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TCGA Cancer Subtype Assignment Of Patient Samples Using Compact Feature Sets

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Molecular subtypes have previously been defined for cancer cohorts within The Cancer Genome Atlas (TCGA), using different classification approaches and genomic platform technologies. Here we address how a newly diagnosed tumor might be efficiently profiled using a limited set of molecular markers, and its subtype membership identified relative to previously characterized TCGA cancers. We describe results using five different machine-learning approaches applied to multi-omic data for 8,791 TCGA cancers comprising 26 different types and 106 subtypes, to derive classification models using parsimonious gene feature sets that support the prediction of a given sample’s molecular subtype. We compare the predictive accuracy of the diverse approaches and compare their associated features, revealing insights into how different single or multi-omic data platforms can be used to predict cancer molecular subtypes. We found that 70 samples are sufficient to derive an estimate of classification accuracy for a prospectively accruing cancer cohort.

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