Modeling T cell temporal response to cancer immunotherapy rationalizes development of combinatorial treatment protocols

Nat Cancer. 2024 May;5(5):742-759. doi: 10.1038/s43018-024-00734-z. Epub 2024 Mar 1.

Abstract

Successful immunotherapy relies on triggering complex responses involving T cell dynamics in tumors and the periphery. Characterizing these responses remains challenging using static human single-cell atlases or mouse models. To address this, we developed a framework for in vivo tracking of tumor-specific CD8+ T cells over time and at single-cell resolution. Our tools facilitate the modeling of gene program dynamics in the tumor microenvironment (TME) and the tumor-draining lymph node (tdLN). Using this approach, we characterize two modes of anti-programmed cell death protein 1 (PD-1) activity, decoupling induced differentiation of tumor-specific activated precursor cells from conventional type 1 dendritic cell (cDC1)-dependent proliferation and recruitment to the TME. We demonstrate that combining anti-PD-1 therapy with anti-4-1BB agonist enhances the recruitment and proliferation of activated precursors, resulting in tumor control. These data suggest that effective response to anti-PD-1 therapy is dependent on sufficient influx of activated precursor CD8+ cells to the TME and highlight the importance of understanding system-level dynamics in optimizing immunotherapies.

MeSH terms

  • Animals
  • CD8-Positive T-Lymphocytes* / drug effects
  • CD8-Positive T-Lymphocytes* / immunology
  • Cell Line, Tumor
  • Dendritic Cells / drug effects
  • Dendritic Cells / immunology
  • Humans
  • Immune Checkpoint Inhibitors / pharmacology
  • Immune Checkpoint Inhibitors / therapeutic use
  • Immunotherapy* / methods
  • Mice
  • Neoplasms / drug therapy
  • Neoplasms / immunology
  • Neoplasms / therapy
  • Programmed Cell Death 1 Receptor / antagonists & inhibitors
  • Tumor Microenvironment* / immunology

Substances

  • Programmed Cell Death 1 Receptor
  • Immune Checkpoint Inhibitors