Christine Jungk

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.

Tumor infiltration in enhancing and non-enhancing parts of glioblastoma: A correlation with histopathology

Purpose To correlate histopathologic findings from biopsy specimens with their corresponding location within enhancing areas, non-enhancing areas and necrotic areas on contrast enhanced T1-weighted MRI scans (cT1). Materials and Methods In 37 patients with newly diagnosed glioblastoma who underwent stereotactic biopsy, we obtained a correlation of 561 1mm3 biopsy specimens with their corresponding position on the intraoperative cT1 image at 1.5 Tesla. Biopsy points were categorized as enhancing (CE), non-enhancing (NE) or necrotic (NEC) on cT1 and tissue samples were categorized as “viable tumor cells”, “blood” or “necrotic tissue (with or without cellular component)”. Cell counting was done semi-automatically. Results NE had the highest content of tissue categorized as viable tumor cells (89% vs. 60% in CE and 30% NEC, respectively). Besides, the average cell density for NE (3764 ± 2893 cells/mm2) was comparable to CE (3506 ± 3116 cells/mm2), while NEC had a lower cell density with 2713 ± 3239 cells/mm2. If necrotic parts and bleeds were excluded, cell density in biopsies categorized as “viable tumor tissue” decreased from the center of the tumor (NEC, 5804 ± 3480 cells/mm2) to CE (4495 ± 3209 cells/mm2) and NE (4130 ± 2817 cells/mm2). Discussion The appearance of a glioblastoma on a cT1 image (circular enhancement, central necrosis, peritumoral edema) does not correspond to its diffuse histopathological composition. Cell density is elevated in both CE and NE parts. Hence, our study suggests that NE contains considerable amounts of infiltrative tumor with a high cellularity which might be considered in resection planning.

Factors triggering an additional resection and determining residual tumor volume on intraoperative MRI: analysis from a prospective single-center registry of supratentorial gliomas.

OBJECTIVE In this analysis, the authors sought to identify variables triggering an additional resection (AR) and determining residual intraoperative tumor volume in 1.5-T intraoperative MRI (iMRI)-guided glioma resections. METHODS A consecutive case series of 224 supratentorial glioma resections (WHO Grades I-IV) from a prospective iMRI registry (inclusion dates January 2011-April 2013) was examined with univariate and multiple regression models including volumetric data, tumor-related, and surgeon-related factors. The surgeons expectation of an AR, in response to a questionnaire completed prior to iMRI, was evaluated using contingency analysis. A machine-learning prediction model was applied to consider if anticipation of intraoperative findings permits preoperative identification of ideal iMRI cases. RESULTS An AR was performed in 70% of cases after iMRI, but did not translate into an accumulated risk for neurological morbidity after surgery (p = 0.77 for deficits in cases with AR vs no AR). New severe persistent deficits occurred in 6.7% of patients. Initial tumor volume determined frequency of ARs and was independently correlated with larger tumor remnants delineated on iMRI (p < 0.0001). Larger iMRI volume was further associated with eloquent location (p = 0.010) and recurrent tumors (p < 0.0001), and with WHO grade (p = 0.0113). Greater surgical experience had no significant influence on the course of surgery. The surgeons capability of ruling out an AR prior to iMRI turned out to incorporate guesswork (negative predictive value 43.6%). In a prediction model, AR could only be anticipated with 65% accuracy after integration of confounding variables. CONCLUSIONS Routine use of iMRI in glioma surgery is a safe and reliable method for resection guidance and is characterized by frequent ARs after scanning. Tumor-related factors were identified that influenced the course of surgery and intraoperative decision-making, and iMRI had a common value for surgeons of all experience levels. Commonly, the subjective intraoperative impression of the extent of resection had to be revised after iMRI review, which underscores the manifold potential of iMRI guidance. In combination with the failure to identify ideal iMRI cases preoperatively, this study supports a generous, tumor-oriented rather than surgeon-oriented indication for iMRI in glioma surgery.

Automatic analysis of cellularity in glioblastoma and correlation with ADC using trajectory analysis and automatic nuclei counting

Objective Several studies have analyzed a correlation between the apparent diffusion coefficient (ADC) derived from diffusion-weighted MRI and the tumor cellularity of corresponding histopathological specimens in brain tumors with inconclusive findings. Here, we compared a large dataset of ADC and cellularity values of stereotactic biopsies of glioblastoma patients using a new postprocessing approach including trajectory analysis and automatic nuclei counting. Materials and Methods Thirty-seven patients with newly diagnosed glioblastomas were enrolled in this study. ADC maps were acquired preoperatively at 3T and coregistered to the intraoperative MRI that contained the coordinates of the biopsy trajectory. 561 biopsy specimens were obtained; corresponding cellularity was calculated by semi-automatic nuclei counting and correlated to the respective preoperative ADC values along the stereotactic biopsy trajectory which included areas of T1-contrast-enhancement and necrosis. Results There was a weak to moderate inverse correlation between ADC and cellularity in glioblastomas that varied depending on the approach towards statistical analysis: for mean values per patient, Spearman’s ρ = -0.48 (p = 0.002), for all trajectory values in one joint analysis Spearman’s ρ = -0.32 (p < 0.001). The inverse correlation was additionally verified by a linear mixed model. Conclusions Our data confirms a previously reported inverse correlation between ADC and tumor cellularity. However, the correlation in the current article is weaker than the pooled correlation of comparable previous studies. Hence, besides cell density, other factors, such as necrosis and edema might influence ADC values in glioblastomas.

Transcriptomic analysis of aggressive meningiomas identifies PTTG1 and LEPR as prognostic biomarkers independent of WHO grade

Meningiomas are frequent central nervous system tumors. Although most meningiomas are benign (WHO grade I) and curable by surgery, WHO grade II and III tumors remain therapeutically challenging due to frequent recurrence. Interestingly, relapse also occurs in some WHO grade I meningiomas. Hence, we investigated the transcriptional features defining aggressive (recurrent, malignantly progressing or WHO grade III) meningiomas in 144 cases. Meningiomas were categorized into non-recurrent (NR), recurrent (R), and tumors undergoing malignant progression (M) in addition to their WHO grade. Unsupervised transcriptomic analysis in 62 meningiomas revealed transcriptional profiles lining up according to WHO grade and clinical subgroup. Notably aggressive subgroups (R+M tumors and WHO grade III) shared a large set of differentially expressed genes (n=332; p<0.01, FC>1.25). In an independent multicenter validation set (n=82), differential expression of 10 genes between WHO grades was confirmed. Additionally, among WHO grade I tumors differential expression between NR and aggressive R+M tumors was affirmed for PTTG1, AURKB, ECT2, UBE2C and PRC1, while MN1 and LEPR discriminated between NR and R+M WHO grade II tumors. Univariate survival analysis revealed a significant association with progression-free survival for PTTG1, LEPR, MN1, ECT2, PRC1, COX10, UBE2C expression, while multivariate analysis identified a prediction for PTTG1 and LEPR mRNA expression independent of gender, WHO grade and extent of resection. Finally, stainings of PTTG1 and LEPR confirmed malignancy-associated protein expression changes. In conclusion, based on the so far largest study sample of WHO grade III and recurrent meningiomas we report a comprehensive transcriptional landscape and two prognostic markers.

Prognostic value of the extent of resection in supratentorial WHO grade II astrocytomas stratified for IDH1 mutation status: a single-center volumetric analysis

Current evidence supports a maximized extent of resection (EOR) in low-grade gliomas (LGG), regardless of different histological subtypes and molecular markers. We therefore evaluated the prognostic impact of extensive, mainly intraoperative (i)MRI-guided surgery in low-grade astrocytomas stratified for IDH1 mutation status. Retrospective assessment of 46 consecutive cases of newly diagnosed supratentorial WHO grade II astrocytomas treated during the last decade was performed. IDH1 mutation status was obtained for all patients. Volumetric analysis of tumor volumes was performed pre-, intra-, early postoperatively and at first follow-up. Survival analysis was conducted with uni-and multivariate regression models implementing clinical parameters and continuous volumetric variables. Median EOR was 90.4 % (range 17.5–100 %) and was increased to 94.9 % (range 34.8–100 %) in iMRI-guided resections (n = 33). A greater EOR was prognostic for increased progression-free survival (HR 0.23, p = 0.031) and time to re-intervention (TTR) (HR 0.23, p = 0.03). In IDH1 mutant patients, smaller residual tumor volumes were associated with increased TTR (HR 1.01, p = 0.03). IDH1 mutation (38/46 cases) was an independent positive prognosticator for overall survival (OS) in multivariate analysis (HR 0.09, p = 0.002), while extensive surgery had limited impact upon OS. In a subgroup of patients with ≥40 % EOR (n = 39), however, initial and residual tumor volumes were prognostic for OS (HR 1.03, p = 0.005 and HR 1.08, p = 0.007, respectively), persistent to adjustment for IDH1. No association between EOR and neurologic morbidity was found. In this analysis of low-grade astrocytomas stratified for IDH1, extensive tumor resections were prognostic for progression and TTR and, in patients with ≥40 % EOR, for OS.

Novel, improved grading system(s) for IDH-mutant astrocytic gliomas

According to the 2016 World Health Organization Classification of Tumors of the Central Nervous System (2016 CNS WHO), IDH-mutant astrocytic gliomas comprised WHO grade II diffuse astrocytoma, IDH-mutant (AIIIDHmut), WHO grade III anaplastic astrocytoma, IDH-mutant (AAIIIIDHmut), and WHO grade IV glioblastoma, IDH-mutant (GBMIDHmut). Notably, IDH gene status has been made the major criterion for classification while the manner of grading has remained unchanged: it is based on histological criteria that arose from studies which antedated knowledge of the importance of IDH status in diffuse astrocytic tumor prognostic assessment. Several studies have now demonstrated that the anticipated differences in survival between the newly defined AIIIDHmut and AAIIIIDHmut have lost their significance. In contrast, GBMIDHmut still exhibits a significantly worse outcome than its lower grade IDH-mutant counterparts. To address the problem of establishing prognostically significant grading for IDH-mutant astrocytic gliomas in the IDH era, we undertook a comprehensive study that included assessment of histological and genetic approaches to prognosis in these tumors. A discovery cohort of 211 IDH-mutant astrocytic gliomas with an extended observation was subjected to histological review, image analysis, and DNA methylation studies. Tumor group-specific methylation profiles and copy number variation (CNV) profiles were established for all gliomas. Algorithms for automated CNV analysis were developed. All tumors exhibiting 1p/19q codeletion were excluded from the series. We developed algorithms for grading, based on molecular, morphological and clinical data. Performance of these algorithms was compared with that of WHO grading. Three independent cohorts of 108, 154 and 224 IDH-mutant astrocytic gliomas were used to validate this approach. In the discovery cohort several molecular and clinical parameters were of prognostic relevance. Most relevant for overall survival (OS) was CDKN2A/B homozygous deletion. Other parameters with major influence were necrosis and the total number of CNV. Proliferation as assessed by mitotic count, which is a key parameter in 2016 CNS WHO grading, was of only minor influence. Employing the parameters most relevant for OS in our discovery set, we developed two models for grading these tumors. These models performed significantly better than WHO grading in both the discovery and the validation sets. Our novel algorithms for grading IDH-mutant astrocytic gliomas overcome the challenges caused by introduction of IDH status into the WHO classification of diffuse astrocytic tumors. We propose that these revised approaches be used for grading of these tumors and incorporated into future WHO criteria.

LOC283731 promoter hypermethylation prognosticates survival after radiochemotherapy in IDH1 wild-type glioblastoma patients

MGMT promoter methylation status is currently the only established molecular prognosticator in IDH wild‐type glioblastoma multiforme (GBM). Therefore, we aimed to discover novel therapy‐associated epigenetic biomarkers. After enrichment for hypermethylated fractions using methyl‐CpG‐immunoprecipitation (MCIp), we performed global DNA methylation profiling for 14 long‐term (LTS; >36 months) and 15 short‐term (STS; 6–10 months) surviving GBM patients. Even after exclusion of the G‐CIMP phenotype, we observed marked differences between the LTS and STS methylome. A total of 1,247 probes in 706 genes were hypermethylated in LTS and 463 probes in 305 genes were found to be hypermethylated in STS patients (p values < 0.05, log2 fold change ± 0.5). We identified 13 differentially methylated regions (DMRs) with a minimum of four differentially methylated probes per gene. Indeed, we were able to validate a subset of these DMRs through a second, independent method (MassARRAY) in our LTS/STS training set (ADCY1, GPC3, LOC283731/ISLR2). These DMRs were further assessed for their prognostic capability in an independent validation cohort (n = 62) of non‐G‐CIMP GBMs from the TCGA. Hypermethylation of multiple CpGs mapping to the promoter region of LOC283731 correlated with improved patient outcome (p = 0.03). The prognostic performance of LOC283731 promoter hypermethylation was confirmed in a third independent study cohort (n = 89), and was independent of gender, performance (KPS) and MGMT status (p = 0.0485, HR = 0.63). Intriguingly, the prediction was most pronounced in younger GBM patients (<60 years). In conclusion, we provide compelling evidence that promoter methylation status of this novel gene is a prognostic biomarker in IDH1 wild‐type/non‐G‐CIMP GBMs.

Spatial transcriptome analysis reveals Notch pathway-associated prognostic markers in IDH1 wild-type glioblastoma involving the subventricular zone

BackgroundThe spatial relationship of glioblastoma (GBM) to the subventricular zone (SVZ) is associated with inferior patient survival. However, the underlying molecular phenotype is largely unknown. We interrogated an SVZ-dependent transcriptome and potential location-specific prognostic markers.MethodsmRNA microarray data of a discovery set (n = 36 GBMs) were analyzed for SVZ-dependent gene expression and process networks using the MetaCore™ workflow. Differential gene expression was confirmed by qPCR in a validation set of 142 IDH1 wild-type GBMs that was also used for survival analysis.ResultsMicroarray analysis revealed a transcriptome distinctive of SVZ+ GBM that was enriched for genes associated with Notch signaling. No overlap was found to The Cancer Genome Atlas’s molecular subtypes. Independent validation of SVZ-dependent expression confirmed four genes with simultaneous prognostic impact: overexpression of HES4 (p = 0.034; HR 1.55) and DLL3 (p = 0.017; HR 1.61) predicted inferior, and overexpression of NTRK2 (p = 0.049; HR 0.66) and PIR (p = 0.025; HR 0.62) superior overall survival (OS). Additionally, overexpression of DLL3 was predictive of shorter progression-free survival (PFS) (p = 0.043; HR 1.64). Multivariate analysis revealed overexpression of HES4 to be independently associated with inferior OS (p = 0.033; HR 2.03), and overexpression of DLL3 with inferior PFS (p = 0.046; HR 1.65).ConclusionsWe identified four genes with SVZ-dependent expression and prognostic significance, among those HES4 and DLL3 as part of Notch signaling, suggesting further evaluation of location-tailored targeted therapies.

Glioma through the looking GLASS: molecular evolution of diffuse gliomas and the Glioma Longitudinal Analysis Consortium

Abstract Adult diffuse gliomas are a diverse group of brain neoplasms that inflict a high emotional toll on patients and their families. The Cancer Genome Atlas and similar projects have provided a comprehensive understanding of the somatic alterations and molecular subtypes of glioma at diagnosis. However, gliomas undergo significant cellular and molecular evolution during disease progression. We review the current knowledge on the genomic and epigenetic abnormalities in primary tumors and after disease recurrence, highlight the gaps in the literature, and elaborate on the need for a new multi-institutional effort to bridge these knowledge gaps and how the Glioma Longitudinal Analysis Consortium (GLASS) aims to systemically catalog the longitudinal changes in gliomas. The GLASS initiative will provide essential insights into the evolution of glioma toward a lethal phenotype, with the potential to reveal targetable vulnerabilities and, ultimately, improved outcomes for a patient population in need.