T cells protect the body from cancer by recognising tumour-associated antigens. Recognising these antigens depends on multiple factors, one of which is T cell avidity, i.e., the total interaction strength between a T cell and a cancer cell. While both high- and low-avidity T cells can kill cancer cells, durable anti-cancer immune responses require the selection of high-avidity T cells. Previous experimentation with anti-cancer vaccines, however, has shown that most vaccines elicit low-avidity T cells. Optimising vaccine schedules may remedy this by preferentially selecting high-avidity T cells. Here, we use mathematical modelling to develop a simple, phenomenological model of avidity selection that may identify vaccine schedules that disproportionately favour low-avidity T cells. We calibrate our model to our prior, more complex model, and then validate it against several experimental data sets. We find that the sensitivity of the model’s parameters change with vaccine dosage, which allows us to use a patient’s data and clinical history to screen for suitable vaccine strategies.

T 细胞通过识别肿瘤相关抗原来保护身体免受癌症侵害。识别这些抗原取决于多种因素，其中之一是 T 细胞亲和力，即 T 细胞和癌细胞之间的总相互作用强度。虽然高亲和力和低亲和力的 T 细胞都可以杀死癌细胞，但持久的抗癌免疫反应需要选择高亲和力的 T 细胞。然而，之前的抗癌疫苗实验表明，大多数疫苗会引发低亲和力的 T 细胞。优化疫苗计划可以通过优先选择高亲和力的 T 细胞来解决这个问题。在这里，我们使用数学建模来开发一个简单的、亲合力选择的现象学模型，该模型可以识别不成比例地偏爱低亲合力 T 细胞的疫苗计划。我们将我们的模型校准到我们之前的更复杂的模型，然后根据几个实验数据集对其进行验证。我们发现模型参数的敏感性随疫苗剂量而变化，这使我们能够使用患者的数据和临床病史来筛选合适的疫苗策略。