DNA methylation-based classification of central nervous system tumours.

Accurate pathological diagnosis is crucial for optimal management of patients with cancer. For the approximately 100 known tumour types of the central nervous system, standardization of the diagnostic process has been shown to be particularly challenging-with substantial inter-observer variability in the histopathological diagnosis of many tumour types. Here we present a comprehensive approach for the DNA methylation-based classification of central nervous system tumours across all entities and age groups, and demonstrate its application in a routine diagnostic setting. We show that the availability of this method may have a substantial impact on diagnostic precision compared to standard methods, resulting in a change of diagnosis in up to 12% of prospective cases. For broader accessibility, we have designed a free online classifier tool, the use of which does not require any additional onsite data processing. Our results provide a blueprint for the generation of machine-learning-based tumour classifiers across other cancer entities, with the potential to fundamentally transform tumour pathology.

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.

Radiogenomics of Glioblastoma: Machine Learning-based Classification of Molecular Characteristics by Using Multiparametric and Multiregional MR Imaging Features.

Purpose To evaluate the association of multiparametric and multiregional magnetic resonance (MR) imaging features with key molecular characteristics in patients with newly diagnosed glioblastoma. Materials and Methods Retrospective data evaluation was approved by the local ethics committee, and the requirement to obtain informed consent was waived. Preoperative MR imaging features were correlated with key molecular characteristics within a single-institution cohort of 152 patients with newly diagnosed glioblastoma. Preoperative MR imaging features (n = 31) included multiparametric (anatomic and diffusion-, perfusion-, and susceptibility-weighted images) and multiregional (contrast-enhancing regions and hyperintense regions at nonenhanced fluid-attenuated inversion recovery imaging) information with histogram quantification of tumor volumes, volume ratios, apparent diffusion coefficients, cerebral blood flow, cerebral blood volume, and intratumoral susceptibility signals. Molecular characteristics determined included global DNA methylation subgroups (eg, mesenchymal, RTK I "PGFRA," RTK II "classic"), MGMT promoter methylation status, and hallmark copy number variations (EGFR, PDGFRA, MDM4, and CDK4 amplification; PTEN, CDKN2A, NF1, and RB1 loss). Univariate analyses (voxel-lesion symptom mapping for tumor location, Wilcoxon test for all other MR imaging features) and machine learning models were applied to study the strength of association and discriminative value of MR imaging features for predicting underlying molecular characteristics. Results There was no tumor location predilection for any of the assessed molecular parameters (permutation-adjusted P > .05). Univariate imaging parameter associations were noted for EGFR amplification and CDKN2A loss, with both demonstrating increased Gaussian-normalized relative cerebral blood volume and Gaussian-normalized relative cerebral blood flow values (area under the receiver operating characteristics curve: 63%-69%, false discovery rate-adjusted P < .05). Subjecting all MR imaging features to machine learning-based classification enabled prediction of EGFR amplification status and the RTK II glioblastoma subgroup with a moderate, yet significantly greater, accuracy (63% for EGFR [P < .01], 61% for RTK II [P = .01]) than prediction by chance; prediction accuracy for all other molecular parameters was not significant. Conclusion The authors found associations between established MR imaging features and molecular characteristics, although not of sufficient strength to enable generation of machine learning classification models for reliable and clinically meaningful prediction of molecular characteristics in patients with glioblastoma. © RSNA, 2016 Online supplemental material is available for this article.

Methylation-based classification of benign and malignant peripheral nerve sheath tumors.

The vast majority of peripheral nerve sheath tumors derive from the Schwann cell lineage and comprise diverse histological entities ranging from benign schwannomas and neurofibromas to high-grade malignant peripheral nerve sheath tumors (MPNST), each with several variants. There is increasing evidence for methylation profiling being able to delineate biologically relevant tumor groups even within the same cellular lineage. Therefore, we used DNA methylation arrays for methylome- and chromosomal profile-based characterization of 171 peripheral nerve sheath tumors. We analyzed 28 conventional high-grade MPNST, three malignant Triton tumors, six low-grade MPNST, four epithelioid MPNST, 33 neurofibromas (15 dermal, 8 intraneural, 10 plexiform), six atypical neurofibromas, 43 schwannomas (including 5 NF2 and 5 schwannomatosis associated cases), 11 cellular schwannomas, 10 melanotic schwannomas, 7 neurofibroma/schwannoma hybrid tumors, 10 nerve sheath myxomas and 10 ganglioneuromas. Schwannomas formed different epigenomic subgroups including a vestibular schwannoma subgroup. Cellular schwannomas were not distinct from conventional schwannomas. Nerve sheath myxomas and neurofibroma/schwannoma hybrid tumors were most similar to schwannomas. Dermal, intraneural and plexiform neurofibromas as well as ganglioneuromas all showed distinct methylation profiles. Atypical neurofibromas and low-grade MPNST were indistinguishable with a common methylation profile and frequent losses of CDKN2A. Epigenomic analysis finds two groups of conventional high-grade MPNST sharing a frequent loss of neurofibromin. The larger of the two groups shows an additional loss of trimethylation of histone H3 at lysine 27 (H3K27me3). The smaller one retains H3K27me3 and is found in spinal locations. Sporadic MPNST with retained neurofibromin expression did not form an epigenetic group and most cases could be reclassified as cellular schwannomas or soft tissue sarcomas. Widespread immunohistochemical loss of H3K27me3 was exclusively seen in MPNST of the main methylation cluster, which defines it as an additional useful marker for the differentiation of cellular schwannoma and MPNST.

Next-generation sequencing in routine brain tumor diagnostics enables an integrated diagnosis and identifies actionable targets.

With the number of prognostic and predictive genetic markers in neuro-oncology steadily growing, the need for comprehensive molecular analysis of neuropathology samples has vastly increased. We therefore developed a customized enrichment/hybrid-capture-based next-generation sequencing (NGS) gene panel comprising the entire coding and selected intronic and promoter regions of 130 genes recurrently altered in brain tumors, allowing for the detection of single nucleotide variations, fusions, and copy number aberrations. Optimization of probe design, library generation and sequencing conditions on 150 samples resulted in a 5-workday routine workflow from the formalin-fixed paraffin-embedded sample to neuropathological report. This protocol was applied to 79 retrospective cases with established molecular aberrations for validation and 71 prospective cases for discovery of potential therapeutic targets. Concordance of NGS compared to established, single biomarker methods was 98.0 %, with discrepancies resulting from one case where a TERT promoter mutation was not called by NGS and three ATRX mutations not being detected by Sanger sequencing. Importantly, in samples with low tumor cell content, NGS was able to identify mutant alleles that were not detectable by traditional methods. Information derived from NGS data identified potential targets for experimental therapy in 37/47 (79 %) glioblastomas, 9/10 (90 %) pilocytic astrocytomas, and 5/14 (36 %) medulloblastomas in the prospective target discovery cohort. In conclusion, we present the settings for high-throughput, adaptive next-generation sequencing in routine neuropathology diagnostics. Such an approach will likely become highly valuable in the near future for treatment decision making, as more therapeutic targets emerge and genetic information enters the classification of brain tumors.

IDH mutant diffuse and anaplastic astrocytomas have similar age at presentation and little difference in survival: a grading problem for WHO.

The WHO 2007 classification of tumors of the CNS distinguishes between diffuse astrocytoma WHO grade II (A II(WHO2007)) and anaplastic astrocytoma WHO grade III (AA III(WHO2007)). Patients with A II(WHO2007) are significantly younger and survive significantly longer than those with AA III(WHO2007). So far, classification and grading relies on morphological grounds only and does not yet take into account IDH status, a molecular marker of prognostic relevance. We here demonstrate that WHO 2007 grading performs poorly in predicting prognosis when applied to astrocytoma carrying IDH mutations. Three independent series including a total of 1360 adult diffuse astrocytic gliomas with IDH mutation containing 683 A II(IDHmut), 562 AA III(IDHmut) and 115 GBM(IDHmut) have been examined for age distribution and survival. In all three series patients with A II(IDHmut )and AA III(IDHmut) were of identical age at presentation of disease (36-37 years) and the difference in survival between grades was much less (10.9 years for A II(IDHmut), 9.3 years for AA III(IDHmut)) than that reported for A II(WHO2007) versus AA III(WHO2007). Our analyses imply that the differences in age and survival between A II(WHO2007) and AA III(WHO2007) predominantly depend on the fraction of IDH-non-mutant astrocytomas in the cohort. This data poses a substantial challenge for the current practice of astrocytoma grading and risk stratification and is likely to have far-reaching consequences on the management of patients with IDH-mutant astrocytoma.

Adult IDH wild type astrocytomas biologically and clinically resolve into other tumor entities.

IDH wild type (IDHwt) anaplastic astrocytomas WHO grade III (AA III) are associated with poor outcome. To address the possibilities of molecular subsets among astrocytoma or of diagnostic reclassification, we analyzed a series of 160 adult IDHwt tumors comprising 120 AA III and 40 diffuse astrocytomas WHO grade II (A II) for molecular hallmark alterations and established methylation and copy number profiles. Based on molecular profiles and hallmark alterations the tumors could be grouped into four major sets. 124/160 (78 %) tumors were diagnosed as the molecular equivalent of conventional glioblastoma (GBM), and 15/160 (9 %) as GBM-H3F3A mutated (GBM-H3). 13/160 (8 %) exhibited a distinct methylation profile that was most similar to GBM-H3-K27, however, lacked the H3F3A mutation. This group was enriched for tumors of infratentorial and midline localization and showed a trend towards a more favorable prognosis. All but one of the 120 IDHwt AA III could be assigned to these three groups. 7 tumors recruited from the 40 A II, comprised a variety of molecular signatures and all but one were reclassified into distinct WHO entities of lower grades. Interestingly, TERT mutations were exclusively restricted to the molecular GBM (78 %) and associated with poor clinical outcome. However, the GBM-H3 group lacking TERT mutations appeared to fare even worse. Our data demonstrate that most of the tumors diagnosed as IDHwt astrocytomas can be allocated to other tumor entities on a molecular basis. The diagnosis of IDHwt diffuse astrocytoma or anaplastic astrocytoma should be used with caution.

Radiomic subtyping improves disease stratification beyond key molecular, clinical, and standard imaging characteristics in patients with glioblastoma.

Background: The purpose of this study was to analyze the potential of radiomics for disease stratification beyond key molecular, clinical, and standard imaging features in patients with glioblastoma. Methods: Quantitative imaging features (n = 1043) were extracted from the multiparametric MRI of 181 patients with newly diagnosed glioblastoma prior to standard-of-care treatment (allocated to a discovery and a validation set, 2:1 ratio). A subset of 386/1043 features were identified as reproducible (in an independent MRI test-retest cohort) and selected for analysis. A penalized Cox model with 10-fold cross-validation (Coxnet) was fitted on the discovery set to construct a radiomic signature for predicting progression-free and overall survival (PFS and OS). The incremental value of a radiomic signature beyond molecular (O6-methylguanine-DNA methyltransferase [MGMT] promoter methylation, DNA methylation subgroups), clinical (patient's age, KPS, extent of resection, adjuvant treatment), and standard imaging parameters (tumor volumes) for stratifying PFS and OS was assessed with multivariate Cox models (performance quantified with prediction error curves). Results: The radiomic signature (constructed from 8/386 features identified through Coxnet) increased the prediction accuracy for PFS and OS (in both discovery and validation sets) beyond the assessed molecular, clinical, and standard imaging parameters (P ≤ 0.01). Prediction errors decreased by 36% for PFS and 37% for OS when adding the radiomic signature (compared with 29% and 27%, respectively, with molecular + clinical features alone). The radiomic signature was-along with MGMT status-the only parameter with independent significance on multivariate analysis (P ≤ 0.01). Conclusions: Our study stresses the role of integrating radiomics into a multilayer decision framework with key molecular and clinical features to improve disease stratification and to potentially advance personalized treatment of patients with glioblastoma.

TERT Promoter Mutations and Risk of Recurrence in Meningioma.

The World Health Organization (WHO) classification and grading system attempts to predict the clinical course of meningiomas based on morphological parameters. However, because of high interobserver variation of some criteria, more reliable prognostic markers are required. Here, we assessed the TERT promoter for mutations in the hotspot regions C228T and C250T in meningioma samples from 252 patients. Mutations were detected in 16 samples (6.4% across the cohort, 1.7%, 5.7%, and 20.0% of WHO grade I, II, and III cases, respectively). Data were analyzed by t test, Fisher's exact test, log-rank test, and Cox proportional hazard model. All statistical tests were two-sided. Within a mean follow-up time in surviving patients of 68.1 months, TERT promoter mutations were statistically significantly associated with shorter time to progression (P < .001). Median time to progression among mutant cases was 10.1 months compared with 179.0 months among wild-type cases. Our results indicate that the inclusion of molecular data (ie, analysis of TERT promoter status) into a histologically and genetically integrated classification and grading system for meningiomas increases prognostic power. Consequently, we propose to incorporate the assessment of TERT promoter status in upcoming grading schemes for meningioma.