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Updated: Jun 7, 2025
A Data Integration Workflow to Identify Drug Combinations Targeting Synthetic Lethal Interactions
Published on: May 27, 2021
A multilineage screen identifies actionable synthetic lethal interactions in human cancers
Samson H Fong1,2, Brent M Kuenzi1, Nicole M Mattson1
1Division of Human Genomics and Precision Medicine, Department of Medicine, University of California San Diego, La Jolla, CA, USA.
Abstract:
Cancers are driven by alterations in diverse genes, creating dependencies that can be therapeutically targeted. However, many genetic dependencies have proven inconsistent across tumors. Here we describe SCHEMATIC, a strategy to identify a core network of highly penetrant, actionable genetic interactions. First, fundamental cellular processes are perturbed by systematic combinatorial knockouts across tumor lineages, identifying 1,805 synthetic lethal interactions (95% unreported). Interactions are then analyzed by hierarchical pooling, revealing that half segregate reliably by tissue type or biomarker status (51%) and a substantial minority are penetrant across lineages (34%). Interactions converge on 49 multigene systems, including MAPK signaling and BAF transcriptional regulatory complexes, which become essential on disruption of polymerases. Some 266 interactions translate to robust biomarkers of drug sensitivity, including frequent genetic alterations in the KDM5C/6A histone demethylases, which sensitize to inhibition of TIPARP (PARP7). SCHEMATIC offers a context-aware, data-driven approach to match genetic alterations to targeted therapies.
Insights
SCHEMATIC identifies reliable genetic interactions across cancer types. This strategy links specific genetic alterations to targeted therapies, improving personalized cancer treatment by revealing actionable dependencies.
Area of Science:
- Oncology
- Genetics
- Systems Biology
Background:
- Cancer arises from genetic alterations, creating dependencies exploitable for therapy.
- Many identified genetic dependencies lack consistency across different tumor types.
- A need exists for robust methods to discover actionable genetic interactions in cancer.
Purpose of the Study:
- To introduce SCHEMATIC, a novel strategy for identifying a core network of highly penetrant, actionable genetic interactions.
- To develop a context-aware, data-driven approach for matching genetic alterations to targeted cancer therapies.
Main Methods:
- Systematic combinatorial knockouts across tumor lineages to perturb fundamental cellular processes.
- Hierarchical pooling analysis of identified synthetic lethal interactions to assess reliability and lineage specificity.
- Convergence analysis of interactions onto multigene systems and identification of drug sensitivity biomarkers.
Main Results:
- Identified 1,805 novel synthetic lethal interactions, with 95% previously unreported.
- Found that 51% of interactions segregate by tissue type or biomarker status, and 34% are penetrant across lineages.
- Discovered 49 essential multigene systems, including MAPK signaling and BAF complexes, and 266 interactions correlating with drug sensitivity, such as KDM5C/6A alterations sensitizing to TIPARP inhibition.
Conclusions:
- SCHEMATIC provides a robust framework for discovering actionable genetic interactions in cancer.
- The identified interactions and biomarkers can guide the development of targeted therapies.
- This approach enhances the precision of matching genetic alterations to effective cancer treatments.
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