Radiation Effect on Immune Cells and the Microbiome

摘要 在过去的几十年里，我们加深了对基础的分子机制的理解 RT的有效性，并在提高RT的效率同时减少副作用方面也取得了进展 与对附近正常组织的损伤有关。然而，全面了解RT是如何在 一个给定的解剖领域，明显改变癌细胞、正常组织和渗透的免疫细胞，基于 每个细胞的独特性仍未解决。此外，也是重要的是，这些组合的RT依赖效应是如何 不同细胞类型的融合对治疗结果的影响仍然知之甚少。这很好 认识到免疫健康对控制癌症的生长和进展很重要；因此，任何治疗方法 默认情况下，受损的免疫细胞会损害免疫系统控制恶性细胞的能力。 虽然项目1专注于结直肠癌肿瘤和肿瘤微环境对放射治疗的反应，在 本项目(项目2)，我们关注RT对免疫细胞和微生物群的影响。 项目2的总体假设是免疫系统是癌症反应的重要组成部分 RT诱导的先天和获得性免疫反应是限制肿瘤进展的关键。 然而，RT的这些免疫学益处被RT对免疫细胞的有害影响所抵消。 健康和生存，随之而来的是白细胞减少和免疫功能障碍。因此，量化的理解 直接或间接(作为旁观者)受影响的免疫细胞的百分比和亚群 RT诱导的损伤是完全了解RT在癌症治疗结果中的作用的基础。 在项目2中，首先，我们将量化外周血单个核细胞(PBMC)的百分比，以及 位于放射野内外的受RT影响的淋巴结内的免疫细胞。第二， 我们将通过计算在单细胞水平上累积的RT暴露来量化RT诱导的损害 以及相关的细胞反应(即细胞凋亡、内质网线粒体应激、炎症通路的激活、 免疫学适合性)。使用单细胞RNA测序和空间转录，我们将创造一条道路 RT对不同免疫细胞类型(T和B细胞亚群，树突状细胞， 单核细胞、巨噬细胞、NK细胞)与放射剂量暴露的关系。此外，我们还将分析定性的 以及RT前后采集的样本中微生物组的数量变化来推断RT如何介导 肠道微生物群的变化可能会影响免疫反应。在项目2的第二部分中，一台机器 将利用学习方法来整合所有获得的数据，以制定更全面的观点 RT如何影响免疫反应和治疗结果。

SUMMARY Over the last decades, we have enhanced our understanding of the molecular mechanisms that underlie the efficacy of RT and have also made progress to improve the efficiency of RT while reducing side effects associated with damage to nearby normal tissues. Nevertheless, a comprehensive understanding of how RT, in a given anatomical field, distinctively alters cancer cells, normal tissue, and infiltrating immune cells based on each cell’s “uniqueness" is still unresolved. Further, and importantly, how these combined RT-dependent effects on different cell types converge to influence therapeutic outcomes remains poorly understood. It is well recognized that immune fitness is important in controlling cancer growth and progression; as such, any therapy compromising immune cells, by default, can compromise the immune system’s ability to control malignant cells. Whereas Project 1 focuses on the tumor and tumor microenvironment response to RT in colorectal cancer, in this project (Project 2), we focus on the impact of RT on immune cells and the microbiome. The overall hypothesis of Project 2 is that the immune system is an important component of the cancer response to RT and that innate and adaptive immune responses elicited by RT are pivotal to restrict cancer progression. However, these immunological benefits of RT are counterbalanced by deleterious effects of RT on immune cell fitness and survival with consequent leukopenia and immune dysfunction. As such, a quantitative understanding of the percentage and sub-populations of immune cells that are directly or indirectly (as bystanders) affected by RT-induced damage is fundamental to completely understand the role of RT in cancer treatment outcomes. In Project 2, firstly, we will quantify the percentage of peripheral blood mononuclear cells (PBMC), as well as immune cells within lymph nodes located inside or outside the radiation field that are affected by RT. Secondly, we will quantify the RT-induced damage by calculating both the accumulated RT exposure at the single cell level and related cellular responses (i.e., apoptosis, ER mitochondrial stress, activation of inflammatory pathways, immunological fitness). Using single cell RNA-sequencing and spatial transcriptomics, we will generate a road map of the effects of RT on the different immune cell types (T and B cell subpopulations, dendritic cells, monocytes, macrophages, NK cells) in relation to RT dose exposure. Additionally, we will analyze qualitative and quantitative changes in the microbiome in samples collected before and after RT to infer how RT-mediated changes in the intestinal microbiome could affect immune responses. In the second part of Project 2, a machine learning approach will be utilized to integrate all of the acquired data to develop a more comprehensive view of how RT influences immune responses and treatment outcomes.