anders; de la Hoya, Miguel; Devilee, Peter; Spurdle, Amanda B; Vreeswijk, Maaike P G; Easton, Douglas F.
In: Genome Medicine, Vol. 14, 51, 2022.Research output: Contribution to journal › Journal article › Research › peer-review
}
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 -