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

Journal: NPJ genomic medicine
Published Date:

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 ( < 4.35 × 10), including . Our framework identifies germline and somatic nonsense variants in 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 -deficiency somatic mutation signature, suggesting treatment targeted to DNA repair.

Authors

  • Chioma J Madubata
    Department of Systems Biology, Columbia University, New York, NY 10032 USA.
  • Alireza Roshan-Ghias
    Department of Systems Biology, Columbia University, New York, NY 10032 USA.
  • Timothy Chu
    Department of Systems Biology, Columbia University, New York, NY 10032 USA.
  • Samuel Resnick
    Department of Systems Biology, Columbia University, New York, NY 10032 USA.
  • Junfei Zhao
    Department of Systems Biology, Columbia University, New York, NY 10032 USA.
  • Luis Arnes
    Department of Systems Biology, Columbia University, New York, NY 10032 USA.
  • Jiguang Wang
    Department of Systems Biology, Columbia University, New York, NY 10032 USA.
  • Raul Rabadan
    Department of Systems Biology, Columbia University, New York, NY 10032 USA.

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