Metabolomics,Unknown,Transcriptomics,Genomics,Proteomics

Dataset Information

Robust Molecular Classification of Ependymal Tumors Across All Anatomic Compartments

ABSTRACT: Ependymal tumors across age groups have been classified solely by histopathology. It is, however, commonly accepted that this classification has limited clinical utility based on its poor reliability. We aimed at establishing a reliable and reproducible molecular classification using DNA methylation fingerprints of the tumors. Studying a cohort of 500 tumors allowed for the delineation of nine robust molecular subgroups, three in each anatomic compartment of the central nervous system (CNS). Two of the supratentorial subgroups are characterized by prototypic fusion genes involving RELA and YAP1, respectively. Regarding clinical associations, the molecular classification proposed herein outperforms the current histopathological classification by far and thus might serve as a basis for the upcoming update of the WHO classification of CNS tumors. DNA methylation patterns in tumors have been shown to represent a very stable molecular memory of the respective cell of origin throughout the disease course, thus making them particularly suitable for tumor classification purposes. Methylation fingerprinting of a large series of ependymal tumors of all grades revealed a highly reliable way of classifying this clinically extremely heterogeneous group of malignancies. In fact, out of nine highly reproducible molecular subgroups identified in the supratentorial, infratentorial and spinal regions, only two harbor the vast majority of clinical high-risk patients (mostly children) for whom novel therapeutic concepts are desperately needed. Since this analysis can be performed from minute amounts of DNA extracted from archived material, it is ideally suited for routine clinical application. We investigated a set of 562 ependymal tumors using the Illumina 450k methylation array.

ORGANISM(S): Homo sapiens

SUBMITTER: Martin Sill

PROVIDER: E-GEOD-65362 | biostudies-arrayexpress |

REPOSITORIES: biostudies-arrayexpress

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Publications

Molecular Classification of Ependymal Tumors across All CNS Compartments, Histopathological Grades, and Age Groups.

Pajtler Kristian W KW Witt Hendrik H Sill Martin M Jones David T W DT Hovestadt Volker V Kratochwil Fabian F Wani Khalida K Tatevossian Ruth R Punchihewa Chandanamali C Johann Pascal P Reimand Jüri J Warnatz Hans-Jörg HJ Ryzhova Marina M Mack Steve S Ramaswamy Vijay V Capper David D Schweizer Leonille L Sieber Laura L Wittmann Andrea A Huang Zhiqin Z van Sluis Peter P Volckmann Richard R Koster Jan J Versteeg Rogier R Fults Daniel D Toledano Helen H Avigad Smadar S Hoffman Lindsey M LM Donson Andrew M AM Foreman Nicholas N Hewer Ekkehard E Zitterbart Karel K Gilbert Mark M Armstrong Terri S TS Gupta Nalin N Allen Jeffrey C JC Karajannis Matthias A MA Zagzag David D Hasselblatt Martin M Kulozik Andreas E AE Witt Olaf O Collins V Peter VP von Hoff Katja K Rutkowski Stefan S Pietsch Torsten T Bader Gary G Yaspo Marie-Laure ML von Deimling Andreas A Lichter Peter P Taylor Michael D MD Gilbertson Richard R Ellison David W DW Aldape Kenneth K Korshunov Andrey A Kool Marcel M Pfister Stefan M SM

Cancer cell 20150501 5

Ependymal tumors across age groups are currently classified and graded solely by histopathology. It is, however, commonly accepted that this classification scheme has limited clinical utility based on its lack of reproducibility in predicting patients' outcome. We aimed at establishing a uniform molecular classification using DNA methylation profiling. Nine molecular subgroups were identified in a large cohort of 500 tumors, 3 in each anatomical compartment of the CNS, spine, posterior fossa, su ...[more]

PMID: 25965575

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Project description:Identification of novel molecular subgroups Background: International consensus recognises four medulloblastoma molecular subgroups - WNT, SHH, Group 3 and Group 4 - each defined by their characteristic genome-wide transcriptomic and DNA methylomic profiles. Subgroups harbor distinct clinico-pathological and molecular features, underpin current disease sub-classification and initial subgroup-directed therapies are underway in clinical trials (i.e. reduced risk-adapted treatments for favorable-risk WNT patients; SMO inhibitors for SHH patients). However, significant biological heterogeneity and differences in survival are apparent within each subgroup, which remain to be resolved. Methods: We undertook comprehensive molecular profiling and unsupervised class discovery (non-negative matrix factorization, t-SNE) of test and validation cohorts to identify consensus primary molecular subgroups within childhood medulloblastoma (<16.0 years), and characterize their clinical and biological significance. Survival modeling was performed in clinically-annotated centrally-reviewed patients (>3.0 years). Findings: Seven robust and reproducible primary molecular subgroups of childhood medulloblastoma were identified, characterized by distinct biological/clinical features. For instance, SHH comprised two age-dependent subgroups, while Grp3 and Grp4 each split into two subgroups with significantly different survival rates. Survival analysis identified secondary features predictive of outcome. Cross-validated subgroup-dependent models incorporating these novel subgroups along with secondary features and established disease risk-factors, outperformed current disease risk-stratification schemes. These schema stratified patients into four clinical risk-groups - favorable-risk (91% 5-year survival, 25% of patients), standard-risk (81%, 23%), high-risk (42%, 38%) and very high-risk (28%, 13%) - to be considered for treatment reduction, intensification or novel therapies respectively. Interpretation: The discovery of seven novel, clinically-significant, subgroups significantly improves disease risk-stratification and provides a new foundation for future research and clinical investigations.

Dataset Information

Robust Molecular Classification of Ependymal Tumors Across All Anatomic Compartments

Publications

Molecular Classification of Ependymal Tumors across All CNS Compartments, Histopathological Grades, and Age Groups.

Similar Datasets

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