Advances in our understanding of the biological basis and molecular characteristics of ependymal tumors since the latest iteration of the World Health Organization (WHO) classification of CNS tumors ...(2016) have prompted the cIMPACT‐NOW group to recommend a new classification. Separation of ependymal tumors by anatomic site is an important principle of the new classification and was prompted by methylome profiling data to indicate that molecular groups of ependymal tumors in the posterior fossa and supratentorial and spinal compartments are distinct. Common recurrent genetic or epigenetic alterations found in tumors belonging to the main molecular groups have been used to define tumor types at intracranial sites; C11orf95 and YAP1 fusion genes for supratentorial tumors and two types of posterior fossa ependymoma defined by methylation group, PFA and PFB. A recently described type of aggressive spinal ependymoma with MYCN amplification has also been included. Myxopapillary ependymoma and subependymoma have been retained as histopathologically defined tumor types, but the classification has dropped the distinction between classic and anaplastic ependymoma. While the cIMPACT‐NOW group considered that data to inform assignment of grade to molecularly defined ependymomas are insufficiently mature, it recommends assigning WHO grade 2 to myxopapillary ependymoma and allows grade 2 or grade 3 to be assigned to ependymomas not defined by molecular status.
Recently, we described a machine learning approach for classification of central nervous system tumors based on the analysis of genome-wide DNA methylation patterns
6
. Here, we report on DNA ...methylation-based central nervous system (CNS) tumor diagnostics conducted in our institution between the years 2015 and 2018. In this period, more than 1000 tumors from the neurosurgical departments in Heidelberg and Mannheim and more than 1000 tumors referred from external institutions were subjected to DNA methylation analysis for diagnostic purposes. We describe our current approach to the integrated diagnosis of CNS tumors with a focus on constellations with conflicts between morphological and molecular genetic findings. We further describe the benefit of integrating DNA copy-number alterations into diagnostic considerations and provide a catalog of copy-number changes for individual DNA methylation classes. We also point to several pitfalls accompanying the diagnostic implementation of DNA methylation profiling and give practical suggestions for recurring diagnostic scenarios.
Summary 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.
DNA methylation data-based precision cancer diagnostics is emerging as the state of the art for molecular tumor classification. Standards for choosing statistical methods with regard to ...well-calibrated probability estimates for these typically highly multiclass classification tasks are still lacking. To support this choice, we evaluated well-established machine learning (ML) classifiers including random forests (RFs), elastic net (ELNET), support vector machines (SVMs) and boosted trees in combination with post-processing algorithms and developed ML workflows that allow for unbiased class probability (CP) estimation. Calibrators included ridge-penalized multinomial logistic regression (MR) and Platt scaling by fitting logistic regression (LR) and Firth's penalized LR. We compared these workflows on a recently published brain tumor 450k DNA methylation cohort of 2,801 samples with 91 diagnostic categories using a 5 × 5-fold nested cross-validation scheme and demonstrated their generalizability on external data from The Cancer Genome Atlas. ELNET was the top stand-alone classifier with the best calibration profiles. The best overall two-stage workflow was MR-calibrated SVM with linear kernels closely followed by ridge-calibrated tuned RF. For calibration, MR was the most effective regardless of the primary classifier. The protocols developed as a result of these comparisons provide valuable guidance on choosing ML workflows and their tuning to generate well-calibrated CP estimates for precision diagnostics using DNA methylation data. Computation times vary depending on the ML algorithm from <15 min to 5 d using multi-core desktop PCs. Detailed scripts in the open-source R language are freely available on GitHub, targeting users with intermediate experience in bioinformatics and statistics and using R with Bioconductor extensions.
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.
Oncogene-induced senescence (OIS) is crucial for tumour suppression. Senescent cells implement a complex pro-inflammatory response termed the senescence-associated secretory phenotype (SASP). The ...SASP reinforces senescence, activates immune surveillance and paradoxically also has pro-tumorigenic properties. Here, we present evidence that the SASP can also induce paracrine senescence in normal cells both in culture and in human and mouse models of OIS in vivo. Coupling quantitative proteomics with small-molecule screens, we identified multiple SASP components mediating paracrine senescence, including TGF-β family ligands, VEGF, CCL2 and CCL20. Amongst them, TGF-β ligands play a major role by regulating p15(INK4b) and p21(CIP1). Expression of the SASP is controlled by inflammasome-mediated IL-1 signalling. The inflammasome and IL-1 signalling are activated in senescent cells and IL-1α expression can reproduce SASP activation, resulting in senescence. Our results demonstrate that the SASP can cause paracrine senescence and impact on tumour suppression and senescence in vivo.
Missense mutations of the V600E type constitute the vast majority of tumor-associated somatic alterations in the v-RAF murine sarcoma viral oncogene homolog B1 (
BRAF
) gene. Initially described in ...melanoma, colon and papillary thyroid carcinoma, these alterations have also been observed in primary nervous system tumors albeit at a low frequency. We analyzed exon 15 of
BRAF
spanning the V600 locus by direct sequencing in 1,320 adult and pediatric tumors of the nervous system including various types of glial, embryonal, neuronal and glioneuronal, meningeal, adenohypophyseal/sellar, and peripheral nervous system tumors. A total of 96
BRAF
mutations were detected; 93 of the V600E type and 3 cases with a three base pair insertion between codons 599 and 600. The highest frequencies of
BRAF
V600E
mutations were found in WHO grade II pleomorphic xanthoastrocytomas (42/64; 66%) and pleomorphic xanthoastrocytomas with anaplasia (15/23; 65%), as well as WHO grade I gangliogliomas (14/77; 18%), WHO grade III anaplastic gangliogliomas (3/6) and pilocytic astrocytomas (9/97; 9%). In pilocytic astrocytomas
BRAF
V600E
mutation was strongly associated with extra-cerebellar location (
p
= 0.009) and was most frequent in diencephalic tumors (4/12; 33%). Glioblastomas and other gliomas were characterized by a low frequency or absence of mutations. No mutations were detected in non-glial tumors, including embryonal tumors, meningiomas, nerve sheath tumors and pituitary adenomas. The high mutation frequencies in pleomorphic xanthoastrocytomas, gangliogliomas and extra-cerebellar pilocytic astrocytomas implicate
BRAF
V600E
mutation as a valuable diagnostic marker for these rare tumor entities. Future clinical trials should address whether
BRAF
V600E
mutant brain tumor patients will benefit from
BRAF
V600E
-directed targeted therapies.
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.
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.
Astrocytoma and oligodendroglioma are histologically and genetically well-defined entities. The majority of astrocytomas harbor concurrent
TP53
and
ATRX
mutations, while most oligodendrogliomas carry ...the 1p/19q co-deletion. Both entities share high frequencies of
IDH
mutations. In contrast, oligoastrocytomas (OA) appear less clearly defined and, therefore, there is an ongoing debate whether these tumors indeed constitute an entity or whether they represent a mixed bag containing both astrocytomas and oligodendrogliomas. We investigated 43 OA diagnosed in different institutions employing histology, immunohistochemistry and in situ hybridization addressing surrogates for the molecular genetic markers
IDH1
R132H,
TP53
,
ATRX
and 1p/19q loss. In all but one OA the combination of nuclear p53 accumulation and ATRX loss was mutually exclusive with 1p/19q co-deletion. In 31/43 OA, only alterations typical for oligodendroglioma were observed, while in 11/43 OA, only indicators for mutations typical for astrocytomas were detected. A single case exhibited a distinct pattern, nuclear expression of p53, ATRX loss,
IDH1
mutation and partial 1p/19q loss. However, this was the only patient undergoing radiotherapy prior to surgery, possibly contributing to the acquisition of this uncommon combination. In OA with oligodendroglioma typical alterations, the portions corresponding to astrocytic part were determined as reactive, while in OA with astrocytoma typical alterations the portions corresponding to oligodendroglial differentiation were neoplastic. These data provide strong evidence against the existence of an independent OA entity.
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