Advancing CAR T-cell Therapies with Artificial Intelligence: Opportunities and Challenges.

Journal: Blood cancer discovery
PMID: 40152695

Abstract

Artificial intelligence could enhance chimeric antigen receptor T-cell therapy outcomes through optimization of all steps, from target identification, vector design, and manufacturing to personalized data-driven clinical decisions. In this report, we highlight steps toward unlocking this potential, including the need for standardized, comprehensive data repositories as a way for addressing barriers to artificial intelligence learning, such as data heterogeneity and patient privacy.

Authors

  • Fabio Luciani
    Systems Immunology and Immunogenomics, School of Biomedical Sciences, UNSW Sydney, Sydney, Australia.
  • Arman Safavi
    Systems Immunology and Immunogenomics, School of Biomedical Sciences, UNSW Sydney, Sydney, Australia.
  • Puneeth Guruprasad
    Center for Cellular Immunotherapies, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, Pennsylvania.
  • Linhui Chen
    Center for Cellular Immunotherapies, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, Pennsylvania.
  • Marco Ruella
    Center for Cellular Immunotherapies, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, Pennsylvania.

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