Surgical stress- and therapy induced transcriptional reprogramming of the spatial landscape in glioblastoma

胶质母细胞瘤是中枢神经系统最常见的恶性疾病。在过去的十年中，尽管临床前结果令人振奋，但大多数临床试验都未能达到目标终点。到目前为止，对复发的抵抗和重塑的机制知之甚少。最近使用多组或单细胞分析等高通量技术的研究表明，肿瘤只有轻微的变化。我的研究小组专注于细胞、代谢和转录水平的空间变化，以了解肿瘤的复杂相互作用。考虑到空间关系，出现了对细胞相互作用组装的新见解。这项拟议的项目计划研究胶质母细胞瘤在内在和外在影响下空间转录结构的变化。通过应用空间分辨转录组分析，我们的目标是在不同治疗方法下确定胶质母细胞瘤的特征。这不仅将概述与治疗相关的效果，而且还将概述由于手术干预而产生的变化。我们假设肿瘤切除和邻近组织损伤在肿瘤微环境的免疫重建中起重要作用。使用人工智能的整体方法应该允许我们综合考虑所获得的知识，而不是孤立的，并另外提供纳入各种临床参数的可能性。目标是模拟肿瘤在治疗过程中的变化，并预测潜在的反应。我们的项目有望提供方向性的见解，可能有助于个性化未来的治疗计划，并为治疗目标提供新的线索。

Glioblastomas are the most common malignant disease of the central nervous system. In the last decade, the majority of all clinical trials failed to achieve their targeted endpoint despite promising preclinical results. The mechanisms of resistance and remodeling in relapse are poorly understood to date. Recent studies using high-throughput techniques such as multi-omic or single-cell analysis have shown only marginal changes in the tumor. My research group is intensively focused on spatial changes at the cellular, metabolic and transcriptional level to understand the complex interactions of the tumor. Considering the spatial relationships, novel insights into the assembly of cellular interactions emerge. The proposed project plans to investigate the alteration of the spatial transcriptional architecture of glioblastomas under intrinsic and extrinsic influences. By applying spatially resolved transcriptome analysis we aim to characterize glioblastomas under various therapies. This will outline not only therapy related effects, but also the changes due to surgical intervention. We hypothesize that tumor resection and adjacent tissue injury play a significant role in the immunological reformation of the tumor microenvironment. A holistic approach using artificial intelligence should allow us to consider the gained knowledge integratively rather than isolated and additionally provide the possibility to incorporate a wide variety of clinical parameters. The goal is to model changes of the tumor under therapy and to predict potential response. Our project is expected to provide directional insights that may help to personalize future therapy planning and provide new clues to therapeutic targets.