Determining optimal radiotherapy and immunotherapy combinations to enhance anti-tumour immunity

确定最佳放疗和免疫治疗组合以增强抗肿瘤免疫力

• 批准号: 2776450
• 金额: --
• 依托单位: University of Manchester
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2022
• 资助国家: 英国
• 起止时间: 2022 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2776450
• 关键词: Determining; optimal; radiotherapy; immunotherapy; combinations

Targeting the immune checkpoints of PD-1/PD-L1 and CTLA-4 has fundamentally changed our understanding of how some cancers may be treated, revealing that CD8+ tumour infiltrating lymphocyte (TIL) responses can be highly effective providing that key regulatory mechanisms are circumvented. However, despite the clinical success of these immunotherapies, the majority of patients fail to respond or develop resistance, meaning that extensive further refinement of these therapies is urgently needed. This can likely be achieved through targeting multiple immune checkpoints in combination with established interventions such as chemotherapy or radiotherapy. While an array of therapeutic combinations are now being trialled, there are a vast number of possible combinations and rationalising this remains a huge challenge. Furthermore we are lacking understanding of the dynamics of the TIL response and how this is regulated, blunting efforts to optimally target the critical pathways involved. Current studies using either patient samples or murine models assess tumour compartments in a single temporal snapshot and thus cannot address the fundamental questions of how different TILs change over time and within the tumour microenvironment. To overcome this, we have developed in vivo models that uniquely enable labelling of immune cells specifically within the tumour, thus distinguishing newly entering and resident populations and allowing the detailed assessment of how TILs change over time. This refined analysis facilitates detailed mechanistic understanding of how therapeutic interventions work in vivo. We have recently used these dynamic tumour models to dissect how anti-PD-L1 therapy impacts both newly recruited and retained CD8 T cells specifically within the tumour environment. In this project we will build on these initial studies and ask how best to use radiation and immunotherapy to drive anti-tumour immunity. To achieve this, we will bridge the research expertise of multiple leading research groups with further support from industrial experts (AstraZeneca). Together, we will investigate how radiotherapy and immune checkpoint therapies targeting PD-L1 and additional inhibitory receptors may be best combined in pre-clinical colorectal tumour models. The studentship will support development of extensive in vivo biology skills to track immune cell migration, and characterise immune cell populations and responses using a range of techniques (multi-parameter flow cytometry, RNA sequencing and transcriptomics) in the context of radiotherapy. Building on this, we will investigate how targeting immune checkpoints in combination with radiotherapy alters the tumour immune compartment with the ultimate aim of providing robust experimental evidence for optimal combinations of therapeutic interventions for cancer patients.

靶向PD-1/PD-L1和CTLA-4的免疫检查点从根本上改变了我们对某些癌症如何治疗的理解，揭示了CD8+肿瘤浸润淋巴细胞（TIL）反应可以非常有效，只要绕过关键的调节机制。然而，尽管这些免疫疗法在临床上取得了成功，但大多数患者没有反应或产生耐药性，这意味着迫切需要对这些疗法进行广泛的进一步改进。这可能通过靶向多个免疫检查点结合既定的干预措施（如化疗或放疗）来实现。虽然现在正在试验一系列治疗组合，但有大量可能的组合，合理化这仍然是一个巨大的挑战。此外，我们缺乏对TIL反应的动力学以及如何调节的了解，从而削弱了最佳靶向所涉及的关键途径的努力。目前使用患者样本或小鼠模型的研究在单个时间快照中评估肿瘤区室，因此无法解决不同TIL如何随时间和肿瘤微环境变化的基本问题。为了克服这一点，我们开发了体内模型，能够独特地标记肿瘤内的免疫细胞，从而区分新进入和居民群体，并详细评估TIL如何随时间变化。这种精细的分析有助于详细了解治疗干预如何在体内起作用。我们最近使用这些动态肿瘤模型来剖析抗PD-L1治疗如何影响肿瘤环境中新招募和保留的CD8 T细胞。在这个项目中，我们将建立在这些初步研究的基础上，并询问如何最好地使用放射和免疫疗法来驱动抗肿瘤免疫。为了实现这一目标，我们将在行业专家（阿斯利康）的进一步支持下，整合多个领先研究小组的研究专业知识。我们将共同研究如何在临床前结直肠肿瘤模型中最好地结合针对PD-L1和其他抑制性受体的放射治疗和免疫检查点疗法。该奖学金将支持广泛的体内生物学技能的发展，以跟踪免疫细胞迁移，并使用一系列技术（多参数流式细胞术，RNA测序和转录组学）在放射治疗的背景下检测免疫细胞群体和反应。在此基础上，我们将研究靶向免疫检查点与放射治疗相结合如何改变肿瘤免疫区室，最终目的是为癌症患者的最佳治疗干预组合提供强有力的实验证据。