A Deep Learning–Based Framework for Supporting Clinical Diagnosis of Glioblastoma Subtypes

Abstract

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.

Immune landscape-based machine-learning–assisted subclassification, prognosis, and immunotherapy prediction for glioblastoma

“…In addition, there have been existing ML models for GBM subtype classification based on different data; for example, Munquad et al. ( 84 ) utilized transcriptome and methylome data to construct classifiers through several ML algorithms, and the best model presents an accuracy of 87.5% on the testing data and 94.48% on external data. Macyszyn et al.…”

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“…40 Another study focused on subtype-specific predictive biomarker discovery, applicable to disease diagnosis and treatment. 41 Scmap employs a graph-based clustering technique to assess the maximum similarity between cells in both reference and query data, enabling…”

“…To address the challenge of identifying biomarkers in every cell cluster, a framework based on regularized multitask learning was developed for simultaneous prediction of subpopulations related to specified cell types 40 . Another study focused on subtype‐specific predictive biomarker discovery, applicable to disease diagnosis and treatment 41 . Scmap employs a graph‐based clustering technique to assess the maximum similarity between cells in both reference and query data, enabling the identification of distinct clusters corresponding to different cell types 42 .…”

Uncovering the subtype-specific disease module and the development of drug response prediction models for glioma

“…Each subtype has distinct molecular features, and they can be classified using genomics and epigenomics profiles. Despite the presence of intratumor molecular heterogeneity, recent research has shown that deep learning (DL) and machine learning (ML)-based methods may accurately identify glioma subtypes [ 6 , 7 ]. Due to distinct molecular characteristics, the subtypes of glioma have different clinical outcomes and responses to treatment, highlighting the importance of personalized medicine for brain cancer treatment [ 8 ].…”