A novel volume-age-KPS (VAK) glioblastoma classification identifies a prognostic cognate microRNA-gene signature.

Pascal O Zinn,John Bruyere,Monika Hegi,Pratheesh Sathyan,Sadhan Majumder,Bhanu Mahajan,Rivka R Colen,Maciej S Lesniak

doi:10.1371/journal.pone.0041522

Abstract

BackgroundSeveral studies have established Glioblastoma Multiforme (GBM) prognostic and predictive models based on age and Karnofsky Performance Status (KPS), while very few studies evaluated the prognostic and predictive significance of preoperative MR-imaging. However, to date, there is no simple preoperative GBM classification that also correlates with a highly prognostic genomic signature. Thus, we present for the first time a biologically relevant, and clinically applicable tumor Volume, patient Age, and KPS (VAK) GBM classification that can easily and non-invasively be determined upon patient admission.MethodsWe quantitatively analyzed the volumes of 78 GBM patient MRIs present in The Cancer Imaging Archive (TCIA) corresponding to patients in The Cancer Genome Atlas (TCGA) with VAK annotation. The variables were then combined using a simple 3-point scoring system to form the VAK classification. A validation set (N = 64) from both the TCGA and Rembrandt databases was used to confirm the classification. Transcription factor and genomic correlations were performed using the gene pattern suite and Ingenuity Pathway Analysis.ResultsVAK-A and VAK-B classes showed significant median survival differences in discovery (P = 0.007) and validation sets (P = 0.008). VAK-A is significantly associated with P53 activation, while VAK-B shows significant P53 inhibition. Furthermore, a molecular gene signature comprised of a total of 25 genes and microRNAs was significantly associated with the classes and predicted survival in an independent validation set (P = 0.001). A favorable MGMT promoter methylation status resulted in a 10.5 months additional survival benefit for VAK-A compared to VAK-B patients.ConclusionsThe non-invasively determined VAK classification with its implication of VAK-specific molecular regulatory networks, can serve as a very robust initial prognostic tool, clinical trial selection criteria, and important step toward the refinement of genomics-based personalized therapy for GBM patients.

Highlights

Glioblastoma Multiforme (GBM) is the most common primary malignant brain tumor in adults
Etienne and colleagues demonstrated that older patients, who often have the De Novo form of GBM, have EGFR overexpression which is responsible for increased angiogenesis, edema, and invasion and might account for the decrease in survival in elderly patients [24]; younger patients more often exhibit a secondary form of glioblastoma that is associated with TP53 mutation [24]
Patient population We identified 78 GBM patients from The Cancer Genome Atlas (TCGA) for whom full annotation of Age, Karnofsky Performance Status (KPS), and MGMT methylation status, and corresponding pretreatment MR imaging was available in the The Cancer Imaging Archive (TCIA)

Summary

Introduction

Glioblastoma Multiforme (GBM) is the most common primary malignant brain tumor in adults. With the recent upsurge of genomic discoveries and enhanced imaging technologies, in particular MRI, additional prognostic and predictive determinants for GBM patients are available. Tumor volumes determined by MRI were shown to be a prognostic biomarker associated with survival in recurrent GBM [19]. Several studies have established Glioblastoma Multiforme (GBM) prognostic and predictive models based on age and Karnofsky Performance Status (KPS), while very few studies evaluated the prognostic and predictive significance of preoperative MR-imaging. We present for the first time a biologically relevant, and clinically applicable tumor Volume, patient Age, and KPS (VAK) GBM classification that can and non-invasively be determined upon patient admission

Methods

Results

Conclusion