title: Improved grading and survival prediction of human astrocytic brain tumors by artificial neural network analysis of gene expression microarray data.
abstract: Histopathologic grading of astrocytic tumors based on current WHO criteria offers a valuable but simplified representation of oncologic reality and is often insufficient to predict clinical outcome. In this study, we report a new astrocytic tumor microarray gene expression data set (n = 65). We have used a simple artificial neural network algorithm to address grading of human astrocytic tumors, derive specific transcriptional signatures from histopathologic subtypes of astrocytic tumors, and asses whether these molecular signatures define survival prognostic subclasses. Fifty-nine classifier genes were identified and found to fall within three distinct functional classes, that is, angiogenesis, cell differentiation, and lower-grade astrocytic tumor discrimination. These gene classes were found to characterize three molecular tumor subtypes denoted ANGIO, INTER, and LOWER. Grading of samples using these subtypes agreed with prior histopathologic grading for both our data set (96.15%) and an independent data set. Six tumors were particularly challenging to diagnose histopathologically. We present an artificial neural network grading for these samples and offer an evidence-based interpretation of grading results using clinical metadata to substantiate findings. The prognostic value of the three identified tumor subtypes was found to outperform histopathologic grading as well as tumor subtypes reported in other studies, indicating a high survival prognostic potential for the 59 gene classifiers. Finally, 11 gene classifiers that differentiate between primary and secondary glioblastomas were also identified.
authors.affiliation: Division of Molecular Histopathology, Department of Pathology, University of Cambridge, Addenbrooke's Hospital, Cambridge, United Kingdom.
authors.forename: Lawrence P
authors.initials: LP
authors.lastname: Petalidis
authors.forename: Anastasis
authors.initials: A
authors.lastname: Oulas
authors.forename: Magnus
authors.initials: M
authors.lastname: Backlund
authors.forename: Matthew T
authors.initials: MT
authors.lastname: Wayland
authors.forename: Lu
authors.initials: L
authors.lastname: Liu
authors.forename: Karen
authors.initials: K
authors.lastname: Plant
authors.forename: Lisa
authors.initials: L
authors.lastname: Happerfield
authors.forename: Tom C
authors.initials: TC
authors.lastname: Freeman
authors.forename: Panayiota
authors.initials: P
authors.lastname: Poirazi
authors.forename: V Peter
authors.initials: VP
authors.lastname: Collins
mesh_terms: D000465:Algorithms; D051017:Apoptosis Regulatory Proteins; D001254:Astrocytoma / Q000145:classification / Q000175:diagnosis* / Q000235:genetics / Q000401:mortality*; D014408:Biomarkers, Tumor / Q000235:genetics; D001932:Brain Neoplasms / Q000145:classification / Q000175:diagnosis* / Q000235:genetics / Q000401:mortality*; D020869:Gene Expression Profiling / Q000379:methods*; D015972:Gene Expression Regulation, Neoplastic; D006801:Humans; D047908:Intracellular Signaling Peptides and Proteins / Q000235:genetics; D016571:Neural Networks, Computer; D020411:Oligonucleotide Array Sequence Analysis / Q000379:methods*; D010750:Phosphoproteins / Q000235:genetics; D011379:Prognosis; D015203:Reproducibility of Results; D015996:Survival Rate
chemical_list: D051017:Apoptosis Regulatory Proteins; D014408:Biomarkers, Tumor; D047908:Intracellular Signaling Peptides and Proteins; C092039:PEA15 protein, human; D010750:Phosphoproteins