Journal
PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA
Volume 114, Issue 38, Pages E7875-E7881Publisher
NATL ACAD SCIENCES
DOI: 10.1073/pnas.1708573114
Keywords
neoantigen; T cell; immunotherapy; applied probability
Categories
Funding
- National Cancer Institute of the NIH Grant [F30CA213878]
- United States-Israel Binational Science Foundation Grant [2015619]
- Cancer Prevention and Research Institute of Texas Scholars Program [R1111]
- Division Of Environmental Biology
- Direct For Biological Sciences [2015619] Funding Source: National Science Foundation
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The advent of cancer immunotherapy has generated renewed hope for the treatment of many malignancies by introducing a number of novel strategies that exploit various properties of the immune system. These therapies are based on the idea that cytotoxic T lymphocytes (CTLs) directly recognize and respond to tumor-associated neoantigens (TANs) in much the same way as they would to foreign peptides presented on cell surfaces. To date, however, nearly all attempts to optimize immunotherapeutic strategies have been empirical. Here, we develop a model of T cell selection based on the assumption of random interaction strengths between a self-peptide and the various T cell receptors. The model enables the analytical study of the effects of selection on the CTL recognition of TANs and completely foreign peptides and can estimate the number of CTLs that can detect donor-matched transplants. We show that negative selection thresholds chosen to reflect experimentally observed thymic survival rates result in near-optimal production of T cells that are capable of surviving selection and recognizing foreign antigen. These analytical results are confirmed by simulation.
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