Single cell characterisation of the mucosal microenvironment in Stomach Inflammation and Related Epithelial Neoplasia (SIREN)

胃癌是全球癌症相关死亡的第二大常见原因。在大多数患者中，诊断是在疾病晚期确定的，此时只能提供有限的姑息治疗，导致五年生存率约为25%。这些肿瘤大多是由胃粘膜的慢性炎症引起的。我们知道在真正的癌症发生之前有一系列的前驱病变。国家指南最近引入了对这些情况的患者进行内窥镜监测，以便早期发现胃癌，但我们离可靠的个体风险预测还很远。引起胃粘膜炎症的主要因素是幽门螺杆菌感染。这种感染通常在婴儿期感染，并在患者一生中持续存在。在许多患者中，它不会引起任何问题，但在其他患者中，由感染引起的免疫反应是癌症进一步发展的关键因素。有人认为，调节局部免疫反应的因素决定了个体癌症发展的风险。这涉及到患者和细菌（如果存在）的遗传因素，胃粘膜中存在的免疫细胞的组成和类型，以及免疫细胞与胃壁其他细胞成分（包括脂肪细胞和粘膜表面细胞）的细胞间相互作用，这些细胞维持着抵抗有害物质（即所谓的致癌物）的实际屏障功能。这些元素之间的良好平衡可能会被某些药物如酸阻滞剂、阿司匹林或他汀类药物进一步扰乱。这项研究的主要目的是生成不同状态的胃炎症的细胞图谱，包括癌前状态。这将通过所谓的单细胞测序方法来实现，该方法允许分析单个细胞类型和细胞群体产生的信使分子。这将有助于了解胃炎症在癌变过程中的功能细胞-细胞相互作用，以及胃内膜内不同细胞类型的结构层次。我们将选择具有不同阶段胃部炎症的特定患者，这些患者暴露于不同的胃癌危险因素中，主要关注幽门螺杆菌引起的炎症。我们将使用现代机器学习方法来生成一个生物信息学模型，该模型将允许预测协调个体免疫反应的因素。这将进一步促进生物标志物的建立，以更精确地预测个体风险，从而实现胃癌的早期发现和预防。这包括产生基于血液的标记物，以识别需要内窥镜检查的肿瘤前病变患者，以及组织病理学标记物，以识别那些有癌症进展高风险的患者。从胃细胞图谱生成的数据中确定的候选生物标志物将在更广泛的患者队列中进行验证，以确认在常规临床环境中应用的可行性。同样，我们寻找可用于开发旨在预防胃炎进一步发展为癌症的药物的靶标。申请人及其合作伙伴的综合专业知识将允许对慢性幽门螺杆菌感染对胃局部免疫反应缺陷的贡献进行精确定义，确定区分疾病不同阶段的关键因素，从表面炎症到癌前状态再到胃癌。这种对健康、炎症、癌症前期和癌症中胃局部免疫系统的多模态单细胞分析尚未进行。

Stomach cancer is the second most common cause of cancer related death worldwide. In most patients, the diagnosis is established at the stage of advanced disease, when only limited, palliative treatment can be offered resulting in five-year survival rates of around 25%. The majority of these tumours are driven by chronic inflammation of the stomach lining. We know about a sequence of precursor lesions that are often present prior to the actual cancer. National guidelines have recently introduced endoscopic surveillance for patients with these conditions to allow early detection of stomach cancer, but we are still far from reliable individual risk prediction. The main factor causing inflammation of the stomach lining is infection with the bacteria Helicobacter pylori (H. pylori). This infection is usually contracted in infanthood and persists throughout the patient's lifetime. In many patients it does not cause any problems, but in others the immune response caused by the infection is the key factor for further cancer development. It has been suggested that factors that modulate the local immune-response dictate the individual risk for cancer development. This involves genetic factors of both the patient and the bacteria (if present), the composition and type of immune cells present in the stomach lining, and cell-cell interaction of the immune cells with other cellular components of the stomach wall, also including fat cells and surface cells of the lining which maintain the actual barrier function against harmful agents, so-called carcinogens. The fine balance between these elements can be further disturbed by certain medication such as acid blockers, aspirin or statins. The key objective of this study is the generation of a cell atlas of different states of stomach inflammation, including pre-cancerous conditions. This will be achieved by so-called single cell sequencing methods that allow the analysis of messenger molecules produced by individual cell types and cell populations. This will help to gain knowledge on the functional cell-cell-interaction in stomach inflammation on its path to cancer and also on the structural hierarchy of different cell types within the lining of the stomach. We will select specific patients with distinct stages of inflammation in the stomach who have been exposed to different risk factors for gastric cancer, with the main focus on inflammation caused by H. pylori. We will use modern machine learning approaches to generate a bioinformatics model that will allow the prediction of factors that orchestrate the individual immune response. This will further facilitate the establishment of biomarkers for a more precise individual risk prediction with the aim of both early detection and prevention of gastric cancer. This includes the generation of blood-based markers to identify patients with preneoplastic conditions requiring endoscopy, as well as histopathology markers to identify those patients with a high risk of progression towards cancer. Candidate biomarkers identified from the data generated for the stomach cell atlas will be validated in a wider cohort of patients to confirm the feasibility of application in a routine clinical setting. Similarly, we look for targets that can be utilised for the development of drugs that aim at the prevention of further progression of stomach inflammation towards cancer. The combined expertise of the applicant and his partners will allow a precise definition of the contribution of chronic H. pylori infection to a local defective immune response in the stomach, identifying key factors that discriminate the different stages of disease, leading from surface inflammation via pre-cancerous conditions to stomach cancer. Such a multi-modal single cell analysis of the local immune system of the stomach in health, inflammation, pre-cancer and cancer has not yet been undertaken.