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CMTC is an independent prognostic predictor, and it outperformed 12 other known prognostic gene signatures, molecular subtype classifications, and all other standard prognostic clinicopathological factors.
We show that standard gene centering produces inaccurate molecular subtype classifications when the clinicopathological distribution of the study cohort differs from that of the training cohort.
Although standard gene centering is commonly applied prior to molecular subtyping, it produces inaccurate molecular subtype classifications when the study and training cohorts differ in their clinicopathological composition.
For comparisons of molecular subtype classifications, we applied a commonly used classifier designed for single sample predictions, the 50-gene subtype classifier (PAM50) developed by Parker et al. [ 18].
However, standard gene centering introduces errors in molecular subtype classifications when the clinicopathological distributions of the study cohort do not match those of the training cohort used to derive the molecular subtype classifier.
In particular, if the training cohort is intended to capture the heterogeneity of the general patient population, standard gene centering will not produce molecular subtype classifications on a study cohort with more narrowly defined clinicopathological characteristics relative to the general population, such as a study cohort of only ER-positive cases.
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Despite the importance of molecular subtype classification of glioblastoma (GBM), the extent of extracellular vesicle (EV -driven molEV -driven phenotypic reprogramolecularands phenotypicereprogramming
Detailed clinical features and molecular subtype classification have been reported elsewhere [ 43, 44].
Below we show that our approach produces fewer errors for molecular subtype classification.
We performed molecular subtype classification using the 50-gene classifier described by Parker et al. [ 27].
Molecular subtype classification was carried out using PAM50 for each of the Oslo cohorts (Parker et al, 2009).
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