Sussman Jonathan H, Xu Jason, Amankulor Nduka, Tan Kai
Graduate Group in Genomics and Computational Biology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA.
Medical Scientist Training Program, University of Pennsylvania, Philadelphia, Pennsylvania, USA.
Neurooncol Adv. 2023 Aug 21;5(1):vdad101. doi: 10.1093/noajnl/vdad101. eCollection 2023 Jan-Dec.
Malignant gliomas are incurable brain neoplasms with dismal prognoses and near-universal fatality, with minimal therapeutic progress despite billions of dollars invested in research and clinical trials over the last 2 decades. Many glioma studies have utilized disparate histologic and genomic platforms to characterize the stunning genomic, transcriptomic, and immunologic heterogeneity found in gliomas. Single-cell and spatial omics technologies enable unprecedented characterization of heterogeneity in solid malignancies and provide a granular annotation of transcriptional, epigenetic, and microenvironmental states with limited resected tissue. Heterogeneity in gliomas may be defined, at the broadest levels, by tumors ostensibly driven by epigenetic alterations (IDH- and histone-mutant) versus non-epigenetic tumors (IDH-wild type). Epigenetically driven tumors are defined by remarkable transcriptional programs, immunologically distinct microenvironments, and incompletely understood topography (unique cellular neighborhoods and cell-cell interactions). Thus, these tumors are the ideal substrate for single-cell multiomic technologies to disentangle the complex intra-tumoral features, including differentiation trajectories, tumor-immune cell interactions, and chromatin dysregulation. The current review summarizes the applications of single-cell multiomics to existing datasets of epigenetically driven glioma. More importantly, we discuss future capabilities and applications of novel multiomic strategies to answer outstanding questions, enable the development of potent therapeutic strategies, and improve personalized diagnostics and treatment via digital pathology.
恶性胶质瘤是无法治愈的脑肿瘤，预后极差，几乎无一例外会导致死亡。尽管在过去20年里在研究和临床试验上投入了数十亿美元，但治疗进展甚微。许多胶质瘤研究利用了不同的组织学和基因组平台来描述胶质瘤中惊人的基因组、转录组和免疫异质性。单细胞和空间组学技术能够以前所未有的方式描述实体恶性肿瘤中的异质性，并在有限的切除组织中对转录、表观遗传和微环境状态进行精细注释。从最广泛的层面来看，胶质瘤的异质性可以由表观遗传改变驱动的肿瘤（异柠檬酸脱氢酶和组蛋白突变型）与非表观遗传肿瘤（异柠檬酸脱氢酶野生型）来界定。表观遗传驱动的肿瘤具有显著的转录程序、免疫上不同的微环境以及尚未完全了解的拓扑结构（独特的细胞邻域和细胞间相互作用）。因此，这些肿瘤是单细胞多组学技术解开复杂的肿瘤内特征（包括分化轨迹、肿瘤-免疫细胞相互作用和染色质失调）的理想对象。本综述总结了单细胞多组学在表观遗传驱动的胶质瘤现有数据集上的应用。更重要的是，我们讨论了新型多组学策略未来的能力和应用，以回答悬而未决的问题，推动有效治疗策略的发展，并通过数字病理学改善个性化诊断和治疗。
