Summary Accurate pathological diagnosis is crucial for optimal management of cancer patients. For the ~100 known central nervous system (CNS) tumour entities, 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. We herein present the development of a comprehensive approach for DNA methylation-based CNS tumour classification across all entities and age groups, and demonstrate its application in a routine diagnostic setting. We show that availability of this method may have substantial impact on diagnostic precision compared with 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 (www.molecularneuropathology.org) requiring no 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.

Link to publication record in Explore Bristol Research PDF-document This is the author accepted manuscript (AAM). The final published version (version of record) is available online via Lancet at https://www.sciencedirect.com/science/article/pii/S1470204517301559?via%3Dihub . Please refer to any applicable terms of use of the publisher. University of Bristol -Explore Bristol Research General rightsThis document is made available in accordance with publisher policies. Please cite only the published version using the reference above. Full terms of use are available:

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.

The WHO 2007 classification of tumors of the CNS distinguishes between diffuse astrocytoma WHO grade II (A IIWHO2007) 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.

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 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. 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.

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.

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.