A multispecies framework for modeling adaptive immunity and immunotherapy in cancer

Area of Science:

• Computational Biology
• Immunology
• Cancer Research

Background:

• Predator-prey theory models tumor growth under immune pressure, involving tumor immunopeptidome and T-cell receptor (TCR) repertoire.
• Tumor immunopeptidome heterogeneity predicts immunotherapy response, suggesting limitations in TCR repertoire's polyclonal response.
• Competition for resources between tumor and T-cells, and among peripheral T-cells, impacts the TCR repertoire, but remains poorly understood.

Purpose of the Study:

• To develop and validate a computational model simulating tumor growth, immunopeptidome diversification, and T-cell expansion.
• To investigate the influence of resource competition on T-cell lineage dynamics and the TCR repertoire.
• To explore the impact of tumor growth and mutation rates on treatment outcomes within a predator-prey framework.

Main Methods:

• Constructed a predator-prey-like computational model calibrated with preclinical and clinical data.
• Modeled tumor growth and immunopeptidome diversification.
• Simulated antigen-specific T-cell lineage expansion and resource consumption (both specific and shared).

Main Results:

• The model accurately described clinically observed tumor immunopeptidomes.
• It recapitulated immunotherapy effects, including immunoediting.
• The framework allowed for the exploration of treatment strategies for tumors with varying growth and mutation rates.

Conclusions:

• Predator-prey dynamics provide a valuable framework for understanding tumor-immune interactions and immunopeptidome evolution.
• The model highlights the importance of T-cell repertoire dynamics and resource competition in cancer immunity.
• This computational approach offers insights into optimizing cancer treatment strategies by considering tumor and immune system interplay.