Expression Signature of IFN/STAT1 Signaling Genes Predicts Poor Survival Outcome in Glioblastoma Multiforme in a Subtype-Specific Manner

Christine W Duarte,Tapan Mehta,Xiangqin Cui,Laura Kelly Vaughan,G Yancey Gillespie,Raymond O Mccubrey,Jacqueline J Harris,Nikolai N Khodarev,Degui Zhi,Christopher D Willey,Ralph R Weichselbaum,Ilya Ulasov

doi:10.1371/journal.pone.0029653

Christine W Duarte, Tapan Mehta + Show 10 more

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https://doi.org/10.1371/journal.pone.0029653

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Abstract

Previous reports have implicated an induction of genes in IFN/STAT1 (Interferon/STAT1) signaling in radiation resistant and prosurvival tumor phenotypes in a number of cancer cell lines, and we have hypothesized that upregulation of these genes may be predictive of poor survival outcome and/or treatment response in Glioblastoma Multiforme (GBM) patients. We have developed a list of 8 genes related to IFN/STAT1 that we hypothesize to be predictive of poor survival in GBM patients. Our working hypothesis that over-expression of this gene signature predicts poor survival outcome in GBM patients was confirmed, and in addition, it was demonstrated that the survival model was highly subtype-dependent, with strong dependence in the Proneural subtype and no detected dependence in the Classical and Mesenchymal subtypes. We developed a specific multi-gene survival model for the Proneural subtype in the TCGA (the Cancer Genome Atlas) discovery set which we have validated in the TCGA validation set. In addition, we have performed network analysis in the form of Bayesian Network discovery and Ingenuity Pathway Analysis to further dissect the underlying biology of this gene signature in the etiology of GBM. We theorize that the strong predictive value of the IFN/STAT1 gene signature in the Proneural subtype may be due to chemotherapy and/or radiation resistance induced through prolonged constitutive signaling of these genes during the course of the illness. The results of this study have implications both for better prediction models for survival outcome in GBM and for improved understanding of the underlying subtype-specific molecular mechanisms for GBM tumor progression and treatment response.

Highlights

Glioblastoma multiforme (GBM) remains the most common primary brain malignancy and carries the worst prognosis [1]
Predictive markers in GBM are quite limited, with the only established marker being the methylation status of O(6)-methylguanine-DNA-methyltransferase (MGMT) which is a predictor of temozolomide [2] and radiation resistance [3]
These results show an increased hazard for death for all of these genes in both the full and Proneural data sets, with significance at a level of 0.05 found for MX1 in the full data set, and significance found for all genes except IFIT1 and USP18 in the Proneural data set

Summary

Introduction

Glioblastoma multiforme (GBM) remains the most common primary brain malignancy and carries the worst prognosis [1]. Several groups have investigated molecular and genetic characteristics of these tumors in order to develop both prognostic and predictive biomarkers. Most of the biomarkers identified to date have been prognostic in that they help to determine estimates of survival (prognosis) independent of treatment. Predictive markers, on the other hand, inform regarding sensitivity to specific therapies. Predictive markers in GBM are quite limited, with the only established marker being the methylation status of O(6)-methylguanine-DNA-methyltransferase (MGMT) which is a predictor of temozolomide [2] and radiation resistance [3]. Studies from other cancers have identified predictive markers with potential application in GBM

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