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.

Keywords: glioblastoma; glioblastoma stem-like cells; microRNAs; patient stratification.

Figure 1

Principal component analysis of miRNA expression identifies two distinct clusters of GSC lines largely corresponding to the GSf‐like/GSr‐like classification described previously. Individual GSC samples (top) or miRNAs (bottom) are distributed into bivariate spaces spanned by the first two principal component loadings (top panel) and scores (bottom panel), respectively.

Figure 2

Classification into two clusters of GSC lines by miRNA signature reproduces the classification based on NMR analysis with the exception of one line.

Figure 3

Box and whiskers plots of miR‐9‐3p (top), miR‐23a (center), and miR‐27a (bottom) expression in M and P subtype GBM samples extracted from TCGA (A) or in GSC lines (B). Numbers of samples in each group are indicated in brackets. The variability represents the range encompassing minimum and maximum values. * and *** indicate a significant (P < 0.05 and P < 0.001) difference between the two groups, respectively (unpaired t‐test, two‐tailed).

Figure 4

Kaplan–Meier analysis shows that among 169 patients with GBM from TCGA, prognosis was significantly worse in those classified as GSr‐like than in those classified as GSf‐like (P = 0.0032) (A). The classification based on miRNA expression applied to the whole cohort of 429 patients for whom survival and miRNA expression data were available, irrespective of the Verhaak subtype classification, revealed that the prognoses of the GSr‐like patients were significantly worse than the prognoses of those classified as GSf‐like (P = 0.042) (B). For this analysis, a training set of 177 of 429 patients of known subtype (93 M and 84 P) was defined to build the linear discriminant function for predicting the GSr‐ and GSf‐like subtypes of the independent test set of 252 patients.

Figure 5

Pathway enrichment analysis of mRNA targets of the three miRNAs included in the signature indicates a significant association with cell survival, cancer, and cell adhesion but also with neurodegenerative diseases.