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Research Articles: Therapeutics, Targets, and Development

Improved grading and survival prediction of human astrocytic brain tumors by artificial neural network analysis of gene expression microarray data

¹ Division of Molecular Histopathology, Department of Pathology, University of Cambridge, Addenbrooke's Hospital, Cambridge, United Kingdom; ² Institute for Molecular Biology and Biotechnology, Foundation for Research and Technology-Hellas; ³ Graduate Program in Molecular Biology and Biomedicine, Department of Biology, University of Crete, Heraklion, Crete, Greece; ⁴ Cambridge Centre for Neuropsychiatric Research, University of Cambridge, Cambridge, United Kingdom; ⁵ Division of Pathway Medicine, College of Medicine, University of Edinburgh, Edinburgh, Scotland, United Kingdom; and ⁶ Department of Oncology-Pathology, Karolinska Hospital, Karolinska Institute, Stockholm, Sweden

Requests for reprints: Panayiota Poirazi, Institute for Molecular Biology and Biotechnology, Foundation for Research and Technology-Hellas, Vassilika Vouton, P.O. Box 1385, GR 711 10 Heraklion, Crete, Greece. E-mail: poirazi{at}imbb.forth.gr

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. [Mol Cancer Ther 2008;7(5):1013–24]

Grant support: Cancer Research UK, UK Medical Research Council, The Jacqueline Seroussi Memorial Foundation for Cancer Research, Samantha Dickson Research Trust, Ludwig Institute for Cancer Research, General Secretariat for Research and Technology, Hellas (project PENED 03ED842), and EMBO Young Investigator Program.

The costs of publication of this article were defrayed in part by the payment of page charges. This article must therefore be hereby marked advertisement in accordance with 18 U.S.C. Section 1734 solely to indicate this fact.

Note: L. P. Petalidis, A. Oulas, P. Poirazi, and V.P. Collins contributed equally to this work.

⁷ Supplementary material for this article is available at Molecular Cancer Therapeutics Online (http://mct.aacrjournals.org/).

⁸ The source code for the ANN and visualization methods is available from http://www.imbb.forth.gr/people/poirazi/software.html.

Received 3/13/07; revised 1/11/08; accepted 3/26/08.