Predict radiation-induced shifts in patient-specific tumor immune ecosystem composition to harness immunological consequences of radiotherapy

摘要 肿瘤相关抗原、应激蛋白和危险相关分子模式是内源性的。 免疫佐剂，既能启动并持续刺激对肿瘤的免疫反应。在……里面 报复，肿瘤可以劫持内在免疫调节程序，从而促进持续生长 尽管有被激活的抗肿瘤免疫反应。临床上明显的肿瘤与患者的共同进化 并形成一个复杂的肿瘤免疫生态系统(TIES)。放射治疗的成功(RT) 可能是辐射改变了肿瘤和免疫细胞的相对比例，使存活下来的 癌细胞容易被免疫系统清除。然而，目前的RT分级并没有 特别侧重于增强免疫反应，免疫细胞也没有像 生物标志物被认为可以预测治疗反应。我们假设患有多发性硬化症的患者 放射可以清除肿瘤，并诱导强大的免疫反应，这种反应可能会被治愈。与弱者的联系 抗肿瘤免疫或较强的免疫抑制可能不会被当前的RT剂量充分干扰 分离以充分利用放射免疫协同作用并提供肿瘤控制。该项目的目标是 结合实验研究和临床数据来校准和严格验证In Silico框架 模拟了不同TIES组成对不同辐射剂量和剂量响应的影响 分馏。我们将重点关注口咽癌，这是发病率增加的少数癌症类型之一。在……里面 具有和不具有肿瘤特异性T细胞的活体肿瘤提供辐射剂量和分割依赖的变化 在免疫渗透中推导出硅胶模型的参数。对于临床分析，我们将使用回顾 51例口咽癌(OPC)组织样本作为训练队列。我们将前瞻性地收集 105例OPC患者放射治疗后的放射敏感性和免疫渗入资料 不同的总剂量，取决于其固有的放射敏感性指数(RSI)。这些数据用作测试队列 对照3个月后完全缓解的临床评估，验证模型结果预测。我们的 总体目标是确定辐射剂量和分级，以优化辐射诱导免疫，并 确定如何使用RT将特定于患者的联系转移到免疫调节的肿瘤消除。这些 AIMS将推动我们如何构思和临床开出RT的深刻变化。辐射可能是 被理解为免疫疗法。对于有不良关系的患者，RT分割方案应侧重于 对免疫调节的肿瘤控制的联系的根本扰动。对于有利的关系，剂量可能 将重点放在免疫激活上，以降级。整合我们的跨学科专业知识使我们能够预测 RT响应和指导个别患者的决策，这有望导致更好的 结果。成功的项目完成激励了硅胶模型框架辅助的临床试验。

SUMMARY Tumor-associated antigens, stress proteins, and danger-associated molecular patterns are endogenous immune adjuvants that can both initiate and continually stimulate an immune response against a tumor. In retaliation, tumors can hijack intrinsic immune regulatory programs, thereby facilitating continued growth despite an activated antitumor immune response. Clinically apparent tumors have co-evolved with the patient’s immune system and form a complex Tumor-Immune EcoSystem (TIES). The success of radiotherapy (RT) may be the result of radiation shifting the relative proportions of tumor and immune cells such that surviving cancer cells are subject to elimination by the immune system. However, current RT fractionation has not specifically focused on enhancing immune responses, nor has immune cell infiltration into the tumor as biomarker been considered to predict treatment response. We hypothesize that patients with a TIES such that radiation debulks the tumor and induces a robust immune response may be cured. A TIES with weak antitumor-immunity or strong immune suppression may not be sufficiently perturbed by current RT dose fractionation to fully harness radiation-immune synergy and provide tumor control. The goal of the project is to combine experimental studies and clinical data to calibrate and rigorously validate the in silico framework that simulates the influence of different TIES compositions on the response to different radiation doses and dose fractionations. We will focus on oropharyngeal cancer, one of the few cancer types increasing in incidence. In vivo tumors with and without tumor specific T cells provide radiation dose and fractionation-dependent changes in immune infiltration to derive in silico model parameters. For clinical analysis we will use a retrospective cohort of 51 oropharyngeal cancer (OPC) tissue samples as training cohort. We will prospectively collect radiosensitivity and immune infiltration data from 105 OPC patients that undergo radiation therapy with different total doses, dependent on their intrinsic radiosensitivity index (RSI). These data serve as a test cohort to validate model outcome predictions against clinical assessment of complete response at 3 months. Our overall aims are to determine radiation dose and fractionation that optimize radiation-induced immunity, and to identify how to use RT to shift a patient-specific TIES toward immune-modulated tumor elimination. These aims will motivate profound changes to how we conceive of and clinically prescribe RT. Radiation could be understood as immunotherapy. For patients with unfavorable TIES, RT fractionation protocols should focus on the radical perturbation of the TIES toward immune-modulated tumor control. For favorable TIES, dose could be de-escalated with focus on immune activation. Integrating our interdisciplinary expertise allows us to predict RT response and guide decision-making for individual patients, which holds the promise of leading to better outcomes. Successful project completion motivates an in silico model framework-aided clinical trial.