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

Media type: E-Article
Title: Breast cancer risks associated with missense variants in breast cancer susceptibility genes
Contributor: 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; [...]
imprint: Springer Science and Business Media LLC, 2022
Published in: Genome Medicine
Language: English
DOI: 10.1186/s13073-022-01052-8
ISSN: 1756-994X
Origination:
Footnote:
Description: <jats:title>Abstract</jats:title><jats:sec> <jats:title>Background</jats:title> <jats:p>Protein truncating variants in <jats:italic>ATM</jats:italic>, <jats:italic>BRCA1</jats:italic>, <jats:italic>BRCA2</jats:italic>, <jats:italic>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>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 <jats:italic>ATM</jats:italic> (1146 training variants), <jats:italic>BRCA1</jats:italic> (644), <jats:italic>BRCA2</jats:italic> (1425),<jats:italic> CHEK2</jats:italic> (325), and <jats:italic>PALB2</jats:italic> (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.</jats:p> </jats:sec><jats:sec> <jats:title>Results</jats:title> <jats:p>The most predictive in silico algorithms were Helix (<jats:italic>BRCA1</jats:italic>, <jats:italic>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</jats:italic>, <jats:italic>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>
Access State: Open Access

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