Synthetic lethality prediction in DNA damage repair, chromatin remodeling and the cell cycle using multi-omics data

Magda Markowska1,2, Magdalena A Budzinska1,3, Anna Coenen-Stass4

  • 1Faculty of Mathematics, Informatics and Mechanics, University of Warsaw, Stefana Banacha 2, 02-097, Warsaw, Poland.

Scientific Reports
|April 29, 2023
PubMed

Insights

We developed SLIDE-VIP, a new computational framework to identify synthetic lethal (SL) gene interactions for cancer therapy. This method significantly reduces candidate pairs, uncovering potential drug targets from complex biological data.

Area of Science:

  • Computational Biology
  • Genomics
  • Cancer Research

Background:

  • Identifying synthetic lethal (SL) gene interactions is crucial for developing targeted cancer therapies.
  • Challenges include the vast number of gene pairs and data noise, hindering robust discovery.

Purpose of the Study:

  • To introduce SLIDE-VIP, a novel framework for discovering robust SL gene interactions.
  • To leverage multi-omics data for enhanced identification of clinically relevant SL pairs.

Main Methods:

  • SLIDE-VIP integrates eight statistical tests, including a new patient data-based test (iSurvLRT).
  • Utilizes multi-omics data from cell line screens, patient data, drug screens, and gene pathways.
  • Applied to identify SL interactions among DNA repair, chromatin remodeling, and cell cycle genes.

Main Results:

  • Reduced the search space from 200,000 to 883 high-confidence SL candidates.
  • Validated known SL pairs (e.g., RB1-E2F3, PRKDC-ATM) and proposed novel candidates (e.g., PTEN-PIK3CB).
  • Drug screen and pathway analyses provided corroboration and biological insights.

Conclusions:

  • SLIDE-VIP effectively identifies SL interactions with significant clinical potential.
  • The framework facilitates the discovery of novel therapeutic targets in cancer.
  • Analysis and visualizations are accessible via the SLIDE-VIP WebApp.

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