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

预测辐射引起的患者特异性肿瘤免疫生态系统组成的变化，以利用放射治疗的免疫学后果

• 批准号: 10589786
• 负责人: Heiko Enderling
• 金额: $ 10.61万
• 依托单位: H. LEE MOFFITT CANCER CTR & RES INST
• 依托单位国家: 美国
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-03-01 至 2023-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10589786
• 关键词: Address; Adoptive Transfer; Biological; Biological Markers; Biopsy Specimen; CD8-Positive T-Lymphocytes; Calibration; Cancer Patient; Cell Survival; Cells; Clinical; Clinical Data; Clinical Trials; Clinical assessments; Complex; Data; Decision Making; Dose; Dose Fractionation; Ecosystem; Environment; Environmental Risk Factor; Evolution; Experimental Models; Fractionated radiotherapy; Fractionation; Funding; Goals; Growth; HPV oropharyngeal cancer; Heat shock proteins; High Prevalence; Human Papillomavirus; Immune; Immune response; Immune system; Immunity; Immunologic Adjuvants; Immunologic Markers; Immunologic Stimulation; Immunologics; Immunosuppression; Immunotherapy; In complete remission; Incidence; Individual; Link; Literature; Malignant Neoplasms; Modeling; Molecular; Molecular Profiling; Normal tissue morphology; Oncology; Outcome; Patient Simulation; Patients; Pattern; Prediction of Response to Therapy; Property; Protocols documentation; Radiation; Radiation Dose Unit; Radiation Oncology; Radiation Tolerance; Radiation therapy; Retrospective cohort; Sample Size; Sampling; Schedule; T-Lymphocyte; Testing; Therapeutic Agents; Tissue Sample; Training; Treatment Efficacy; Treatment-related toxicity; Tumor Antigens; Tumor Debulking; Tumor Immunity; Tumor-Infiltrating Lymphocytes; anti-tumor immune response; cancer cell; cancer survival; cancer therapy; cancer type; clinical practice; cohort; cytotoxic; dynamic system; experimental study; first-in-human; immune activation; immune cell infiltrate; immune clearance; immunoregulation; improved; improved outcome; in silico; in vivo; indexing; individual patient; innovation; interest; malignant oropharynx neoplasm; mathematical model; mouse model; outcome prediction; personalized medicine; predictive modeling; programs; prospective; radiation effect; response; spatiotemporal; standard of care; success; synergism; therapy outcome; treatment response; tumor; tumor growth; virus related cancer

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.

概括 与肿瘤相关的抗原，应激蛋白和危险的分子模式是内源性的 最初可以刺激对肿瘤的免疫反应的免疫佐剂 报复，肿瘤可以劫持内在的免疫调节计划，从而促进生长 尽管激活的抗肿瘤免疫反应。 免疫系统并形成一个复杂的肿瘤免疫生态系统（RT）。 可能是辐射转移肿瘤和免疫细胞相对比例的结果 癌细胞受到免疫系统的消除。 特别专注于增强免疫反应，免疫细胞渗入肿瘤也不是 生物标志物被认为可以预测治疗反应。 辐射脱离肿瘤并诱导可靠的IMUNE反应可以治愈。 抗肿瘤免疫或强烈的免疫替代性可能不会受到当前RT剂量的扰动 完全利用辐射 - 免疫协同作用并提供肿瘤控制的分馏。 结合实验研究和钙质数据，以校准和严格验证在计算机框架中 模拟不同关系组成对不同辐射剂量和剂量的反应的影响 分馏。 带有肿瘤特异性T细胞的体内肿瘤可提供辐射剂量和分级依赖性变化 在用于临床分析的硅模型参数中的免疫浸润。 51个口交癌（OPC）组织样品作为训练队列 来自105名OPC患者的放射疗法和免疫浸润数据，并接受放射治疗。 不同的总剂量，取决于其内在的辐射指数（RSI）。 为了验证对竞争反应的临床评估的模型预测3个月 总体目的是确定辐射剂量和分馏，以使辐射诱导的免疫力以及对 确定如何使用RT将患者特异性的纽带转移到免疫调节的肿瘤消除。 目的将激励我们对我们的想法进行深刻的变化，并在临床上开处方 RT分级方案理解为具有不利联系的患者的免疫疗法 朝向免疫调节的肿瘤控制的根本扰动。 通过关注免疫激活来逐渐升级。 RT的反应和指导个别患者的决策，这有望导致更好 结果。