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A Data Integration Workflow to Identify Drug Combinations Targeting Synthetic Lethal Interactions
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A Data Integration Workflow to Identify Drug Combinations Targeting Synthetic Lethal Interactions

Published on: May 27, 2021

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A landscape of synthetic viable interactions in cancer

Yunyan Gu1, Ruiping Wang1, Yue Han1

  • 1Department of Systems Biology, College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China.

Briefings in Bioinformatics
|January 19, 2017
PubMed

Insights

Synthetic viability interactions can help cancer cells resist targeted drugs. Our new computational method, SVDR, identifies these interactions to predict drug resistance and patient prognosis.

Area of Science:

  • Genomics
  • Computational Biology
  • Cancer Research

Background:

  • Synthetic viability (SV) interactions, where combined gene alterations rescue lethal single-gene defects, are implicated in cancer drug resistance.
  • Current methods to detect SV interactions in cancer genomes for drug resistance studies are limited.

Purpose of the Study:

  • To develop a computational method (SVDR) for detecting synthetic viability-induced drug resistance (SVDR).
  • To characterize the landscape of SV interactions across diverse cancer types and assess their clinical relevance.
  • To evaluate SVDR's capability in predicting drug resistance using pharmacogenomic data.

Main Methods:

  • Integrated multidimensional datasets: copy number alteration, whole-exome mutation, expression profiles, and clinical data.
  • Applied SVDR to The Cancer Genome Atlas (TCGA) data (8580 tumors, 32 cancer types).
  • Incorporated functional genomics (shRNA) and yeast genetic interaction data.
  • Validated drug resistance prediction using Cancer Cell Line Encyclopedia (CCLE) and Broad Cancer Therapeutics Response Portal data.

Main Results:

  • SVDR comprehensively mapped SV interactions across 32 cancer types.
  • Identified that SV interactions are generally beneficial for cancer cells.
  • Demonstrated that SV interactions can predict clinical patient prognosis.
  • Showcased SVDR's high reliability in predicting drug resistance across independent pharmacogenomic datasets.

Conclusions:

  • SVDR is the first genome-scale, data-driven approach to identify SV interactions linked to cancer drug resistance.
  • This method provides a foundation for discovering genomic markers for drug resistance prediction.
  • SVDR can infer potential drug combinations for enhanced anti-cancer therapies.

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