Project 2: Radiation-Induced Lymphopenia: Understanding, Predictive Modeling and Developing Photon and Proton-Based Mitigation Strategies.

项目 2：辐射引起的淋巴细胞减少症：理解、预测建模和开发基于光子和质子的缓解策略。

• 批准号: 10270306
• 负责人: Steven Hsesheng Lin
• 金额: $ 64.7万
• 依托单位: MASSACHUSETTS GENERAL HOSPITAL
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-09-21 至 2026-08-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10270306
• 关键词: Adaptive Immune System; Affect; Biological Factors; CD8-Positive T-Lymphocytes; Cancer Patient; Cell physiology; Cells; Characteristics; Clinical; Computer Simulation; Cytometry; DNA Damage; Data; Databases; Development; Disease; Disease Outcome; Distant Metastasis; Dose; Fractionation; Future; Goals; Heterogeneity; Immune; Immune system; Immunity; Immunologic Surveillance; Immunotherapy; Incidence; Intensity-Modulated Radiotherapy; Ionizing radiation; Link; Location; Lymphocyte; Lymphocyte Count; Lymphocyte Depletion; Lymphocyte Subset; Lymphopenia; Machine Learning; Malignant Neoplasms; Malignant neoplasm of brain; Malignant neoplasm of esophagus; Malignant neoplasm of liver; Measures; Mediating; Mediator of activation protein; Modality; Modeling; Outcome; Patient risk; Patients; Pattern; Phenotype; Photons; Pre-Clinical Model; Predictive Factor; Predisposition; Process; Prognostic Factor; Protons; Radiation; Radiation Dose Unit; Radiation Tolerance; Radiation therapy; Radiation-Induced Cancer; Recurrence; Regimen; Research; Risk; Role; Selection for Treatments; Severities; Site; Solid Neoplasm; Structure; T cell clonality; T-Cell Receptor; T-Lymphocyte; T-cell diversity; Techniques; Testing; Time; Treatment outcome; Tumor Immunity; Tumor Volume; Vaccination; Work; X-Ray Therapy; base; cancer therapy; cancer type; clinical practice; clinical translation; clinically relevant; clinically significant; cytotoxic; cytotoxicity; evidence base; experimental study; functional status; high risk; immunological diversity; immunological status; improved; improved outcome; individual patient; optimal treatments; patient subsets; personalized approach; personalized care; personalized predictions; personalized strategies; population based; predictive modeling; preservation; prospective; proton therapy; radiation risk; risk mitigation; side effect; treatment optimization; treatment planning; tumor

Project 2 - Summary Radiation-Induced Lymphopenia: Understanding, Predictive Modeling and Developing Photon and Proton-Based Mitigation Strategies There is accumulating evidence across many types of cancers that radiation-induced lymphopenia (RIL) is common, but it is often ignored as an unavoidable side effect. Severe RIL has been shown to correlate with poor disease-specific outcomes. Extensive use of radiotherapy (RT) in the curative management of solid tumors necessitates the development of RIL-mitigation strategies. We have compelling evidence of significant differences in the lymphocyte-sparing effects of proton therapy (PT) vs. photon (or x-ray) therapy (XRT), presumably attributable to the differences in their dose distribution patterns. Our work has further demonstrated that both patient-specific and dosimetric factors contribute to the risk of severe RIL and T-cell clonality. Our hypotheses are as follows: (1) RIL predictive models that account for individual patient susceptibilities and dosimetric factors will have clinically significant predictive power; (2) reducing dose to circulating immune cells and immune structures at risk preserves not only the quantity but, more importantly, the quality of lymphocytes, which has a direct positive impact on cancer immunity and disease outcomes; (3) through the utilization of intensity-modulated proton and photon RT (IMPT and IMRT), employing individualized dosimetric constraints derived from the models, we will be able to select the optimum treatment modality (protons or photons) and develop patient-specific strategies to substantially mitigate RIL and its consequences. To test these hypotheses, we propose three specific aims. In Aim 1, we will utilize our large databases of mainly esophagus, liver and brain cancer patients to improve our understanding of lymphocyte depletion as a function of dosimetric and patient-specific baseline clinical factors and develop models to accurately predict individualized severe RIL risk. In Aim 2, we will evaluate the clinical impact of the radiation treatment modality on T-cell diversity, immune repertoire, and functional immune status. We will test the hypothesis that the quality of lymphocytes as measured by immune phenotyping, T-cell diversity, and functional immunity after RT is a major driver of clinical outcomes rather than just the absolute lymphocyte count. In Aim 3, we will assess the validity of our models using independent retrospective and prospective data. We will also apply the models to select the optimum treatment modality and technique for a given patient and define the personalized dosimetric constraints to be used to optimize proton and photon radiation dose distribution patterns to minimize RIL severity and risk. Upon the completion of this project, we will have a better understanding of how the baseline clinical characteristics and proton and photon dosimetric factors impact RIL risk and severity, T-cell diversity, and functional immunity. Moreover, we will have developed advanced proton and photon dosimetric strategies to reduce RIL risk. Our research has the potential to better select the optimum modality for each patient, to optimize IMPT and IMRT treatments to maximally mitigate lymphopenia to improve RT outcomes, and to optimally integrate RT with immunotherapy strategies in the future.

项目2 -摘要 辐射诱发的淋巴细胞减少症：理解、预测建模和发展光子学， 基于质子的缓解策略 在许多类型的癌症中，有越来越多的证据表明，辐射诱导的淋巴细胞减少症（RIL）是一种恶性肿瘤。 它很常见，但往往被忽视，作为一个不可避免的副作用。严重的RIL已被证明与 疾病特异性结果较差。放射治疗（RT）在实体瘤治疗管理中的广泛应用 肿瘤的发展需要RIL缓解策略。我们有令人信服的证据 质子治疗（PT）与光子（或X射线）治疗（XRT）的淋巴细胞保留效果的差异， 这可能归因于它们的剂量分布模式的差异。我们的工作进一步 研究表明，患者特异性和剂量测定因素都有助于严重RIL和T细胞 克隆性我们的假设如下：（1）考虑个体患者的RIL预测模型 敏感性和剂量测定因素将具有临床显著的预测能力;（2）将剂量降低至 处于危险中的循环免疫细胞和免疫结构不仅保留了数量，更重要的是， 淋巴细胞的质量，这对癌症免疫和疾病结果有直接的积极影响;（3） 通过使用强度调制质子和光子RT（IMPT和IMRT），采用个体化 从模型中导出的剂量学约束，我们将能够选择最佳的治疗方式 （质子或光子），并制定患者特异性策略，以大大减轻RIL及其后果。 为了验证这些假设，我们提出了三个具体目标。在目标1中，我们将利用我们的大型数据库 主要是食道癌、肝癌和脑癌患者，以提高我们对淋巴细胞耗竭的认识， 剂量测定和患者特异性基线临床因素的功能，并开发模型以准确预测 个体化严重RIL风险。在目标2中，我们将评估放射治疗方式的临床影响 T细胞多样性、免疫库和功能性免疫状态。我们将测试假设， RT后通过免疫表型、T细胞多样性和功能性免疫测定的淋巴细胞质量 是临床结果的主要驱动因素，而不仅仅是绝对淋巴细胞计数。在目标3中，我们将评估 我们的模型使用独立的回顾性和前瞻性数据的有效性。我们还将应用模型 为给定患者选择最佳治疗方式和技术， 用于优化质子和光子辐射剂量分布模式的剂量学约束， RIL严重性和风险。当这个项目完成后，我们会更了解 基线临床特征以及质子和光子剂量测定因素影响RIL风险和严重程度，T细胞 多样性和功能性免疫。此外，我们还将开发先进的质子和光子剂量计， 降低RIL风险的策略。我们的研究有可能更好地选择每种最佳方式 患者，以优化IMPT和IMRT治疗，最大限度地缓解淋巴细胞减少症，改善RT结局， 并在未来将RT与免疫治疗策略进行最佳整合。