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

手术应激和治疗诱导胶质母细胞瘤空间景观的转录重编程

• 批准号: 527529530
• 负责人: Professor Dr. Dieter-Henrik Heiland
• 金额: --
• 依托单位: Neurochirurgische Klinik
• 依托单位国家: 德国
• 项目类别: Research Grants
• 资助国家: 德国
• 项目状态: 未结题
• 来源: https://gepris.dfg.de/gepris/projekt/527529530?language=en
• 关键词: Surgical; stress; therapy; induced; transcriptional

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.

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，尽管临床前结果很有希望，但大多数临床试验都未能达到目标终点。迄今为止，对复发中的抵抗和重塑机制知之甚少。最近的研究使用高通量技术，如多组学或单细胞分析显示，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，新的见解组装的细胞相互作用出现。该项目计划研究胶质母细胞瘤在内在和外在影响下的空间转录结构的改变。通过应用空间分辨转录组分析，我们的目标是在各种治疗下表征胶质母细胞瘤。这不仅将概述治疗相关的影响，而且还将概述手术干预引起的变化。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使我们能够综合而不是孤立地考虑所获得的知识，并且还提供了将各种临床参数纳入其中的可能性。目标是模拟治疗下肿瘤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，并为治疗靶点提供新的线索。