Machine learning identifies clinical tumor mutation landscape pathways of resistance to checkpoint inhibitor therapy in NSCLC.

Journal: Journal for immunotherapy of cancer
PMID: 40032600

Abstract

BACKGROUND: Immune checkpoint inhibitors (CPIs) have revolutionized cancer therapy for several tumor indications. However, a substantial fraction of patients treated with CPIs derive no benefit or have short-lived responses to CPI therapy. Identifying patients who are most likely to benefit from CPIs and deciphering resistance mechanisms is therefore essential for developing adjunct treatments that can abrogate tumor resistance.

Authors

  • Vitalay Fomin
    Roche Pharmaceutical Research and Early Development, Data & Analytics, Roche Innovation Center New York, Little Falls, New Jersey, USA.
  • WeiQing Venus So
    Roche Pharmaceutical Research and Early Development, Data & Analytics, Roche Innovation Center New York, Little Falls, New Jersey, USA.
  • Richard Alex Barbieri
    Data Science, Capgemini Engineering, Paris, France.
  • Kenley Hiller-Bittrolff
    Data Science, Capgemini Engineering, Paris, France.
  • Elina Koletou
    Roche Innovation Center Basel, Switzerland.
  • Tiffany Tu
    Roche Pharmaceutical Research and Early Development, Data & Analytics, Roche Innovation Center New York, Little Falls, New Jersey, USA.
  • Bruno Gomes
    Pharma Research and Early Development, Early Development Oncology, Roche Innovation Center Basel, Basel, Switzerland.
  • James Cai
    Roche Pharmaceutical Research and Early Development, Data & Analytics, Roche Innovation Center New York, Little Falls, New Jersey, USA.
  • Jehad Charo
    Roche Pharmaceutical Research and Early Development Oncology, Roche Innovation Center Zurich, Schlieren, Switzerland jehad.charo@roche.com.

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