CScape: a tool for predicting oncogenic single-point mutations in the cancer genome

Sci Rep. 2017 Sep 14;7(1):11597. doi: 10.1038/s41598-017-11746-4.


For somatic point mutations in coding and non-coding regions of the genome, we propose CScape, an integrative classifier for predicting the likelihood that mutations are cancer drivers. Tested on somatic mutations, CScape tends to outperform alternative methods, reaching 91% balanced accuracy in coding regions and 70% in non-coding regions, while even higher accuracy may be achieved using thresholds to isolate high-confidence predictions. Positive predictions tend to cluster in genomic regions, so we apply a statistical approach to isolate coding and non-coding regions of the cancer genome that appear enriched for high-confidence predicted disease-drivers. Predictions and software are available at .

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Computational Biology / methods
  • Databases, Genetic
  • Genome, Human*
  • Genomics / methods*
  • Humans
  • Molecular Sequence Annotation
  • Neoplasms / genetics*
  • Point Mutation*
  • ROC Curve
  • Software*
  • Web Browser