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

总结 肿瘤相关抗原、应激蛋白和肿瘤相关分子模式是内源性的 免疫佐剂可以启动并持续刺激针对肿瘤的免疫应答。在 为了报复，肿瘤可以劫持内在的免疫调节程序，从而促进持续生长， 尽管有激活的抗肿瘤免疫反应临床上明显的肿瘤与患者的肿瘤共同进化。 免疫系统，并形成一个复杂的肿瘤免疫生态系统（TIES）。放疗（RT）的成功 可能是辐射改变肿瘤和免疫细胞的相对比例的结果， 癌细胞会被免疫系统清除。然而，目前的RT分馏没有 特别关注增强免疫反应，也没有免疫细胞浸润到肿瘤中， 生物标志物被认为可以预测治疗反应。我们假设TIES患者， 放射治疗可以使肿瘤缩小并诱导强烈的免疫反应，从而可以治愈。A TIES弱 抗肿瘤免疫或强免疫抑制可能不会受到当前RT剂量的充分干扰 分离以充分利用辐射-免疫协同作用并提供肿瘤控制。该项目的目标是 联合收割机结合实验研究和临床数据，以校准和严格验证计算机模拟框架， 模拟了不同TIES组分对不同辐射剂量和剂量响应的影响 分馏。我们将重点讨论口咽癌，这是发病率增加的少数癌症类型之一。在 具有和不具有肿瘤特异性T细胞的体内肿瘤提供辐射剂量和分级依赖性变化 在免疫浸润中，以获得计算机模型参数。对于临床分析，我们将使用回顾性 51个口咽癌（OPC）组织样本的队列作为训练队列。我们将前瞻性地收集 来自105名接受放射治疗的OPC患者的放射敏感性和免疫浸润数据， 不同的总剂量，取决于其固有的放射敏感性指数（RSI）。这些数据作为测试队列 根据3个月时完全缓解的临床评估验证模型结局预测。我们 总体目标是确定优化辐射诱导免疫的辐射剂量和分割， 确定如何使用RT将患者特异性TIES转向免疫调节的肿瘤消除。这些 目标将促使我们如何构思和临床处方RT的深刻变化。 也就是免疫疗法对于不良TIES患者，RT分割方案应重点关注 TIES对免疫调节肿瘤控制的根本扰动。对于有利的TIES，剂量可以 把重点放在免疫激活上整合我们的跨学科专业知识使我们能够预测 RT反应和指导决策的个别患者，这持有的承诺，导致更好的 成果。项目的成功完成激发了计算机模型框架辅助临床试验。

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.