Dataset Information

DNA methylation-based classification of ependymomas in adulthood: implications for diagnosis and treatment.

ABSTRACT:

Background

Ependymal tumors are glial tumors that commonly manifest in children and young adults. Their classification has remained entirely morphological until recently, and surgery and radiotherapy are the main treatment options, especially in adults. Here we sought to correlate DNA methylation profiles with clinical and pathological characteristics in the prospective cohort of the German Glioma Network.

Methods

Tumors from 122 adult patients with myxopapillary ependymoma, ependymoma, anaplastic ependymoma, subependymoma, or RELA fusion-positive ependymoma classified according to the World Health Organization (WHO) 2016 were subjected to DNA methylation profiling using the Illumina HumanMethylation450 BeadChip platform. Molecular data were correlated with histologic features and clinical characteristics.

Results

At a median follow-up of 86.7 months, only 22 patients experienced progression (18.0%) and 13 patients (10.7%) died. Each tumor could be assigned to one of the previously defined molecular ependymoma subgroups. All histologic subependymomas corresponded to subependymoma (SE) DNA methylation subgroups, but the reverse was not true: 19 histologic ependymomas (WHO grade II) were allocated to molecular SE groups. Similarly, all histological myxopapillary ependymomas were assigned to the molecularly defined spinal myxopapillary ependymoma (SP-MPE) class, but this molecular subgroup additionally included 15 WHO grade II ependymomas by histology. Overall, WHO grade II ependymomas distributed into 7 molecular subgroups.

Conclusion

Most adult patients with ependymoma show a favorable prognosis. Molecular classification may provide diagnostic and prognostic information beyond histology and facilitate patient stratification in future clinical trials. The prognostic significance of a subependymoma or myxopapillary ependymoma DNA methylation phenotype without corresponding histology requires further study.

Key points

1. Ependymoma diagnosed in adult patients most often shows a good prognosis. 2. Molecular classification can support diagnostic and prognostic information beyond histology.

SUBMITTER: Witt H

PROVIDER: S-EPMC6231197 | biostudies-literature | 2018 Nov

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

DNA methylation-based classification of ependymomas in adulthood: implications for diagnosis and treatment.

Witt Hendrik H Gramatzki Dorothee D Hentschel Bettina B Pajtler Kristian W KW Felsberg Jörg J Schackert Gabriele G Löffler Markus M Capper David D Sahm Felix F Sill Martin M von Deimling Andreas A Kool Marcel M Herrlinger Ulrich U Westphal Manfred M Pietsch Torsten T Reifenberger Guido G Pfister Stefan M SM Tonn Jörg C JC Weller Michael M

Neuro-oncology 20181101 12

<h4>Background</h4>Ependymal tumors are glial tumors that commonly manifest in children and young adults. Their classification has remained entirely morphological until recently, and surgery and radiotherapy are the main treatment options, especially in adults. Here we sought to correlate DNA methylation profiles with clinical and pathological characteristics in the prospective cohort of the German Glioma Network.<h4>Methods</h4>Tumors from 122 adult patients with myxopapillary ependymoma, epend ...[more]

PMID: 30053291

Similar Datasets

Project description:Sinonasal undifferentiated carcinoma (SNUC) is an aggressive malignancy harboring IDH2 R172 mutations in >80% cases. We explored the potential of genome-wide DNA methylation profiling to elucidate tumor biology and improve the diagnosis of sinonasal undifferentiated carcinoma and its histologic mimics. Forty-two cases, including sinonasal undifferentiated, large cell neuroendocrine, small cell neuroendocrine, and SMARCB1-deficient carcinomas and olfactory neuroblastoma, were profiled by Illumina Infinium Methylation EPIC array interrogating >850,000 CpG sites. The data were analyzed using a custom bioinformatics pipeline. IDH2 mutation status was determined by the targeted exome sequencing (MSK-IMPACTTM) in most cases. H3K27 methylation level was assessed by the immunohistochemistry-based H-score. DNA methylation-based semi-supervised hierarchical clustering analysis segregated IDH2 mutants, mostly sinonasal undifferentiated (n = 10) and large cell neuroendocrine carcinomas (n = 4), from other sinonasal tumors, and formed a single cluster irrespective of the histologic type. t-distributed stochastic neighbor embedding dimensionality reduction analysis showed no overlap between IDH2 mutants, SMARCB1-deficient carcinoma and olfactory neuroblastoma. IDH2 mutants demonstrated a global methylation phenotype and an increase in repressive trimethylation of H3K27 in comparison to IDH2 wild-type tumors (p < 0.001). Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis showed no difference in pathway activation between IDH2-mutated sinonasal undifferentiated and large cell neuroendocrine carcinomas. In comparison to SMARCB1-deficient, IDH2-mutated carcinomas were associated with better disease-free survival (p = 0.034) and lower propensity for lung metastasis (p = 0.002). ARID1A mutations were common in small cell neuroendocrine carcinoma but not among IDH2 mutants (3/3 versus 0/18 and p < 0.001). IDH2 mutations in sinonasal carcinomas induce a hypermethylator phenotype and define a molecular subgroup of tumors arising in this location. IDH2-mutated sinonasal undifferentiated carcinoma and large cell neuroendocrine carcinoma likely represent a phenotypic spectrum of the same entity, which is distinct from small cell neuroendocrine and SMARCB1-deficient sinonasal carcinomas. DNA methylation-based analysis of the sinonasal tumors has potential to improve the diagnostic accuracy and classification of tumors arising in this location.

Project description:Based on DNA-methylation, ependymomas growing in the spinal cord comprise two major molecular types termed spinal (SP-EPN) and myxopapillary ependymomas (MPE(-A/B)), which differ with respect to their clinical features and prognosis. Due to the existing discrepancy between histomorphogical diagnoses and classification using methylation data, we asked whether deep neural networks can predict the DNA methylation class of spinal cord ependymomas from hematoxylin and eosin stained whole-slide images. Using explainable AI, we further aimed to prospectively improve the consistency of histology-based diagnoses with DNA methylation profiling by identifying and quantifying distinct morphological patterns of these molecular ependymoma types. We assembled a case series of 139 molecularly characterized spinal cord ependymomas (nMPE = 84, nSP-EPN = 55). Self-supervised and weakly-supervised neural networks were used for classification. We employed attention analysis and supervised machine-learning methods for the discovery and quantification of morphological features and their correlation to the diagnoses of experienced neuropathologists. Our best performing model predicted the DNA methylation class with 98% test accuracy and used self-supervised learning to outperform pretrained encoder-networks (86% test accuracy). In contrast, the diagnoses of neuropathologists matched the DNA methylation class in only 83% of cases. Domain-adaptation techniques improved model generalization to an external validation cohort by up to 22%. Statistically significant morphological features were identified per molecular type and quantitatively correlated to human diagnoses. The approach was extended to recently defined subtypes of myxopapillary ependymomas (MPE-(A/B), 80% test accuracy). In summary, we demonstrated the accurate prediction of the DNA methylation class of spinal cord ependymomas (SP-EPN, MPE(-A/B)) using hematoxylin and eosin stained whole-slide images. Our approach may prospectively serve as a supplementary resource for integrated diagnostics and may even help to establish a standardized, high-quality level of histology-based diagnostics across institutions-in particular in low-income countries, where expensive DNA-methylation analyses may not be readily available.

Dataset Information

DNA methylation-based classification of ependymomas in adulthood: implications for diagnosis and treatment.

Background

Methods

Results

Conclusion

Key points

Publications

DNA methylation-based classification of ependymomas in adulthood: implications for diagnosis and treatment.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets