Department of Neurosurgery, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-Sen University Cancer Center, Guangzhou, China.
School of Mathematics, Sun Yat-Sen University, Guangzhou, China.
Brief Bioinform. 2020 May 21;21(3):1080-1097. doi: 10.1093/bib/bbz040.
Occurrence and development of cancers are governed by complex networks of interacting intercellular and intracellular signals. The technology of single-cell RNA sequencing (scRNA-seq) provides an unprecedented opportunity for dissecting the interplay between the cancer cells and the associated microenvironment. Here we combined scRNA-seq data with clinical bulk gene expression data to develop a computational pipeline for identifying the prognostic and predictive signature that connects cancer cells and microenvironmental cells. The pipeline was applied to glioma scRNA-seq data and revealed a tumor-associated microglia/macrophage-mediated EGFR/ERBB2 feedback-crosstalk signaling module, which was defined as a multilayer network biomarker (MNB) to predict survival outcome and therapeutic response of glioma patients. We used publicly available clinical data sets from large cohorts of glioma patients to examine the prognostic significance and predictive accuracy of the MNB, which outperformed conventional gene biomarkers and other methods. Additionally, the MNB was found to be predictive of the sensitivity or resistance of glioma patients to molecularly targeted therapeutics. Moreover, the MNB was an independent and the strongest prognostic factor when adjusted for clinicopathologic risk factors and other existing gene signatures. The robustness of the MNB was further tested on additional data sets. Our study presents a promising scRNA-seq transcriptome-based multilayer network approach to elucidate the interactions between tumor cell and tumor-associated microenvironment and to identify prognostic and predictive signatures of cancer patients. The proposed MNB method may facilitate the design of more effective biomarkers for predicting prognosis and therapeutic resistance of cancer patients.
癌症的发生和发展受细胞间和细胞内相互作用的复杂信号网络调控。单细胞 RNA 测序 (scRNA-seq) 技术为剖析癌细胞与相关微环境之间的相互作用提供了前所未有的机会。在这里，我们将 scRNA-seq 数据与临床批量基因表达数据相结合，开发了一种用于识别连接癌细胞和微环境细胞的预后和预测特征的计算管道。该管道应用于胶质母细胞瘤 scRNA-seq 数据，揭示了一个肿瘤相关的小胶质细胞/巨噬细胞介导的 EGFR/ERBB2 反馈交叉信号模块，该模块被定义为一个多层网络生物标志物 (MNB)，用于预测胶质母细胞瘤患者的生存结果和治疗反应。我们使用来自大量胶质母细胞瘤患者的公开临床数据集来检查 MNB 的预后意义和预测准确性，该准确性优于传统的基因生物标志物和其他方法。此外，MNB 被发现可预测胶质母细胞瘤患者对分子靶向治疗的敏感性或耐药性。此外，当调整临床病理危险因素和其他现有基因特征时，MNB 是独立的、最强的预后因素。MNB 的稳健性在其他数据集上进一步进行了测试。我们的研究提出了一种有前途的基于 scRNA-seq 转录组的多层网络方法，用于阐明肿瘤细胞与肿瘤相关微环境之间的相互作用，并识别癌症患者的预后和预测特征。所提出的 MNB 方法可能有助于设计更有效的生物标志物，以预测癌症患者的预后和治疗耐药性。
