Shen Xilin, Wang Xiaoli, Shen Hongru, Feng Mengyao, Wu Dan, Yang Yichen, Li Yang, Yang Meng, Ji Wei, Wang Wei, Zhang Qiang, Song Fangfang, Liu Ben, Chen Kexin, Li Xiangchun
Tianjin Cancer Institute, National Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin Medical University Cancer Institute and Hospital, Tianjin Medical University, Tianjin, China.
Department of Obstetrics and Gynecology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
Front Oncol. 2021 Oct 15;11:734407. doi: 10.3389/fonc.2021.734407. eCollection 2021.
Brain tumor ranks as the most devastating cancer type. The complex tumor immune microenvironment prevents brain tumor from receiving therapeutic benefits. The purpose of this study was to stratify brain tumors based on their distinct immune infiltration signatures to facilitate better clinical decision making and prognosis prediction.
We developed a deep learning model to characterize immune infiltration from transcriptome. The developed model was applied to distill expression signatures of transcriptome of brain tumor samples. We performed molecular subtyping with the extracted expression signatures to unveil brain tumor subtypes. Computational methods, including gene set enrichment analysis, Kaplan-Meier survival and multivariate Cox regression analyses, were employed.
We identified two distinctive subtypes (i.e. C1/2) of brain tumor featured by distinct immune infiltration signatures. The C1 subtype is characterized by protective immune infiltration signatures, including high infiltration of CD8+ T cells and activation of . The C2 subtype has an extensive infiltration of tumor-associated macrophages and microglia, and was enriched with immune suppressive, wound-healing, and angiogenic signatures. The C1 subtype had significantly better prognosis as compared with C2 (Log-rank test, HR: 2.5, 95% CI: 2.2 - 2.7; = 8.2e-78). This difference remained statistically significant (multivariate Cox model, HR: 2.2, 95% CI: 1.7 - 2.9; = 3.7e-10) by taking into account age, gender, recurrent/secondary status at sampling time, tumor grade, histology, radio-chemotherapy, mutation, methylation, and co-deletion of 1p and 19q. This finding was validated in six datasets. The C2 subtype of glioblastoma patients with mutation has poor survival analogous to those without mutation (Log-rank test, adjusted = 0.8), while C1 has favorable prognosis as compared with glioblastoma of C2 subtype with mutation (Log-rank test, adjusted = 1.2e-3) or without mutation (Log-rank test, adjusted = 1.3e-6).
We identified two distinctive subtypes of brain tumor with different immune infiltration signatures, which might be helpful as an independent prognosticator for brain tumor.
脑肿瘤是最具毁灭性的癌症类型。复杂的肿瘤免疫微环境阻碍了脑肿瘤从治疗中获益。本研究的目的是根据脑肿瘤独特的免疫浸润特征进行分层，以促进更好的临床决策和预后预测。
我们开发了一种深度学习模型，用于从转录组中表征免疫浸润。将开发的模型应用于提取脑肿瘤样本转录组的表达特征。我们使用提取的表达特征进行分子亚型分析，以揭示脑肿瘤亚型。采用了包括基因集富集分析、Kaplan-Meier生存分析和多变量Cox回归分析在内的计算方法。
我们鉴定出脑肿瘤的两种不同亚型（即C1/2），其具有不同的免疫浸润特征。C1亚型的特征是具有保护性免疫浸润特征，包括CD8+T细胞的高浸润和……的激活。C2亚型有肿瘤相关巨噬细胞和小胶质细胞的广泛浸润，并富含免疫抑制、伤口愈合和血管生成特征。与C2相比，C1亚型的预后明显更好（对数秩检验，HR：2.5，95%CI：2.2 - 2.7；P = 8.2e-78）。在考虑年龄、性别、采样时的复发/继发状态、肿瘤分级、组织学、放化疗、……突变、……甲基化以及1p和19q的共缺失后，这种差异在统计学上仍然显著（多变量Cox模型，HR：2.2，95%CI：1.7 - 2.9；P = 3.7e-10）。这一发现在六个数据集中得到了验证。具有……突变的胶质母细胞瘤患者的C2亚型生存情况与无……突变的患者相似（对数秩检验，校正P = 0.8），而与具有……突变的C2亚型胶质母细胞瘤（对数秩检验，校正P = 1.2e-3）或无……突变的C2亚型胶质母细胞瘤（对数秩检验，校正P = 1.3e-6）相比，C1亚型具有良好的预后。
我们鉴定出具有不同免疫浸润特征的两种不同脑肿瘤亚型，这可能有助于作为脑肿瘤的独立预后指标。
