One of the main reasons for the aggressive behavior of glioblastoma (GBM) is its intrinsic intra-tumor heterogeneity, characterized by the presence of clonal and subclonal differentiated tumor cell populations, glioma stem cells, and components of the tumor microenvironment, which affect multiple hallmark cellular functions in cancer. “Tumor Heterogeneity” usually encompasses both inter-tumor heterogeneity (population-level differences); and intra-tumor heterogeneity (differences within individual tumors). Tumor heterogeneity may be assessed in a single time point (spatial heterogeneity) or along the clinical evolution of GBM (longitudinal heterogeneity). Molecular methods may detect clonal and subclonal alterations to describe tumor evolution, even when samples from multiple areas are collected in the same time point (spatial-temporal heterogeneity). In GBM, although the inter-tumor mutational landscape is relatively homogeneous, intra-tumor heterogeneity is a striking feature of this tumor. In this review, we will address briefly the inter-tumor heterogeneity of the CNS tumors that yielded the current glioma classification. Next, we will take a deeper dive in the intra-tumor heterogeneity of GBMs, which directly affects prognosis and response to treatment. Our approach aims to follow technological developments, allowing for characterization of intra-tumor heterogeneity, beginning with differences on histomorphology of GBM and ending with molecular alterations observed at single-cell level.

Rapid tumor growth, widespread brain-invasion, and therapeutic resistance critically contribute to glioblastoma (GBM) recurrence and dismal patient outcomes. Although GBM stem cells (GSC) are shown to play key roles in these processes, the molecular pathways governing the GSC phenotype (GBM-stemness) remain poorly defined. Here, we show that epigenetic silencing of miR-146a significantly correlated with worse patient outcome and importantly, miR-146a level was significantly lower in recurrent tumors compared with primary ones. Further, miR-146a overexpression significantly inhibited the proliferation and invasion of GBM patient-derived primary cells and increased their response to temozolomide (TMZ), both in vitro and in vivo. Mechanistically, miR-146a directly silenced POU3F2 and SMARCA5, two transcription factors that mutually regulated each other, significantly compromising GBM-stemness and increasing TMZ response. Collectively, our data show that miR-146a–POU3F2/SMARCA5 pathway plays a critical role in suppressing GBM-stemness and increasing TMZ-response, suggesting that POU3F2 and SMARCA5 may serve as novel therapeutic targets in GBM. Implications: miR-146a predicts favorable prognosis and the miR-146a–POU3F2/SMARCA5 pathway is important for the suppression of stemness in GBM.

PURPOSE: This study sought to determine the prognostic significance of the WHO-defined glioma molecular subgroups along with additional alterations, including MGMT promoter methylation and mutations in ATRX, CIC, FUBP1, TERT, and TP53, in NRG/RTOG 0424 using long-term follow-up data. METHODS: Mutations were determined using an Ion Torrent sequencing panel. 1p/19q co-deletion and MGMT promoter methylation were determined by Affymetrix OncoScan and Illumina 450K arrays. Progression-free survival (PFS) and overall survival (OS) were estimated using the Kaplan-Meier method and tested using the log-rank test. Hazard ratios were calculated using the Cox proportional hazard model. Multivariable analyses (MVAs) included patient pretreatment characteristics. RESULTS: We obtained complete molecular data to categorize 80/129 eligible patients within the WHO subgroups. Of these, 26 (32.5%) were IDHmutant/co-deleted, 28 (35%) were IDHmutant/non-co-deleted, and 26 (32.5%) were IDHwild-type. Upon single-marker MVA, both IDHmutant subgroups were associated with significantly better OS and PFS ( P values < .001), compared with the IDHwild-type subgroup. MGMT promoter methylation was obtained on 76 patients, where 58 (76%) were methylated and 18 (24%) were unmethylated. Single-marker MVAs demonstrated that MGMT promoter methylation was statistically significant for OS ( P value < .001) and PFS ( P value = .003). In a multimarker MVA, one WHO subgroup comparison ( IDHmutant/co-deleted v IDHwild-type) was significant for OS ( P value = .045), whereas MGMT methylation did not retain significance. CONCLUSION: This study reports the long-term prognostic effect of the WHO molecular subgroups, MGMT promoter methylation, and other mutations in NRG/RTOG 0424. These results demonstrate that the WHO molecular classification and MGMT both serve as strong prognostic indicators, but that MGMT does not appear to add statistically significant prognostic value to the WHO subgrouping, above and beyond IDH and 1p/19q status.

Aims: Resource‐strained healthcare ecosystems often struggle with the adoption of the World Health Organization (WHO) recommendations for the classification of central nervous system (CNS) tumors. The generation of robust clinical diagnostic aids and the advancement of simple solutions to inform investment strategies in surgical neuropathology would improve patient care in these settings.Methods: We used simple information theory calculations on a brain cancer simulation model and real‐world data sets to compare contributions of clinical, histologic, immunohistochemical, and molecular information. An image noise assay was generated to compare the efficiencies of different image segmentation methods in H&E and Olig2 stained images obtained from digital slides. An auto‐adjustable image analysis workflow was generated and compared with neuropathologists for p53 positivity quantification. Finally, the density of extracted features of the nuclei, p53 positivity quantification, and combined ATRX/age feature was used to generate a predictive model for 1p/19q codeletion in IDH‐mutant tumors.Results: Information theory calculations can be performed on open access platforms and provide significant insight into linear and nonlinear associations between diagnostic biomarkers. Age, p53, and ATRX status have significant information for the diagnosis of IDH‐mutant tumors. The predictive models may facilitate the reduction of false‐positive 1p/19q codeletion by fluorescence in situ hybridization (FISH) testing.Conclusions: We posit that this approach provides an improvement on the cIMPACT‐NOW workflow recommendations for IDH‐mutant tumors and a framework for future resource and testing allocation.

Background: Despite the advances in glioblastoma (GBM) treatment, the average life span of patients is 14 months. Therefore, it is urgent to identity biomarkers of prognosis, treatment response, or development of novel treatment strategies. We previously described the association of high epidermal growth factor-like domain multiple 7 (EGFL7) expression and unfavorable outcome of pilocytic astrocytoma patients. The present study aims to analyze the prognostic potential of EGFL7 in GBM isocitrate dehydrogenase (IDH)-wildtype, using immunohistochemistry and in silico approaches.Methods: Spearman’s correlation analysis of The Cancer Genome Atlas RNA sequencing data was performed. The genes strongly correlated to EGFL7 expression were submitted to enrichment gene ontology and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis. Additionally, EGFL7 expression was associated with patient overall survival. The expression of EGFL7 was analyzed through immunohistochemistry in 74 GBM IDH-wildtype patients’ samples, and was associated with clinicopathological data and overall survival.Results: In silico analysis found 78 genes strongly correlated to EGFL7 expression. These genes were enriched in 40 biological processes and eight KEGG pathways, including angiogenesis/vasculogenesis, cell adhesion, and phosphoinositide 3-kinase–Akt, Notch, and Rap1 signaling pathways. The immunostaining showed high EGFL7 expression in 39 cases (52.7%). High immunolabelling was significantly associated with low Karnofsky Performance Status and poor overall survival. Cox analysis showed that GBMs IDH-wildtype with high EGFL7 expression presented a higher risk of death compared to low expression (hazard ratio, 1.645; 95% confidence interval, 1.021 to 2.650; p = .041).Conclusions: This study gives insights regarding the genes that are correlated with EGFL7, as well as biological processes and signaling pathways, which should be further investigated in order to elucidate their role in glioblastoma biology.