PO.CL12.01 · 临床研究
使用标志性基因集特征的新型胶质母细胞瘤亚型分类：不良预后与TP53下游通路的关联
A novel glioblastoma subtype classification using hallmark gene set signatures: Association between poor prognosis and TP53 downstream pathway
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摘要 Abstract
中文摘要
胶质母细胞瘤（GBM）是一种极具侵袭性和治疗抵抗性的原发性脑肿瘤，预后极差。尽管已提出多种分子亚型以改进诊断和治疗方法，但由于瘤内异质性导致的分类标准模糊以及临床相关性低，其向临床实践的转化仍然有限。本研究利用了DepMap数据库中GBM细胞系的RNA-seq数据，以及来自癌症基因组图谱（TCGA）、中国脑胶质瘤基因组图谱（CGGA）、PRJNA1051047和内部队列的bulk RNA-seq数据集。此外，还检查了与配对的内部RNA-seq样本相配的GeoMx DSP数据，以表征空间转录组模式。使用Hallmark单样本基因集富集分析（ssGSEA）模块评分，通过非负矩阵分解（NMF）一致性聚类将GBM细胞系分为两个新亚型。随后使用源自这些聚类的差异表达基因（DEG）特征，通过贝叶斯复合协变量预测（BCCP）模型，将来自四个独立队列的IDH野生型GBM样本分为两组——命名为CA（细胞因子活跃）和GA（生长活跃）亚型。CA亚型表现出较差的生存结局及肿瘤相关通路的富集升高，包括IL2-STAT5信号、凋亡和炎症反应。值得注意的是，TP53在CA组中成为一个突出的上游调控因子，与四个患者队列中多种精选数据集里10条TP53相关通路的ssGSEA模块评分升高相一致。GeoMx DSP分析进一步比较了归入CA和GA亚型的alpha-SMA、CD45和CD31注释感兴趣区域（AOIs）间的TP53表达和ssGSEA模块评分。虽然在任何AOI类别中TP53表达在两组间均无显著差异，但Hallmark TP53信号模块评分特异性地在CA亚型的alpha-SMA AOIs中升高，尤其是在血管周围区域。总的来说，我们识别出两个具有临床相关性的亚型，其中CA亚型与不良预后及TP53下游通路的显著失调密切相关。这些发现提示TP53可能在GBM的侵袭性中发挥潜在的重要作用。
查看英文原文 English abstract
Glioblastoma (GBM) is an extremely aggressive and treatment-resistant primary brain tumor with a markedly poor prognosis. Although various molecular subtypes have been proposed to improve diagnostic and therapeutic approaches, their translation into clinical practice remains limited due to ambiguous classification criteria resulting from intra-tumoral heterogeneity and low clinical relevance. In this study, RNA-seq data from GBM cell lines in the DepMap database and bulk RNA-seq datasets from The Cancer Genome Atlas (TCGA), the Chinese Glioma Genome Atlas (CGGA), PRJNA1051047, and in-house cohorts were utilized. Furthermore, GeoMx DSP data paired with matched in-house RNA-seq samples were examined to characterize spatial transcriptomic patterns. Using Hallmark single-sample Gene Set Enrichment Analysis (ssGSEA) module scores, GBM cell lines were divided into two new subtypes by Non-negative Matrix Factorization (NMF) consensus clustering. Differentially expressed gene (DEG) signatures derived from these clusters were then used to classify IDH-wildtype GBM samples from four independent cohorts into two groups-designated as CA (cytokine-active) and GA (growth-active) subtypes-via a Bayesian compound covariate prediction (BCCP) model. The CA subtype exhibited poorer survival outcomes and elevated enrichment of tumor-associated pathways, including IL2-STAT5 signaling, apoptosis, and inflammatory response. Notably, TP53 emerged as a prominent upstream regulator in the CA group, consistent with increased ssGSEA module scores of 10 TP53-related pathways in various curated datasets in four patient cohorts. GeoMx DSP analyses further compared TP53 expression and ssGSEA module scores across alpha-SMA, CD45, and CD31 annotated regions of interest (AOIs) categorized into CA and GA subtypes. While TP53 expression did not significantly differ between the two groups in any AOI category, the Hallmark TP53 signaling module score was specifically elevated in alpha-SMA AOIs of CA subtype, particularly around perivascular regions. Collectively, two clinically relevant subtypes were identified, with the CA subtype being strongly associated with poor prognosis and significant dysregulation of TP53 downstream pathways. These findings suggest that TP53 may play a potentially important role in the aggressiveness of GBM.
利益披露 Disclosure
Y. Kim, None..
J. Park, None..
J. Lim, None.