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A three-microRNA signature identifies two subtypes of glioblastoma patients with different clinical outcomes.

ABSTRACT: Glioblastoma multiforme (GBM) is the most common and malignant primary brain tumor in adults, characterized by aggressive growth, limited response to therapy, and inexorable recurrence. Because of the extremely unfavorable prognosis of GBM, it is important to develop more effective diagnostic and therapeutic strategies based on biologically and clinically relevant patient stratification systems. Analyzing a collection of patient-derived GBM stem-like cells (GSCs) by gene expression profiling, nuclear magnetic resonance spectroscopy, and signal transduction pathway activation, we identified two GSC clusters characterized by different clinical features. Due to the widely documented role played by microRNAs (miRNAs) in the tumorigenesis process, in this study we explored whether these two GBM patient subtypes could also be discriminated by different miRNA signatures. Global miRNA expression pattern was analyzed by oblique principal component analysis and principal component analysis. By a combined inferential strategy on PCA results, we identified a reduced set of three miRNAs - miR-23a, miR-27a, and miR-9* (miR-9-3p) - able to discriminate the proneural- and mesenchymal-like GSC phenotypes as well as mesenchymal and proneural subtypes of primary GBM included in The Cancer Genome Atlas (TCGA) data set. Kaplan-Meier analysis showed a significant correlation between the selected miRNAs and overall survival in 429 GBM specimens from TCGA-identifying patients who had an unfavorable outcome. The survival prognostic capability of the three-miRNA signatures could have important implications for the understanding of the biology of GBM subtypes and could be useful in patient stratification to facilitate interpretation of results from clinical trials.

SUBMITTER: Marziali G

PROVIDER: S-EPMC5579331 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description: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.

Project description:BackgroundGlioblastoma (GBM) is the most malignant among gliomas with an inevitable lethal outcome. The elucidation of the physiology and regulation of this tumor is mandatory to unravel novel target and effective therapeutics. Emerging concepts show that the minor subset of glioblastoma stem cells (GSCs) accounts for tumorigenicity, representing the true target for innovative therapies in GBM.MethodsHere, we isolated and established functionally stable and steadily expanding GSCs lines from a large cohort of GBM patients. The molecular, functional and antigenic landscape of GBM tissues and their derivative GSCs was highlited in a side-by-side comprehensive genomic and transcriptomic characterization by ANOVA and Fisher's exact tests. GSCs' physio-pathological hallmarks were delineated by comparing over time in vitro and in vivo their expansion, self-renewal and tumorigenic ability with hierarchical linear models for repeated measurements and Kaplan-Meier method. Candidate biomarkers performance in discriminating GBM patients' classification emerged by classification tree and patients' survival analysis.ResultsHere, distinct biomarker signatures together with aberrant functional programs were shown to stratify GBM patients as well as their sibling GSCs population into TCGA clusters. Of importance, GSCs cells were demonstrated to fully resemble over time the molecular features of their patient of origin. Furthermore, we pointed out the existence of distinct GSCs subsets within GBM classification, inherently endowed with different self-renewal and tumorigenic potential. Particularly, classical GSCs were identified by more undifferentiated biological hallmarks, enhanced expansion and clonal capacity as compared to the more mature, relatively slow-propagating mesenchymal and proneural cells, likely endowed with a higher potential for infiltration either ex vivo or in vivo. Importantly, the combination of DCX and EGFR markers, selectively enriched among GSCs pools, almost exactly predicted GBM patients' clusters together with their survival and drug response.ConclusionsIn this study we report that an inherent enrichment of distinct GSCs pools underpin the functional inter-cluster variances displayed by GBM patients. We uncover two selectively represented novel functional biomarkers capable of discriminating GBM patients' stratification, survival and drug response, setting the stage for the determination of patient-tailored diagnostic and prognostic strategies and, mostly, for the design of appropriate, patient-selective treatment protocols.

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A three-microRNA signature identifies two subtypes of glioblastoma patients with different clinical outcomes.

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