Identification of potentially oncogenic alterations from tumor-only samples reveals Fanconi anemia pathway mutations...

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.