Harnessing synthetic lethality to predict the response to cancer treatment

Area of Science:

• Oncology
• Genomics
• Computational Biology

Background:

• Synthetic lethality (SL) is a promising strategy for cancer therapy, but experimental screens often yield candidates with limited clinical relevance.
• Identifying clinically actionable SL interactions is crucial for translating SL into effective cancer treatments.

Purpose of the Study:

• To develop and validate ISLE (identification of clinically relevant synthetic lethality), a data-driven approach to identify clinically relevant SL interactions (cSLi).
• To improve the translational value of SL candidates and enhance precision cancer therapy.

Main Methods:

• Mining The Cancer Genome Atlas (TCGA) cohort to identify cSLi from lab-screened SL candidates.
• Validating ISLE using large-scale drug response screens and predicting drug efficacy in mouse xenograft models.
• Experimentally testing predicted cSLi in screening experiments, assessing context-specific sensitivity (hypoxic vs. normoxic conditions).

Main Results:

• ISLE successfully identified clinically relevant SL interactions (cSLi).
• Experimental validation confirmed predicted context-specific sensitivities and synergistic drug combinations.
• cSLi demonstrated utility in predicting patient drug treatment response and providing patient stratification signatures.

Conclusions:

• ISLE is a valuable tool for identifying clinically relevant synthetic lethality, complementing existing precision cancer therapy methods.
• This approach expands the scope of precision therapy beyond actionable mutations to the whole genome.
• ISLE offers a pathway to improve patient stratification and predict treatment response for more effective cancer therapies.