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转录组图谱鉴定出髓母细胞瘤中两种未被识别的室管膜瘤亚型和新途径。

Transcriptomic landscape identifies two unrecognized ependymoma subtypes and novel pathways in medulloblastoma.

作者信息

Arora Sonali, Nuechterlein Nicholas, Jensen Matt, Glatzer Gregory, Sievers Philipp, Varadharajan Srinidhi, Korshunov Andrey, Sahm Felix, Mack Stephen C, Taylor Michael D, Holland Eric C

机构信息

Human Biology Division, Fred Hutchinson Cancer Center, Seattle, WA, USA. 2.

Neuropathology Unit, Surgical Neurology Branch, National Institute of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, MD, USA.

出版信息

bioRxiv. 2024 Oct 22:2024.10.21.619495. doi: 10.1101/2024.10.21.619495.

Abstract

Medulloblastoma and ependymoma are prevalent pediatric central nervous system tumors with significant molecular and clinical heterogeneity. We collected bulk RNA sequencing data from 888 medulloblastoma and 370 ependymoma tumors to establish a comprehensive reference landscape. Following rigorous batch effect correction, normalization, and dimensionality reduction, we constructed a unified landscape to explore gene expression, signaling pathways, gene fusions, and copy number variations. Our analysis revealed distinct clustering patterns, including two primary ependymoma compartments, EPN-E1 and EPN-E2, each with specific gene fusions and molecular signatures. In medulloblastoma, we achieved precise stratification of Group 3/4 tumors by subtype and in SHH tumors by patient age. Our landscape serves as a vital resource for identifying biomarkers, refining diagnoses, and enables the mapping of new patients' bulk RNA-seq data onto the reference framework to facilitate accurate disease subtype identification. The landscape is accessible via Oncoscape, an interactive platform, empowering global exploration and application.

摘要

髓母细胞瘤和室管膜瘤是常见的小儿中枢神经系统肿瘤,具有显著的分子和临床异质性。我们收集了888例髓母细胞瘤和370例室管膜瘤肿瘤的批量RNA测序数据,以建立一个全面的参考图谱。经过严格的批次效应校正、归一化和降维处理后,我们构建了一个统一的图谱,以探索基因表达、信号通路、基因融合和拷贝数变异。我们的分析揭示了不同的聚类模式,包括两个主要的室管膜瘤亚群,EPN-E1和EPN-E2,每个亚群都有特定的基因融合和分子特征。在髓母细胞瘤中,我们按亚型对3/4组肿瘤进行了精确分层,并按患者年龄对SHH肿瘤进行了分层。我们的图谱是识别生物标志物、完善诊断的重要资源,并能够将新患者的批量RNA-seq数据映射到参考框架上,以促进准确的疾病亚型识别。该图谱可通过交互式平台Oncoscape访问,支持全球范围内的探索和应用。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ef7d/12218622/4c9b88d0458b/nihpp-2024.10.21.619495v2-f0001.jpg

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