Frontiers in OncologyGenetic & Genomic MedicinePeer-reviewed journal article
Disease Spectrum of Breast Cancer Susceptibility Genes
Jin WangiD, Preeti SinghiD, Kanhua YiniD, Jingan ZhouiD, Yujia Bao, Menghua WuiD, Kush Pathak, Sophia K. McKinleyiD, Danielle BrauniD, Kevin S. HughesiD
- Citations
- 15
- FWCI
- 1.56
- Field percentile
- top 18%
Abstract
BackgroundPathogenic variants in cancer susceptibility genes can increase the risk of a spectrum of diseases. We aim to evaluate the disease spectrum of breast cancer susceptibility genes (BCSGs) to develop a comprehensive resource of gene-disease associations for clinicians. MethodsThirteen genes (ATM, BARD1, BRCA1, BRCA2, CDH1, CHEK2, NBN, NF1, PALB2, PTEN, RECQL, STK11 and TP53), that have been conclusively established as BCSGs by the Clinical Genome Resource (ClinGen) and the NCCN guidelines, were investigated. For these thirteen genes, potential gene-disease associations were identified and evaluated based on six genetic resources (ClinGen, NCCN, OMIM, Genetics Home Reference, GeneCards and Gene-NCBI) and an additional literature review using a semiautomated natural language processing (NLP) abstract classification procedure. ResultsA total of 40 diseases were confirmed as being associated with one or more of the 13 BCSGs by our evaluation. Malignant diseases including prostate cancer, pancreatic cancer, colorectal cancer, brain tumor, gastric cancer, ovarian cancer, and sarcoma were associated with at least 3 BCSGs. Furthermore, a total of 87 gene-disease associations were confirmed by our evaluation, of which 85% (74/87) were confirmed by ClinGen and/or NCCN. Conversely, 9 gene-disease associations absent from both ClinGen and NCCN were confirmed in the other four genetic resources ([≥]3) and 4 gene-disease associations were confirmed by the NLP-based procedure. ConclusionThis is the first study to systematically investigate the reported disease spectrum of BCSGs in multiple sources. Our innovative approach provides a general guide for evaluating gene-disease associations, and improves the clinical management for at-risk individuals.
- organism: human
- assay: GWAS summary statistics
- research focus: database / resource
- approach: statistical model
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