Inferring Potential Cancer Driving Synonymous Variants

Zishuo Zeng, Yana Bromberg

Research output: Contribution to journalArticlepeer-review


Synonymous single nucleotide variants (sSNVs) are often considered functionally silent, but a few cases of cancer-causing sSNVs have been reported. From available databases, we collected four categories of sSNVs: germline, somatic in normal tissues, somatic in cancerous tissues, and putative cancer drivers. We found that screening sSNVs for recurrence among patients, conservation of the affected genomic position, and synVep prediction (synVep is a machine learning-based sSNV effect predictor) recovers cancer driver variants (termed proposed drivers) and previously unknown putative cancer genes. Of the 2.9 million somatic sSNVs found in the COSMIC database, we identified 2111 proposed cancer driver sSNVs. Of these, 326 sSNVs could be further tagged for possible RNA splicing effects, RNA structural changes, and affected RBP motifs. This list of proposed cancer driver sSNVs provides computational guidance in prioritizing the experimental evaluation of synonymous mutations found in cancers. Furthermore, our list of novel potential cancer genes, galvanized by synonymous mutations, may highlight yet unexplored cancer mechanisms.

Original languageEnglish (US)
Article number778
Issue number5
StatePublished - May 2022
Externally publishedYes

All Science Journal Classification (ASJC) codes

  • Genetics
  • Genetics(clinical)


  • cancer drivers
  • sSNV
  • somatic variants
  • synonymous variants
  • variant functional impact


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