Personalized cancer therapy prioritization based on driver alteration co-occurrence patterns

Genome Med. 2020 Sep 9;12(1):78. doi: 10.1186/s13073-020-00774-x.


Identification of actionable genomic vulnerabilities is key to precision oncology. Utilizing a large-scale drug screening in patient-derived xenografts, we uncover driver gene alteration connections, derive driver co-occurrence (DCO) networks, and relate these to drug sensitivity. Our collection of 53 drug-response predictors attains an average balanced accuracy of 58% in a cross-validation setting, rising to 66% for a subset of high-confidence predictions. We experimentally validated 12 out of 14 predictions in mice and adapted our strategy to obtain drug-response models from patients' progression-free survival data. Our strategy reveals links between oncogenic alterations, increasing the clinical impact of genomic profiling.

Keywords: Driver co-occurrence networks; Drug-response biomarkers; Precision oncology.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Algorithms
  • Antineoplastic Agents / pharmacology
  • Antineoplastic Agents / therapeutic use
  • Biomarkers, Tumor
  • Clinical Decision-Making
  • Databases, Factual
  • Disease Management
  • Drug Resistance, Neoplasm / drug effects
  • Gene Expression Regulation, Neoplastic / drug effects
  • Genomics / methods
  • Humans
  • Models, Theoretical*
  • Neoplasms / etiology*
  • Neoplasms / pathology
  • Neoplasms / therapy*
  • Oncogenes
  • Precision Medicine* / methods
  • Reproducibility of Results
  • Translational Research, Biomedical
  • Treatment Outcome


  • Antineoplastic Agents
  • Biomarkers, Tumor