Knowledge Graph-based Recommendation Framework Identifies Novel Drivers of Resistance in EGFR mutant Non-small Cell Lung Cancer

Abstract

Resistance to EGFR inhibitors (EGFRi) presents a major obstacle in treating non-small cell lung cancer (NSCLC). One of the most exciting new ways to find potential resistance markers involves running functional genetic screens, such as CRISPR, followed by manual triage of significantly enriched genes. This triage process to identify high value hits resulting from the CRISPR screen involves significant manual curation that requires specialized knowledge and can take even experts several months to comprehensively complete. To find key drivers of resistance faster we built a hybrid recommendation system on top of a heterogeneous biomedical knowledge graph integrating preclinical, clinical, and literature evidence. Genes were ranked based on trade-offs between diverse types of evidence linking them to potential mechanisms of EGFRi resistance. This unbiased approach identified 36 resistance markers from >3,000 genes, reducing hit identification time from months to minutes. In addition to reproducing known resistance markers, our method identified novel resistance mechanisms that we prospectively validated.