Center for Theoretical Biological Physics, Rice University, Houston, TX 77005;
Department of Bioengineering, Rice University, Houston, TX 77005.
Proc Natl Acad Sci U S A. 2017 Sep 19;114(38):E7875-E7881. doi: 10.1073/pnas.1708573114. Epub 2017 Sep 5.
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.
癌症免疫疗法的出现为治疗许多恶性肿瘤带来了新的希望,它引入了许多新的策略,利用了免疫系统的各种特性。这些疗法的基础是细胞毒性 T 淋巴细胞 (CTL) 能够直接识别和响应肿瘤相关的新抗原 (TAN),就像它们识别细胞表面上呈现的外来肽一样。然而,迄今为止,几乎所有优化免疫治疗策略的尝试都是经验性的。在这里,我们基于自我肽和各种 T 细胞受体之间的随机相互作用强度的假设,建立了 T 细胞选择的模型。该模型能够对选择对 CTL 识别 TAN 和完全外来肽的影响进行分析研究,并能够估计能够检测到与供体匹配的移植的 CTL 的数量。我们表明,选择以反映实验观察到的胸腺存活率的负选择阈值导致能够在选择中存活并识别外来抗原的 T 细胞的近乎最佳产生。这些分析结果通过模拟得到了证实。