Knowledge graph-based recommendation framework identifies drivers of resistance in EGFR mutant non-small cell lung cancer

Anna Gogleva, Dimitris Polychronopoulos, Matthias Pfeifer, Vladimir Poroshin, Michaël Ughetto, Matthew J. Martin, Hannah Thorpe, Aurelie Bornot, Paul D. Smith, Ben Sidders, Jonathan R. Dry, Miika Ahdesmäki, Ultan McDermott, Eliseo Papa () and Krishna C. Bulusu ()
Additional contact information
Anna Gogleva: AI Engineering, R&D IT, AstraZeneca
Dimitris Polychronopoulos: Oncology R&D, AstraZeneca
Matthias Pfeifer: Oncology R&D, AstraZeneca
Vladimir Poroshin: IGNITE, AstraZeneca
Michaël Ughetto: AI Engineering, R&D IT, AstraZeneca
Matthew J. Martin: Oncology R&D, AstraZeneca
Hannah Thorpe: Oncology R&D, AstraZeneca
Aurelie Bornot: Discovery Sciences, R&D, AstraZeneca
Paul D. Smith: Oncology R&D, AstraZeneca
Ben Sidders: Oncology R&D, AstraZeneca
Jonathan R. Dry: Oncology R&D, AstraZeneca
Miika Ahdesmäki: Oncology R&D, AstraZeneca
Ultan McDermott: Oncology R&D, AstraZeneca
Eliseo Papa: AI Engineering, R&D IT, AstraZeneca
Krishna C. Bulusu: Oncology R&D, AstraZeneca

Nature Communications, 2022, vol. 13, issue 1, 1-14

Abstract: 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 manual curation that requires specialized knowledge and can take even experts several months to comprehensively complete. To find key drivers of resistance faster we build a recommendation system on top of a heterogeneous biomedical knowledge graph integrating pre-clinical, clinical, and literature evidence. The recommender system ranks genes based on trade-offs between diverse types of evidence linking them to potential mechanisms of EGFRi resistance. This unbiased approach identifies 57 resistance markers from >3,000 genes, reducing hit identification time from months to minutes. In addition to reproducing known resistance markers, our method identifies previously unexplored resistance mechanisms that we prospectively validate.

Date: 2022
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DOI: 10.1038/s41467-022-29292-7

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