Department of Neurosurgery, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100730, China.
Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100730, China.
Brief Bioinform. 2021 Sep 2;22(5). doi: 10.1093/bib/bbab032.
Glioblastoma (GBM) is the most malignant and lethal intracranial tumor, with extremely limited treatment options. Immunotherapy has been widely studied in GBM, but none can significantly prolong the overall survival (OS) of patients without selection. Considering that GBM cancer stem cells (CSCs) play a non-negligible role in tumorigenesis and chemoradiotherapy resistance, we proposed a novel stemness-based classification of GBM and screened out certain population more responsive to immunotherapy. The one-class logistic regression algorithm was used to calculate the stemness index (mRNAsi) of 518 GBM patients from The Cancer Genome Atlas (TCGA) database based on transcriptomics of GBM and pluripotent stem cells. Based on their stemness signature, GBM patients were divided into two subtypes via consensus clustering, and patients in Stemness Subtype I presented significantly better OS but poorer progression-free survival than Stemness Subtype II. Genomic variations revealed patients in Stemness Subtype I had higher somatic mutation loads and copy number alteration burdens. Additionally, two stemness subtypes had distinct tumor immune microenvironment patterns. Tumor Immune Dysfunction and Exclusion and subclass mapping analysis further demonstrated patients in Stemness Subtype I were more likely to respond to immunotherapy, especially anti-PD1 treatment. The pRRophetic algorithm also indicated patients in Stemness Subtype I were more resistant to temozolomide therapy. Finally, multiple machine learning algorithms were used to develop a 7-gene Stemness Subtype Predictor, which were further validated in two external independent GBM cohorts. This novel stemness-based classification could provide a promising prognostic predictor for GBM and may guide physicians in selecting potential responders for preferential use of immunotherapy.
胶质母细胞瘤（GBM）是最恶性和致命的颅内肿瘤，治疗选择极为有限。免疫疗法已在 GBM 中广泛研究，但在没有选择的情况下，没有一种方法能显著延长患者的总生存期（OS）。考虑到 GBM 癌症干细胞（CSC）在肿瘤发生和放化疗耐药中发挥着不可忽视的作用，我们提出了一种新的基于干性的 GBM 分类方法，并筛选出对免疫治疗更敏感的特定人群。我们使用单类逻辑回归算法，根据 GBM 和多能干细胞的转录组学，计算了来自癌症基因组图谱（TCGA）数据库的 518 名 GBM 患者的干性指数（mRNAsi）。基于其干性特征，通过共识聚类将 GBM 患者分为两个亚型，Stemness 亚型 I 的患者 OS 明显更好，但无进展生存期较差。基因组变异揭示，Stemness 亚型 I 的患者体细胞突变负荷和拷贝数改变负担更高。此外，两种干性亚型具有不同的肿瘤免疫微环境模式。肿瘤免疫功能障碍和排除以及亚类映射分析进一步表明，Stemness 亚型 I 的患者更有可能对免疫治疗，特别是抗 PD1 治疗产生反应。pRRophetic 算法还表明，Stemness 亚型 I 的患者对替莫唑胺治疗更耐药。最后，我们使用多种机器学习算法开发了一个 7 基因的 Stemness 亚型预测器，并在两个外部独立的 GBM 队列中进行了验证。这种新的基于干性的分类方法可为 GBM 提供有前途的预后预测指标，并可能指导医生选择潜在的反应者，优先使用免疫治疗。
