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.

PURPOSE: NRG Oncology/RTOG 9802 (ClinicalTrials.gov Identifier: NCT00003375 ) is a practice-changing study for patients with WHO low-grade glioma (LGG, grade II), as it was the first to demonstrate a survival benefit of adjuvant chemoradiotherapy over radiotherapy. This post hoc study sought to determine the prognostic and predictive impact of the WHO-defined molecular subgroups and corresponding molecular alterations within NRG Oncology/RTOG 9802.METHODS: IDH1/2 mutations were determined by immunohistochemistry and/or deep sequencing. A custom Ion AmpliSeq panel was used for mutation analysis. 1p/19q codeletion and MGMT promoter methylation were determined by copy-number arrays and/or Illumina 450K array, respectively. Progression-free survival (PFS) and overall survival (OS) were estimated using the Kaplan-Meier method. Hazard ratios (HRs) were calculated using the Cox proportional hazard model and tested using the log-rank test. Multivariable analyses (MVAs) were performed incorporating treatment and common prognostic factors as covariates.RESULTS: Of the eligible patients successfully profiled for the WHO-defined molecular groups (n = 106/251), 26 (24%) were IDH-wild type, 43 (41%) were IDH-mutant/non-codeleted, and 37(35%) were IDH-mutant/codeleted. MVAs demonstrated that WHO subgroup was a significant predictor of PFS after adjustment for clinical variables and treatment. Notably, treatment with postradiation chemotherapy (PCV; procarbazine, lomustine (CCNU), and vincristine) was associated with longer PFS (HR, 0.32; P = .003; HR, 0.13; P < .001) and OS (HR, 0.38; P = .013; HR, 0.21; P = .029) in the IDH-mutant/non-codeleted and IDH-mutant/codeleted subgroups, respectively. In contrast, no significant difference in either PFS or OS was observed with the addition of PCV in the IDH-wild-type subgroup.CONCLUSION: This study is the first to report the predictive value of the WHO-defined diagnostic classification in a set of uniformly treated patients with LGG in a clinical trial. Importantly, this post hoc analysis supports the notion that patients with IDH-mutant high-risk LGG regardless of codeletion status receive benefit from the addition of PCV.

Glioblastoma (GBM), the most aggressive primary brain tumor, has a current 5-year survival rate of 5% in the United States.¹ However, a small subset of patients do experience longer survival, suggesting underlying heterogeneity; therefore, development of better prognostic classification models is crucial. The first effort to comprehensively analyze GBM patient survival by prognostic grouping was published in 1993, using recursive partitioning analysis (RPA), a nonparametric statistical technique that creates distinct prognostic groups based on combinations of variables.² This initial RPA analysis included both GBM and anaplastic astrocytoma patients, who received radiation with and without chemotherapy or a radiation sensitizer. Based on clinical and histological characteristics, this analysis identified 6 prognostic classes (I-VI) with distinct survival outcomes.² Subsequently, a follow-up study involving only patients with GBM revised the original RPA model into 3 classes (III, IV, and V/VI),³ and this has been applied to many recent GBM clinical trials. Although the current RPA appears to accurately stratify patients in the temozolomide era,⁴ the original classes were not identified using a training set of temozolomide-treated patients and may not reflect the most accurate prognostic classes. Moreover, as recent studies have revealed key molecular pathways associated with pathogenesis of GBM, it was hypothesized that inclusion of corresponding proteins could enhance the discriminatory power of the current, RPA model. Therefore, we incorporated potential protein-based variables using specimens from the NRG Oncology (RTOG) 0525 trial, a phase 3 trial that compared standard adjuvant temozolomide vs a dose-dense schedule in patients with newly diagnosed GBM.⁵ No statistical difference in survival outcomes was found between the 2 arms of NRG Oncology RTOG 0525.

MicroRNAs regulate several aspects of tumorigenesis and cancer progression. Most cancer tissues are archived formalin-fixed and paraffin-embedded (FFPE). While microRNAs are a more stable form of RNA thought to withstand FFPE-processing and degradation there is only limited evidence for the latter assumption. We examined whether microRNA profiling can be successfully conducted on FFPE cancer tissues using SOLiD ligation based sequencing. Tissue storage times (2–9 years) appeared to not affect the number of detected microRNAs in FFPE samples compared to matched frozen samples (paired t-test p>0.7). Correlations of microRNA expression values were very high across microRNAs in a given sample (Pearson’s r = 0.71–0.95). Higher variance of expression values among samples was associated with higher correlation coefficients between FFPE and frozen tissues. One of the FFPE samples in this study was degraded for unknown reasons with a peak read length of 17 nucleotides compared to 21 in all other samples. The number of detected microRNAs in this sample was within the range of microRNAs detected in all other samples. Ligation-based microRNA deep sequencing on FFPE cancer tissues is feasible and RNA degradation to the degree observed in our study appears to not affect the number of microRNAs that can be quantified.