Table5_A Deep Learning–Based Framework for Supporting Clinical Diagnosis of Glioblastoma Subtypes.PDF

Understanding molecular features that facilitate aggressive phenotypes in glioblastoma multiforme (GBM) remains a major clinical challenge. Accurate diagnosis of GBM subtypes, namely classical, proneural, and mesenchymal, and identification of specific molecular features are crucial for clinicians for systematic treatment. We develop a biologically interpretable and highly efficient deep learning framework based on a convolutional neural network for subtype identification. The classifiers were generated from high-throughput data of different molecular levels, i.e., transcriptome and methylome. Furthermore, an integrated subsystem of transcriptome and methylome data was also used to build the biologically relevant model. Our results show that deep learning model outperforms the traditional machine learning algorithms. Furthermore, to evaluate the biological and clinical applicability of the classification, we performed weighted gene correlation network analysis, gene set enrichment, and survival analysis of the feature genes. We identified the genotype–phenotype relationship of GBM subtypes and the subtype-specific predictive biomarkers for potential diagnosis and treatment.

阐明多形性胶质母细胞瘤（glioblastoma multiforme, GBM）中驱动侵袭性表型的分子特征，仍是当前临床面临的重大挑战。精准诊断多形性胶质母细胞瘤的经典型、原神经型及间质型亚型，并明确其特异性分子特征，对于临床医生制定系统化治疗方案至关重要。本研究开发了一种基于卷积神经网络（convolutional neural network, CNN）、具备生物学可解释性且高效的深度学习框架，用于多形性胶质母细胞瘤的亚型分型。该分类模型基于不同分子层面的高通量数据构建，涵盖转录组（transcriptome）与甲基化组（methylome）两类数据。此外，本研究还整合转录组与甲基化组数据，构建了具备生物学相关性的模型。研究结果表明，本研究所用的深度学习模型性能优于传统机器学习算法。为评估该分型方法的生物学与临床适用性，本研究对特征基因开展了加权基因共表达网络分析、基因集富集分析及生存分析。本研究明确了多形性胶质母细胞瘤各亚型的基因型-表型关联，并筛选出可用于潜在诊疗的亚型特异性预测生物标志物（biomarker）。