In:
npj Genomic Medicine, Springer Science and Business Media LLC, Vol. 2, No. 1 ( 2017-10-03)
Abstract:
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 × 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.
Type of Medium:
Online Resource
ISSN:
2056-7944
DOI:
10.1038/s41525-017-0032-5
Language:
English
Publisher:
Springer Science and Business Media LLC
Publication Date:
2017
detail.hit.zdb_id:
2813848-X
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