Chimeric tumor modeling reveals role of partial PDL1 expression in resistance to virally induced immunotherapy

Mee Y Bartee1, Parker C Dryja1, Eric Bartee2

  • 1Department of Microbiology and Immunology, Medical University of South Carolina, Basic Science Building Rm 208C, 173 Ashley Ave, Charleston, SC, 29425, USA.

Insights

Tumor resistance to oncolytic immunotherapy depends on the percentage of cancer cells expressing PDL1. Even partial PDL1 expression significantly impacts treatment outcomes and relapse dynamics.

Area of Science:

  • Immunology
  • Oncology
  • Cancer Research

Background:

  • Tumor cell expression of Programmed Death-Ligand 1 (PDL1) can limit the effectiveness of cancer immunotherapies.
  • Previous studies demonstrated that tumors lacking PDL1 are sensitive to oncolytic virus treatment, while PDL1-expressing tumors are resistant.
  • Heterogeneous PDL1 expression in patient tumors complicates immunotherapy outcomes, with limited understanding of its impact.

Purpose of the Study:

  • To investigate the impact of partial PDL1 expression on tumor response to oncolytic immunotherapy.
  • To determine the threshold of PDL1-positive cells required for functional tumor resistance.
  • To explore the role of PDL1 expression in tumor relapse after treatment.

Main Methods:

  • Generation of partially PDL1-positive tumors in immunocompetent animal models.
  • Assessment of tumor response to oncolytic virus treatment based on varying PDL1 expression levels.
  • Analysis of PDL1 expression in tumors before and after treatment, including relapsed tumors.

Main Results:

  • Tumor responsiveness to oncolytic immunotherapy showed a linear correlation with the percentage of PDL1-expressing cells.
  • Tumors with higher percentages of PDL1-positive cells exhibited greater resistance to treatment.
  • Relapsed tumors demonstrated a significant increase in PDL1-capable cells, suggesting immune editing.

Conclusions:

  • Varying levels of PDL1 expression critically influence oncolytic immunotherapy outcomes.
  • Tumor heterogeneity in PDL1 expression challenges a binary PDL1-positive/negative classification.
  • Understanding partial PDL1 positivity is crucial for predicting treatment response and managing disease relapse.

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