Subclassification of Newly Diagnosed XXXX through an Immunohistochemical Approach.

@entity1 @entity5 @entity470 @entity983 @entity2277 @entity9086 @entity361 @entity463
Molecular signatures in @entity463 ( @entity463 ) have been described that correlate with clinical outcome and response to therapy. The Proneural (PN) and @entity361 ( @entity361 ) signatures have been identified most consistently, but others including Classical (CLAS) have also been reported. The molecular signatures have been detected by array techniques at RNA and DNA level, but these methods are costly and cannot take into account individual contributions of different cells within a @entity5 . Therefore, the aim of this study was to investigate whether subclasses of newly diagnosed GBMs could be assessed and assigned by application of standard pathology laboratory procedures. 123 newly diagnosed GBMs were analyzed for the @entity5 cell expression of 23 pre-identified proteins and @entity470 amplification, together allowing for the subclassification of 65% of the @entity5 . Immunohistochemistry (IHC)-based profiling was found to be analogous to transcription-based profiling using a 9-gene transcriptional signature for PN and @entity361 subclasses. Based on these data a novel, minimal IHC-based scheme for subclass assignment for GBMs is proposed. Positive staining for IDH1R132H can be used for PN subclass assignment, high @entity470 expression for the CLAS subtype and a combined high expression of @entity983 , @entity2277 and/or @entity9086 for the @entity361 subclass. The application of the proposed scheme was evaluated in an independent @entity5 set, which resulted in similar subclass assignment rates as those observed in the training set. The IHC-based subclassification scheme proposed in this study therefore could provide very useful in future studies for stratification of individual @entity1 samples.
