4.6 Article

Hallmarks of Resistance to Immune-Checkpoint Inhibitors

Journal

CANCER IMMUNOLOGY RESEARCH
Volume 10, Issue 4, Pages 372-383

Publisher

AMER ASSOC CANCER RESEARCH
DOI: 10.1158/2326-6066.CIR-20-0586

Keywords

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Funding

  1. Bristol Myers Squibb
  2. CPRIT Research Training Program [RP170067]
  3. Fulbright Commission Franco-Americaine
  4. John J. Kopchick Foundation
  5. NIH/NCI [PA30CA016672, P50CA221703, R01CA236905, U24CA224285, K99/R00CA256526]
  6. PICI
  7. MDACC intramural funding
  8. NIH [K08CA234392]
  9. PICI Bridge Fellows Award
  10. NCI SPORE [P50-CA192937]

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This study systematically explores key nodes of immune resistance through the lens of known biological processes, providing support and guidance for future immune resistance research. Integration of multiomic high-dimensional analyses with patient data can help guide effective drug development strategies and improve patient outcomes.
Immune-checkpoint inhibitors (ICI), although revolutionary in improving long-term survival outcomes, are mostly effective in patients with immune-responsive tumors. Most patients with cancer either do not respond to ICIs at all or experience disease progression after an initial period of response. Treatment resistance to ICIs remains a major challenge and defines the biggest unmet medical need in oncology worldwide. In a collaborative workshop, thought leaders from academic, bio-pharma, and nonprofit sectors convened to outline a resistance framework to support and guide future immune-resistance research. Here, we explore the initial part of our effort by collating seminal discoveries through the lens of known bio-logical processes. We highlight eight biological processes and refer to them as immune resistance nodes. We examine the seminal discoveries that define each immune resistance node and pose critical questions, which, if answered, would greatly expand our notion of immune resistance. Ultimately, the expansion and application of this work calls for the integration of multiomic high-dimensional analyses from patient-level data to produce a map of resistance phenotypes that can be utilized to guide effective drug development and improved patient outcomes.

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