Effects of thymic selection on T cell recognition of foreign and tumor antigenic peptides

Area of Science:

• Immunology
• Computational Biology
• Oncology

Background:

• Cancer immunotherapy offers novel strategies by leveraging the immune system, particularly cytotoxic T lymphocytes (CTLs).
• Current optimization of immunotherapies relies heavily on empirical approaches rather than predictive models.
• CTLs recognize tumor-associated neoantigens (TANs) similarly to foreign peptides presented on cell surfaces.

Purpose of the Study:

• To develop a predictive model for T cell selection in cancer immunotherapy.
• To analytically study the impact of selection on CTL recognition of TANs and foreign peptides.
• To estimate the number of CTLs capable of detecting donor-matched transplants.

Main Methods:

• Development of a mathematical model for T cell selection based on random interaction strengths between self-peptides and T cell receptors.
• Analytical study of selection effects on CTL recognition of TANs and foreign peptides.
• Simulation to confirm analytical results and validate the model's predictions.

Main Results:

• The model demonstrates that negative selection thresholds, aligned with experimental thymic survival rates, lead to near-optimal T cell production.
• Optimized T cells exhibit enhanced survival post-selection and improved recognition of foreign antigens.
• The model provides a framework for estimating CTLs capable of recognizing donor-matched transplants.

Conclusions:

• The developed model provides an analytical approach to understanding T cell selection in immunotherapy.
• Experimentally observed thymic survival rates correlate with optimal T cell production for immunotherapy.
• This work offers a foundation for moving beyond empirical optimization in cancer immunotherapy development.