Journal
NPJ GENOMIC MEDICINE
Volume 2, Issue -, Pages -Publisher
NATURE PORTFOLIO
DOI: 10.1038/s41525-017-0032-5
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Funding
- NIH [U54 CA193313, 5T32 GM07367]
- Precision Medicine Fellowship [UL1 TR000040]
- Swiss National Science Foundation
- National Research Service Award [F31CA210607]
- [R01 CA185486-01]
- [R01 CA179044-01A1]
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Cancer is caused by germline and somatic mutations, which can share biological features such as amino acid change. However, integrated germline and somatic analysis remains uncommon. We present a framework that uses machine learning to learn features of recurrent somatic mutations to (1) predict somatic variants from tumor-only samples and (2) identify somatic-like germline variants for integrated analysis of tumor-normal DNA. Using data from 1769 patients from seven cancer types (bladder, glioblastoma, low-grade glioma, lung, melanoma, stomach, and pediatric glioma), we show that somatic-like germline variants are enriched for autosomal-dominant cancer-predisposition genes (p < 4.35 x 10(-15)), including TP53. Our framework identifies germline and somatic nonsense variants in BRCA2 and other Fanconi anemia genes in 11% (11/100) of bladder cancer cases, suggesting a potential genetic predisposition in these patients. The bladder carcinoma patients with Fanconi anemia nonsense variants display a BRCA-deficiency somatic mutation signature, suggesting treatment targeted to DNA repair.
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