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Large-scale meta-analysis of mutations identified in panels of breast/ovarian cancer-related genes - Providing evidence of cancer predisposition genes

Journal

GYNECOLOGIC ONCOLOGY
Volume 153, Issue 2, Pages 452-462

Publisher

ACADEMIC PRESS INC ELSEVIER SCIENCE
DOI: 10.1016/j.ygyno.2019.01.027

Keywords

Breast cancer; Ovarian cancer; Multi-gene panel testing; Meta-analysis; Cancer predisposition genes

Funding

  1. Polish National Science Centre [NCN 2015/17/B/NZ2/01182, 2016/22/A/NZ2/00184, 2015/19/N/NZ5/02247]

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Objective. Germline mutations occurring in the highly penetrant genes BRCA1 and BRCA2 are responsible for only certain cases of familial breast cancer (BC) and ovarian cancer (OC). Thus, the use of NGS multi-gene panel (MGP) testing has recently become very popular. Methods. To estimate a reliable BC and OC risk associated with pathogenic variants in the selected candidate BC/OC predisposition genes, a comprehensive meta-analysis of 48 MGP-based studies analyzing BC/OC patients was conducted. The role of 37 genes was evaluated, comparing, in total, the mutation frequency in similar to 120,000 BC/OC cases and similar to 120,000 controls, which guaranteed strong statistical support with high confidence for most analyzed genes. Results. We characterized the strategies of MGP analyses and the types and localizations of the identified mutations and showed that 13 and 11 of the analyzed genes were significantly associated with an increased BC and OC risk, respectively. The risk attributed to some of these genes (e.g., CDKN2A and PALB2 for BC) was similar to that observed for BRCA2. The analysis also showed a substantial difference in the profile of genes contributing to either BC or OC risk, including genes specifically associated with a high risk of OC but not BC (e.g., RAD51C, and RADS1D). Conclusions. Our study provides strong statistical proof, defines the risk for many genes often considered candidates for BC/OC predisposition and excludes the role of other genes frequently analyzed in the MGPs. In the context of clinical diagnostics, the results support the knowledge-based interpretation of identified mutations. (C) 2019 The Authors. Published by Elsevier Inc.

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