A Simplified Integrated Molecular and Immunohistochemistry-based Algorithm Allows High Accuracy Prediction of Glioblastoma Transcriptional Subtypes

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The authors developed a novel simplified assay for glioblastoma transcriptional classification on formalin-fixed-paraffin-embedded tissue samples. On such dataset, immunohistochemical profiles, based on expression of a restricted panel of gene classifiers, were integrated by machine learning approach to generate a glioblastoma transcriptional signature based on protein quantification that allowed to efficiently assign transcriptional subgroups to an extended cohort. Correlations with both histopathological features and clinical outcome have been also performed... More of this in the Laboratory Investigation Blog.

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