Deep learning identified glioblastoma subtypes based on internal genomic expression ranks

• Published in: BMC cancer Vol. 22; no. 1; p. 86
• Main Authors: Mao, Xing-Gang, Xue, Xiao-Yan, Wang, Ling, Lin, Wei, Zhang, Xiang
• Format: Journal Article
• Language: English
• Published: England BioMed Central Ltd 20.01.2022; BioMed Central; BMC
• Subjects: Algorithms; Analysis; Brain cancer; Classical; Classification; Databases, Genetic; Datasets; Deep Learning; Deep neural network; Diagnosis; Gene expression; Gene Expression Profiling - methods; Gene Expression Regulation, Neoplastic; Genetic aspects; Genomics; Glioblastoma; Glioblastoma - classification; Glioblastoma multiforme; Health aspects; Humans; Machine learning; Mesenchymal; Mesenchyme; Neural; Neural networks; Neural Networks, Computer; Normal Distribution; Proneural; Risk factors; Statistical analysis; Tumors; China; Support vector machines; Deep neural network; Glioma; Machine learning; Classical; Molecular subtype; Proneural; Artificial intelligence; Mesenchymal; Neural
• ISSN: 1471-2407; 1471-2407
• DOI: 10.1186/s12885-022-09191-2

Glioblastoma (GBM) can be divided into subtypes according to their genomic features, including Proneural (PN), Neural (NE), Classical (CL) and Mesenchymal (ME). However, it is a difficult task to unify various genomic expression profiles which were standardized with various procedures from different studies and to manually classify a given GBM sample into a subtype. An algorithm was developed to unify the genomic profiles of GBM samples into a standardized normal distribution (SND), based on their internal expression ranks. Deep neural networks (DNN) and convolutional DNN (CDNN) models were trained on original and SND data. In addition, expanded SND data by combining various The Cancer Genome Atlas (TCGA) datasets were used to improve the robustness and generalization capacity of the CDNN models. The SND data kept unimodal distribution similar to their original data, and also kept the internal expression ranks of all genes for each sample. CDNN models trained on the SND data showed significantly higher accuracy compared to DNN and CDNN models trained on primary expression data. Interestingly, the CDNN models classified the NE subtype with the lowest accuracy in the GBM datasets, expanded datasets and in IDH wide type GBMs, consistent with the recent studies that NE subtype should be excluded. Furthermore, the CDNN models also recognized independent GBM datasets, even with small set of genomic expressions. The GBM expression profiles can be transformed into unified SND data, which can be used to train CDNN models with high accuracy and generalization capacity. These models suggested NE subtype may be not compatible with the 4 subtypes classification system.