Hayley Patricia Ellis

Current therapeutic advances targeting EGFR and EGFRvIII in glioblastoma

Epidermal growth factor receptor (EGFR) and EGFRvIII analysis is of current interest in glioblastoma – the most common malignant primary CNS tumor, because of new EGFRvIII vaccine trials underway. EGFR activation in glioblastoma promotes cellular proliferation via activation of MAPK and PI3K–Akt pathways, and EGFRvIII is the most common variant, leading to constitutively active EGFR. This review explains EGFR and EGFRvIII signaling in GBM; describes targeted therapy approaches to date including tyrosine kinase inhibitor, antibody-based therapies, vaccines and pre-clinical RNA-based therapies, and discusses the difficulties encountered with these approaches including pathway redundancy and intratumoral heterogeneity.

DNA methylation-based classification and grading system for meningioma: a multicentre, retrospective analysis

BACKGROUND The WHO classification of brain tumours describes 15 subtypes of meningioma. Nine of these subtypes are allotted to WHO grade I, and three each to grade II and grade III. Grading is based solely on histology, with an absence of molecular markers. Although the existing classification and grading approach is of prognostic value, it harbours shortcomings such as ill-defined parameters for subtypes and grading criteria prone to arbitrary judgment. In this study, we aimed for a comprehensive characterisation of the entire molecular genetic landscape of meningioma to identify biologically and clinically relevant subgroups. METHODS In this multicentre, retrospective analysis, we investigated genome-wide DNA methylation patterns of meningiomas from ten European academic neuro-oncology centres to identify distinct methylation classes of meningiomas. The methylation classes were further characterised by DNA copy number analysis, mutational profiling, and RNA sequencing. Methylation classes were analysed for progression-free survival outcomes by the Kaplan-Meier method. The DNA methylation-based and WHO classification schema were compared using the Brier prediction score, analysed in an independent cohort with WHO grading, progression-free survival, and disease-specific survival data available, collected at the Medical University Vienna (Vienna, Austria), assessing methylation patterns with an alternative methylation chip. FINDINGS We retrospectively collected 497 meningiomas along with 309 samples of other extra-axial skull tumours that might histologically mimic meningioma variants. Unsupervised clustering of DNA methylation data clearly segregated all meningiomas from other skull tumours. We generated genome-wide DNA methylation profiles from all 497 meningioma samples. DNA methylation profiling distinguished six distinct clinically relevant methylation classes associated with typical mutational, cytogenetic, and gene expression patterns. Compared with WHO grading, classification by individual and combined methylation classes more accurately identifies patients at high risk of disease progression in tumours with WHO grade I histology, and patients at lower risk of recurrence among WHO grade II tumours (p=0·0096) from the Brier prediction test). We validated this finding in our independent cohort of 140 patients with meningioma. INTERPRETATION DNA methylation-based meningioma classification captures clinically more homogenous groups and has a higher power for predicting tumour recurrence and prognosis than the WHO classification. The approach presented here is potentially very useful for stratifying meningioma patients to observation-only or adjuvant treatment groups. We consider methylation-based tumour classification highly relevant for the future diagnosis and treatment of meningioma. FUNDING German Cancer Aid, Else Kröner-Fresenius Foundation, and DKFZ/Heidelberg Institute of Personalized Oncology/Precision Oncology Program.

Current Challenges in Glioblastoma: Intratumour Heterogeneity, Residual Disease, and Models to Predict Disease Recurrence

Glioblastoma (GB) is the most common primary malignant brain tumor, and despite the availability of chemotherapy and radiotherapy to combat the disease, overall survival remains low with a high incidence of tumor recurrence. Technological advances are continually improving our understanding of the disease, and in particular, our knowledge of clonal evolution, intratumor heterogeneity, and possible reservoirs of residual disease. These may inform how we approach clinical treatment and recurrence in GB. Mathematical modeling (including neural networks) and strategies such as multiple sampling during tumor resection and genetic analysis of circulating cancer cells, may be of great future benefit to help predict the nature of residual disease and resistance to standard and molecular therapies in GB.

Biological Rationale for the Use of PPARγ Agonists in Glioblastoma

Glioblastoma multiforme (GBM) is the most common primary intrinsic central nervous system tumor and has an extremely poor overall survival with only 10% patients being alive after 5 years. There has been interesting preliminary evidence suggesting that diabetic patients receiving peroxisome proliferator-activated receptor gamma (PPARγ) agonists, a group of anti-diabetic, thiazolidinedione drugs, have an increased median survival for glioblastoma. Although thiazolidinediones are effective oral medications for type 2 diabetes, certain agonists carry the risk for congestive heart failure, myocardial infarction, cardiovascular disease, bone loss, weight gain, and fluid retention as side-effects. The nuclear receptor transcription factor PPARγ has been found to be expressed in high grade gliomas, and its activation has been shown to have several antineoplastic effects on human and rat glioma cell lines, and in some instances an additional protective increase in antioxidant enzymes has been observed in normal astrocytes. At present, no clinical trials are underway with regards to treating glioma patients using PPARγ agonists. This review presents the case for evaluating the potential of PPARγ agonists as novel adjuvants in the treatment of refractory high grade glioma.

BRAF Fusion Analysis in Pilocytic Astrocytomas: KIAA1549-BRAF 15-9 Fusions Are More Frequent in the Midline Than Within the Cerebellum.

Abstract Pilocytic astrocytomas (PAs) are increasingly tested for KIAA1549-BRAF fusions. We used reverse transcription polymerase chain reaction for the 3 most common KIAA1549-BRAF fusions, together with BRAF V600E and histone H3.3 K27M analyses to identify relationships of these molecular characteristics with clinical features in a cohort of 32 PA patients. In this group, the overall BRAF fusion detection rate was 24 (75%). Ten (42%) of the 24 had the 16-9 fusion, 8 (33%) had only the 15-9 fusion, and 1 (4%) of the patients had only the 16-11 fusion. In the PAs with only the 15-9 fusion, 1 PA was in the cerebellum and 7 were centered in the midline outside of the cerebellum, that is, in the hypothalamus (n = 4), optic pathways (n = 2), and brainstem (n = 1). Tumors within the cerebellum were negatively associated with fusion 15-9. Seven (22%) of the 32 patients had tumor-related deaths and 25 of the patients (78%) were alive between 2 and 14 years after initial biopsy. Age, sex, tumor location, 16-9 fusion, and 15-9 fusion were not associated with overall survival. Thus, in this small cohort, 15-9 KIAA1549-BRAF fusion was associated with midline PAs located outside of the cerebellum; these tumors, which are generally difficult to resect, are prone to recurrence.

Decreased expression of the mitochondrial BCAT protein correlates with improved patient survival in IDH-WT gliomas

Background and research question: Gliomas represent 43% of all solid intracranial tumours, of which glioblastomas have the poorest prognosis. Recently, the human cytosolic branched-chain aminotransferase protein (hBCATc), which metabolises the branched-chain amino acids (BCAA), was identified as a biomarker and therapeutic target for glioblastomas carrying wild-type isocitrate dehydrogenase (IDH-WT) genes. However, the clinical utility of the mitochondrial isoform, hBCATm, which also metabolises BCAAs, was not determined nor its potential role in predicting patient survival. Methods: Glioblastomas, of grades II-IV, from 53 patients were graded by a neuropathologist, where the IDH and MGMT status were assessed. Tumours positive for hBCATm, hBCATc and BCKDC were characterised using immunohistochemistry and Western blot analysis using antibodies specific to these proteins. Results: Here, we report that in IDH-WT tumours, the expression of hBCATm is significantly increased (p=0.034) relative to IDH mutation gliomas, and significantly correlates with patient survival, on Kaplan-Meier analysis, where low hBCATm expression is a positive prognostic factor (p=0.003). Moreover, increased hBCATm expression in these glioblastomas correlated with tumour grade indicating their role as a predictive biomarker of glioma progression. Multiple banding was observed for the branched-chain α-keto acid dehydrogenase complex, which catalyses the committed step in BCAA metabolism, but a significant change in expression was absent (p=0.690). Conclusion: Until now, IDH-WT glioblastomas have a uniformly poor prognosis, however we demonstrate for the first time that relatively low hBCATm may select for a better performing subset within this group and may represent a therapeutic target in these hard to treat patients.

Impact of Variable RNA-Sequencing Depth on Gene Expression Signatures and Target Compound Robustness: Case Study Examining Brain Tumor (Glioma) Disease Progression

Purpose Gene expression profiling can uncover biologic mechanisms underlying disease and is important in drug development. RNA sequencing (RNA-seq) is routinely used to assess gene expression, but costs remain high. Sample multiplexing reduces RNA-seq costs; however, multiplexed samples have lower cDNA sequencing depth, which can hinder accurate differential gene expression detection. The impact of sequencing depth alteration on RNA-seq–based downstream analyses such as gene expression connectivity mapping is not known, where this method is used to identify potential therapeutic compounds for repurposing. Methods In this study, published RNA-seq profiles from patients with brain tumor (glioma) were assembled into two disease progression gene signature contrasts for astrocytoma. Available treatments for glioma have limited effectiveness, rendering this a disease of poor clinical outcome. Gene signatures were subsampled to simulate sequencing alterations and analyzed in connectivity mapping to investigate target compound robustness. Results Data loss to gene signatures led to the loss, gain, and consistent identification of significant connections. The most accurate gene signature contrast with consistent patient gene expression profiles was more resilient to data loss and identified robust target compounds. Target compounds lost included candidate compounds of potential clinical utility in glioma (eg, suramin, dasatinib). Lost connections may have been linked to low-abundance genes in the gene signature that closely characterized the disease phenotype. Consistently identified connections may have been related to highly expressed abundant genes that were ever-present in gene signatures, despite data reductions. Potential noise surrounding findings included false-positive connections that were gained as a result of gene signature modification with data loss. Conclusion Findings highlight the necessity for gene signature accuracy for connectivity mapping, which should improve the clinical utility of future target compound discoveries.