Abstract
Molecular marker-based glioblastoma (GBM) subclassification is emerging as a key factor in personalized GBM treatment planning. Multiple genetic alterations, including methylation status and mutations, have been proposed in GBM subclassification. RNA-Sequence (RNA-Seq)-based molecular profiling of GBM is widely implemented and readily quantifiable. Machine learning (ML) algorithms have been reported as an applicable method that can consistently subgroup GBM. In this study, we systematically studied the applicability of the commonly used ML algorithms based on The Cancer Genome Atlas Glioblastoma Multiforme (TCGA-GBM) dataset and cross-validated in the Chinese Glioma Genome Atlas (CGGA) dataset. ML algorithms studied include Binomial and multinomial Logistic Regression, Linear discriminant analysis, Decision trees, K-Nearest Neighbors, Gaussian Naive Bayes, Support Vector Machines, Gradient Boosting, Voting Ensemble, Multi-Layer Perceptron. RNA-Seq data of 44 biomarkers were passed through the algorithms for performance evaluation. We found ML algorithms Support Vector Machines, Multi-Layer Perceptron s, and Voting Ensemble are best equipped in assigning GBM to correct molecular subgroups of GBM without histological studies.
Highlights
IntroductionA study carried out by Verhaak et al (2010) provided evidence of distinct and clinically relevant 4 subtypes of glioblastoma multiforme (GBM) distinguishable by genomic abnormalities [1]
A study carried out by Verhaak et al (2010) provided evidence of distinct and clinically relevant 4 subtypes of glioblastoma multiforme (GBM) distinguishable by genomic abnormalities [1]. These include a Mesenchymal subtype defined by NF1 mutation, a Classical subtype with notable EGFR abnormalities, a Proneural subtype typified by distinct PDGFRA and IDH1 events, and a Neural subtype defined by abnormal expressions of neuron markers such as NEFL [1, 2]
To identify the most appropriate models for GBM subtype classification, we evaluated the performance of these approaches as well as numerous other traditionally employed model types for comparison
Summary
A study carried out by Verhaak et al (2010) provided evidence of distinct and clinically relevant 4 subtypes of glioblastoma multiforme (GBM) distinguishable by genomic abnormalities [1] These include a Mesenchymal subtype defined by NF1 mutation, a Classical subtype with notable EGFR abnormalities, a Proneural subtype typified by distinct PDGFRA and IDH1 events, and a Neural subtype defined by abnormal expressions of neuron markers such as NEFL [1, 2]. Reliably identification of GBM subtypes is of exceptional importance for personalized GBM treatment Complicated genetic markers such as mutations and methylation identifying the individual subtype with high accuracy have been proposed [8]. To identify the most appropriate models for GBM subtype classification, we evaluated the performance of these approaches as well as numerous other traditionally employed model types for comparison
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