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

Author

Listed:
  • 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)

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.

Suggested Citation

  • 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 &, 2022. "Knowledge graph-based recommendation framework identifies drivers of resistance in EGFR mutant non-small cell lung cancer," Nature Communications, Nature, vol. 13(1), pages 1-14, December.
    • Handle: RePEc:nat:natcom:v:13:y:2022:i:1:d:10.1038_s41467-022-29292-7
    DOI: 10.1038/s41467-022-29292-7
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    References listed on IDEAS

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