INCISOR: An Algorithm to Identify Synthetic Rescue Mediators of Resistance to Targeted and Immunotherapy

Area of Science:

• Oncology
• Genomics
• Computational Biology

Background:

• Targeted therapies and immunotherapies have advanced cancer treatment.
• Tumor resistance to these therapies remains a significant clinical challenge.
• Tumor cells can acquire compensatory gene alterations, known as synthetic rescue (SR) interactions, to evade therapy.

Purpose of the Study:

• To introduce INCISOR, a novel algorithm for identifying synthetic rescue interactions.
• To provide a framework for developing novel therapeutic strategies to overcome tumor resistance.

Main Methods:

• Developed INCISOR, an algorithm leveraging tumor transcriptomics and clinical data from 10,000 patients.
• Integrated data from experimental screens to identify SRs.
• Performed genome-wide analysis of half a billion possible gene-gene combinations.

Main Results:

• Identified common synthetic rescue interactions across multiple cancer types.
• Demonstrated INCISOR's capability for large-scale SR identification.
• Established a computational framework for understanding and targeting therapy resistance.

Conclusions:

• Synthetic rescue interactions are a key mechanism of tumor resistance.
• INCISOR is a powerful tool for discovering SRs across diverse cancer types.
• This work provides a foundation for designing next-generation therapies to combat cancer drug resistance.