Breast Cancer Risk Genes - Association Analysis in More than 113,000 Women
- PMID: 33471991
- PMCID: PMC7611105
- DOI: 10.1056/NEJMoa1913948
Breast Cancer Risk Genes - Association Analysis in More than 113,000 Women
Abstract
Background: Genetic testing for breast cancer susceptibility is widely used, but for many genes, evidence of an association with breast cancer is weak, underlying risk estimates are imprecise, and reliable subtype-specific risk estimates are lacking.
Methods: We used a panel of 34 putative susceptibility genes to perform sequencing on samples from 60,466 women with breast cancer and 53,461 controls. In separate analyses for protein-truncating variants and rare missense variants in these genes, we estimated odds ratios for breast cancer overall and tumor subtypes. We evaluated missense-variant associations according to domain and classification of pathogenicity.
Results: Protein-truncating variants in 5 genes (ATM, BRCA1, BRCA2, CHEK2, and PALB2) were associated with a risk of breast cancer overall with a P value of less than 0.0001. Protein-truncating variants in 4 other genes (BARD1, RAD51C, RAD51D, and TP53) were associated with a risk of breast cancer overall with a P value of less than 0.05 and a Bayesian false-discovery probability of less than 0.05. For protein-truncating variants in 19 of the remaining 25 genes, the upper limit of the 95% confidence interval of the odds ratio for breast cancer overall was less than 2.0. For protein-truncating variants in ATM and CHEK2, odds ratios were higher for estrogen receptor (ER)-positive disease than for ER-negative disease; for protein-truncating variants in BARD1, BRCA1, BRCA2, PALB2, RAD51C, and RAD51D, odds ratios were higher for ER-negative disease than for ER-positive disease. Rare missense variants (in aggregate) in ATM, CHEK2, and TP53 were associated with a risk of breast cancer overall with a P value of less than 0.001. For BRCA1, BRCA2, and TP53, missense variants (in aggregate) that would be classified as pathogenic according to standard criteria were associated with a risk of breast cancer overall, with the risk being similar to that of protein-truncating variants.
Conclusions: The results of this study define the genes that are most clinically useful for inclusion on panels for the prediction of breast cancer risk, as well as provide estimates of the risks associated with protein-truncating variants, to guide genetic counseling. (Funded by European Union Horizon 2020 programs and others.).
Copyright © 2021 Massachusetts Medical Society.
Figures
Comment in
-
Which Genes for Hereditary Breast Cancer?N Engl J Med. 2021 Feb 4;384(5):471-473. doi: 10.1056/NEJMe2035083. Epub 2021 Jan 20. N Engl J Med. 2021. PMID: 33471975 No abstract available.
-
The ten genes for breast (and ovarian) cancer susceptibility.Nat Rev Clin Oncol. 2021 May;18(5):259-260. doi: 10.1038/s41571-021-00491-3. Nat Rev Clin Oncol. 2021. PMID: 33692540 No abstract available.
Similar articles
-
Pathology of Tumors Associated With Pathogenic Germline Variants in 9 Breast Cancer Susceptibility Genes.JAMA Oncol. 2022 Mar 1;8(3):e216744. doi: 10.1001/jamaoncol.2021.6744. Epub 2022 Mar 17. JAMA Oncol. 2022. PMID: 35084436 Free PMC article.
-
A Population-Based Study of Genes Previously Implicated in Breast Cancer.N Engl J Med. 2021 Feb 4;384(5):440-451. doi: 10.1056/NEJMoa2005936. Epub 2021 Jan 20. N Engl J Med. 2021. PMID: 33471974 Free PMC article.
-
Gene panel testing of 5589 BRCA1/2-negative index patients with breast cancer in a routine diagnostic setting: results of the German Consortium for Hereditary Breast and Ovarian Cancer.Cancer Med. 2018 Apr;7(4):1349-1358. doi: 10.1002/cam4.1376. Epub 2018 Mar 9. Cancer Med. 2018. PMID: 29522266 Free PMC article.
-
Moderate penetrance genes complicate genetic testing for breast cancer diagnosis: ATM, CHEK2, BARD1 and RAD51D.Breast. 2022 Oct;65:32-40. doi: 10.1016/j.breast.2022.06.003. Epub 2022 Jun 18. Breast. 2022. PMID: 35772246 Free PMC article. Review.
-
Recommendations for Preventive Care for Women with Rare Genetic Cause of Breast and Ovarian Cancer.Klin Onkol. 2019 Summer;32(Supplementum2):6-13. doi: 10.14735/amko2019S6. Klin Onkol. 2019. PMID: 31409076 Review. English.
Cited by
-
Radiomic Machine Learning in Invasive Ductal Breast Cancer: Prediction of Ki-67 Expression Level Based on Radiomics of DCE-MRI.Technol Cancer Res Treat. 2024 Jan-Dec;23:15330338241288751. doi: 10.1177/15330338241288751. Technol Cancer Res Treat. 2024. PMID: 39431304 Free PMC article.
-
Quantitative Assessment of PALB2 and BRIP1 Genes Expression in the Breast Cancer Cell Line under the Influence of Tamoxifen.Galen Med J. 2023 Dec 26;12:1-8. doi: 10.31661/gmj.v12i0.2483. eCollection 2023. Galen Med J. 2023. PMID: 39430039 Free PMC article.
-
Phenotypic evaluation of deep learning models for classifying germline variant pathogenicity.NPJ Precis Oncol. 2024 Oct 19;8(1):235. doi: 10.1038/s41698-024-00710-x. NPJ Precis Oncol. 2024. PMID: 39427061 Free PMC article.
-
Vitamin D receptor is associated with prognostic characteristics of breast cancer after neoadjuvant chemotherapy-an observational study.Front Oncol. 2024 Oct 1;14:1458124. doi: 10.3389/fonc.2024.1458124. eCollection 2024. Front Oncol. 2024. PMID: 39411136 Free PMC article.
-
Understanding genetic variations associated with familial breast cancer.World J Surg Oncol. 2024 Oct 10;22(1):271. doi: 10.1186/s12957-024-03553-9. World J Surg Oncol. 2024. PMID: 39390525 Free PMC article. Review.
References
-
- University of Cambridge and Cancer Research UK. Breast Cancer Association Consortium. 2020 http://bcac.ccge.medschl.cam.ac.uk.
-
- Antoniou AC, Easton DF. Polygenic inheritance of breast cancer: implications for design of association studies. Genet Epidemiol. 2003;25:190–202. - PubMed
Publication types
MeSH terms
Grants and funding
LinkOut - more resources
Full Text Sources
Other Literature Sources
Medical
Research Materials
Miscellaneous