Establishment of Prognostic Models for Astrocytic and Oligodendroglial Brain Tumors with Standardized Quantification of Marker Gene Expression and Clinical Variables

Background Prognosis models established using multiple molecular markers in cancer along with clinical variables should enable prediction of natural disease progression and residual risk faced by patients. In this study, multivariate Cox proportional hazards analyses were done based on overall survival (OS) of 100 glioblastoma multiformes (GBMs, 92 events), 49 anaplastic astrocytomas (AAs, 33 events), 45 gliomas with oligodendroglial features, including anaplastic oligodendroglioma (AO, 13 events) and oligodendraglioma (O, 9 events). The modeling included two clinical variables (patient age and recurrence at the time of sample collection) and the expression variables of 13 genes selected based on their proven biological and/or prognosis functions in gliomas ( ABCG2, BMI1, MELK, MSI1, PROM1, CDK4, EGFR, MMP2, VEGFA, PAX6, PTEN, RPS9, and IGFBP2 ). Gene expression data was a log-transformed ratio of marker and reference ( ACTB ) mRNA levels quantified using absolute real-time qRT-PCR. Results Age is positively associated with overall grade (4 for GBM, 3 for AA, 2_1 for AO_O), but lacks significant prognostic value in each grade. Recurrence is an unfavorable prognostic factor for AA, but lacks significant prognostic values for GBM and AO_O. Univariate models revealed opposing prognostic effects of ABCG2, MELK, BMI1, PROM1, IGFBP2, PAX6, RPS9 , and MSI1 expressions for astrocytic (GBM and AA) and oligodendroglial tumors (AO_O). Multivariate models revealed independent prognostic values for the expressions of MSI1 (unfavorable) in GBM, CDK4 (unfavorable) and MMP2 (favorable) in AA, while IGFBP2 and MELK (unfavorable) in AO_O. With all 13 genes and 2 clinical variables, the model R 2 was 14.2% ( P = 0.358) for GBM, 45.2% ( P = 0.029) for AA, and 62.2% ( P = 0.008) for AO_O. Conclusion The study signifies the challenge in establishing a significant prognosis model for GBM. Our success in establishing prognosis models for AA and AO_O was largely based on identification of a set of genes with independent prognostic values and application of standardized gene expression quantification to allow formation of a large cohort in analysis

Molecular Profiling Reveals Biologically Discrete Subsets and Pathways of Progression in Diffuse Glioma

Therapy development for adult diffuse glioma is hindered by incomplete knowledge of somatic glioma driving alterations and suboptimal disease classification. We defined the complete set of genes associated with 1,122 diffuse grade II-III-IV gliomas from The Cancer Genome Atlas and used molecular profiles to improve disease classification, identify molecular correlations, and provide insights into the progression from low- to high-grade disease. Whole-genome sequencing data analysis determined that ATRX but not TERT promoter mutations are associated with increased telomere length. Recent advances in glioma classification based on IDH mutation and 1p/19q co-deletion status were recapitulated through analysis of DNA methylation profiles, which identified clinically relevant molecular subsets. A subtype of IDH mutant glioma was associated with DNA demethylation and poor outcome; a group of IDH-wild-type diffuse glioma showed molecular similarity to pilocytic astrocytoma and relatively favorable survival. Understanding of cohesive disease groups may aid improved clinical outcomes

Modeling prognosis for patients with malignant astrocytic gliomas: Quantifying the expression of multiple genetic markers and clinical variables1

The disparate lengths of survival among patients with malignant astrocytic gliomas (anaplastic astrocytomas [AAs] and glioblastoma multiforme [GBM]) cannot be adequately accounted for by clinical variables (patient age, histology, and recurrent status). Using real-time quantitative reverse transcription–polymerase chain reaction, we quantified the expression of four genes that were putative prognostic markers (CDK4, IGFBP2, MMP2, and RPS9) in a set of 43 AAs, 41 GBMs, and seven adjacent normal brain tissues. We previously explicated the expression and prognostic value of PAX6, PTEN, VEGF, and EGFR in these glioma tissues and established a comprehensive prognostic model (Zhou et al., 2003). This study attempts to improve that model by including four additional genetic markers, which exhibited a differential expression (P < 0.001) among tumor grades and between tumor and normal tissues. By including eight log-scaled gene expression variables, three clinical variables, and interaction terms among the eight genes, we established a prognostic model that accounted for two thirds of the variation (R2) in survival for this set of patients. To improve the R2 of the model without compromising its clinical utility, our data demonstrated that incorporating genes from different pathways markedly strengthens the model. Spearman rank correlation analysis of gene expression demonstrated a statistically significant positive correlation (P < 0.01) between the expression of IGFBP2–MMP2 and IGFBP2–VEGF in GBMs, but not in AAs. This finding suggests that the expression of IGFBP2 is associated with pathways activated specifically in GBMs that result in enhancing invasiveness and angiogenesis

A Novel HSP90 inhibitor, NVP-HSP990 Targets the Cell Cycle Regulators to Produce Anti-glioma Effect in Olig2-expressing Glioma Stem Cells

Glioblastoma multiforme’s genetic heterogeneity and signaling alterations decrease the effectiveness of single-agent therapies. Heat shock protein 90 (HSP90) is a key molecular chaperone that modulates a group of proteins, many of which are key deregulated signals in glioma cells. The ability to target multiple proteins by inhibiting HSP90 is therefore an appealing therapeutic objective for GBM. Previous work identifying glioma stem cells (GSCs) and demonstrating the initiation of gliomas by GSCs in animals offered hope for the development of anti-glioma therapeutics. Our study evaluated a HSP90 inhibitor, NVP-HSP990 (Novartis), in a panel of GSC lines. NVP-HSP990 treatment resulted in a dose-dependent inhibition of GSC growth, with IC50 values in the low nanomolar range. Our results distinguished between two subgroups of GSCs, revealing a subset of cells expressing Olig2 that were more sensitive to NVP-HSP990. Moreover, NVP-HSP990 markedly impaired GSC maintenance and triggered neuronal differentiation as demonstrated by the expression of neuronal markers TuJ1 and NeuN in responder GSC lines. NVP-HSP990 also disrupted the cell-cycle control mechanism by decreasing CDK2 and CDK4 and induced apoptosis-related molecules. Parallel to the in vitro activity of the compound, our in vivo study of an intracranial model of GSCs showed prolonged median survival times in treated cohorts. Therefore, our findings suggest that GBM with high Olig2 expression might be more susceptible to NVP-HSP990 treatment and HSP90 signaling in GBM warrants further investigations