GLIOMATCH: The malignant Glioma immuno-oncology matchmaker: towards data-driven precision medicine using spatially resolved radio-multiomics

GLIOMATCH：恶性胶质瘤免疫肿瘤学媒人：利用空间分辨的放射多组学实现数据驱动的精准医学

• 批准号: 10113516
• 金额: $ 65.57万
• 依托单位: UNIVERSITY OF EDINBURGH
• 依托单位国家: 英国
• 项目类别: EU-Funded
• 财政年份: 2024
• 资助国家: 英国
• 起止时间: 2024 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=10113516
• 关键词: GLIOMATCH; malignant; Glioma; immuno; oncology

Adult and paediatric malignant glioma (GBM and pHGG) remain among the most difficult-to-treat cancers with 5-year survival ratesof <5% despite intensive standard-of-care therapy. The differences among patients and the heterogeneous and plastic nature of eachindividual tumour have resulted in all therapeutic clinical trials failing during the past 20 years. Recently, immunotherapy has beenshowing great promise, but only in subsets of patients. Identifying those patients cannot be done a priori as biomarkers are still largelymissing, nor are we able to follow-up on therapeutic efficacy when patients get treated. The GLIOMATCH project aims at improvingthe clinical outcome of GBM/pHGG patients by enabling immunology-based patient stratification to empower personalised matchingof appropriate immunotherapy, while improving follow-up of clinical responses to existing/novel therapeutics. This will be achievedby integrating spatially resolved, multi-layered tissue maps (using integrated single-cell multiomics), with non-invasive MRI images.This integration will fuel into a novel MRI Radio-multiomics hub, that will be made available to clinical professionals through whichthey can perform tumour-host based patient stratification and personalised therapy matching while interpreting longitudinal follow-upand treatment efficacy. The proposed data-driven models will be developed by analysing the largest cohort of immuno-oncology (I/O)treated GBM/pHGG patients (n>300, including pre-post treatment samples) with matched controls (n>300) and exceptionally long-termsurviving GBM patients (n~140), in which various tumour-host niches will be studied in how they respond to I/O perturbations andlead to improved clinical outcome. This will be empowered by deploying an UNCAN-compatible data lake, to which incremental datacollection will be used to further refine the machine learning models, while proposing novel treatment options. This action is part of theCancer Mission cluster of projects on “Understanding (tumour-host interactions)".

尽管进行了强化标准护理治疗，成人和儿童恶性胶质瘤（GBM 和 pHGG）仍然是最难治疗的癌症，其 5 年生存率仍低于 5%。患者之间的差异以及每个个体的异质性和可塑性。过去 20 年来，癌症导致所有治疗性临床试验都失败了，最近，免疫疗法显示出巨大的前景，但只能在部分患者中进行识别。生物标志物仍然很大程度上缺失，我们也无法在患者接受治疗时跟踪治疗效果。GLIOMATCH 项目旨在通过基于免疫学的患者分层来实现适当免疫治疗的个性化匹配，从而改善 GBM/pHGG 患者的临床结果。同时改善对现有/新型疗法的临床反应的随访。这将通过将空间分辨的多层组织图（使用集成的单细胞多组学）与非侵入性 MRI 图像相集成来实现。为新型 MRI 放射多组学中心提供燃料，临床专业人员可以通过该中心进行基于肿瘤宿主的患者分层和个性化治疗匹配，同时解释纵向随访和治疗效果。通过分析最大的免疫肿瘤学 (I/O) 治疗 GBM/pHGG 患者队列 (n>300，包括治疗前和治疗后样本) 以及匹配对照 (n>300) 和特殊情况来开发长期存活的 GBM 患者 (n~140)，其中将研究各种肿瘤宿主生态位如何响应 I/O 扰动并改善临床结果，这将通过部署与 UNCAN 兼容的数据湖来实现。增量数据收集将用于进一步完善机器学习模型，同时提出新颖的治疗方案，该行动是“理解（肿瘤与宿主相互作用）”项目的癌症使命集群的一部分。