Dataset Information

New Brain Tumor Entities Emerge from Molecular Classification of CNS-PNETs.

ABSTRACT: Primitive neuroectodermal tumors of the central nervous system (CNS-PNETs) are highly aggressive, poorly differentiated embryonal tumors occurring predominantly in young children but also affecting adolescents and adults. Herein, we demonstrate that a significant proportion of institutionally diagnosed CNS-PNETs display molecular profiles indistinguishable from those of various other well-defined CNS tumor entities, facilitating diagnosis and appropriate therapy for patients with these tumors. From the remaining fraction of CNS-PNETs, we identify four new CNS tumor entities, each associated with a recurrent genetic alteration and distinct histopathological and clinical features. These new molecular entities, designated "CNS neuroblastoma with FOXR2 activation (CNS NB-FOXR2)," "CNS Ewing sarcoma family tumor with CIC alteration (CNS EFT-CIC)," "CNS high-grade neuroepithelial tumor with MN1 alteration (CNS HGNET-MN1)," and "CNS high-grade neuroepithelial tumor with BCOR alteration (CNS HGNET-BCOR)," will enable meaningful clinical trials and the development of therapeutic strategies for patients affected by poorly differentiated CNS tumors.

SUBMITTER: Sturm D

PROVIDER: S-EPMC5139621 | biostudies-literature | 2016 Feb

REPOSITORIES: biostudies-literature

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New Brain Tumor Entities Emerge from Molecular Classification of CNS-PNETs.

Sturm Dominik D Orr Brent A BA Toprak Umut H UH Hovestadt Volker V Jones David T W DTW Capper David D Sill Martin M Buchhalter Ivo I Northcott Paul A PA Leis Irina I Ryzhova Marina M Koelsche Christian C Pfaff Elke E Allen Sariah J SJ Balasubramanian Gnanaprakash G Worst Barbara C BC Pajtler Kristian W KW Brabetz Sebastian S Johann Pascal D PD Sahm Felix F Reimand Jüri J Mackay Alan A Carvalho Diana M DM Remke Marc M Phillips Joanna J JJ Perry Arie A Cowdrey Cynthia C Drissi Rachid R Fouladi Maryam M Giangaspero Felice F Łastowska Maria M Grajkowska Wiesława W Scheurlen Wolfram W Pietsch Torsten T Hagel Christian C Gojo Johannes J Lötsch Daniela D Berger Walter W Slavc Irene I Haberler Christine C Jouvet Anne A Holm Stefan S Hofer Silvia S Prinz Marco M Keohane Catherine C Fried Iris I Mawrin Christian C Scheie David D Mobley Bret C BC Schniederjan Matthew J MJ Santi Mariarita M Buccoliero Anna M AM Dahiya Sonika S Kramm Christof M CM von Bueren André O AO von Hoff Katja K Rutkowski Stefan S Herold-Mende Christel C Frühwald Michael C MC Milde Till T Hasselblatt Martin M Wesseling Pieter P Rößler Jochen J Schüller Ulrich U Ebinger Martin M Schittenhelm Jens J Frank Stephan S Grobholz Rainer R Vajtai Istvan I Hans Volkmar V Schneppenheim Reinhard R Zitterbart Karel K Collins V Peter VP Aronica Eleonora E Varlet Pascale P Puget Stephanie S Dufour Christelle C Grill Jacques J Figarella-Branger Dominique D Wolter Marietta M Schuhmann Martin U MU Shalaby Tarek T Grotzer Michael M van Meter Timothy T Monoranu Camelia-Maria CM Felsberg Jörg J Reifenberger Guido G Snuderl Matija M Forrester Lynn Ann LA Koster Jan J Versteeg Rogier R Volckmann Richard R van Sluis Peter P Wolf Stephan S Mikkelsen Tom T Gajjar Amar A Aldape Kenneth K Moore Andrew S AS Taylor Michael D MD Jones Chris C Jabado Nada N Karajannis Matthias A MA Eils Roland R Schlesner Matthias M Lichter Peter P von Deimling Andreas A Pfister Stefan M SM Ellison David W DW Korshunov Andrey A Kool Marcel M

Cell 20160201 5

Primitive neuroectodermal tumors of the central nervous system (CNS-PNETs) are highly aggressive, poorly differentiated embryonal tumors occurring predominantly in young children but also affecting adolescents and adults. Herein, we demonstrate that a significant proportion of institutionally diagnosed CNS-PNETs display molecular profiles indistinguishable from those of various other well-defined CNS tumor entities, facilitating diagnosis and appropriate therapy for patients with these tumors. F ...[more]

PMID: 26919435

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Project description:The Biopharmaceutics Drug Disposition Classification System (BDDCS) was successfully employed for predicting drug-drug interactions (DDIs) with respect to drug metabolizing enzymes (DMEs), drug transporters and their interplay. The major assumption of BDDCS is that the extent of metabolism (EoM) predicts high versus low intestinal permeability rate, and vice versa, at least when uptake transporters or paracellular transport is not involved. We recently published a collection of over 900 marketed drugs classified for BDDCS. We suggest that a reliable model for predicting BDDCS class, integrated with in vitro assays, could anticipate disposition and potential DDIs of new molecular entities (NMEs). Here we describe a computational procedure for predicting BDDCS class from molecular structures. The model was trained on a set of 300 oral drugs, and validated on an external set of 379 oral drugs, using 17 descriptors calculated or derived from the VolSurf+ software. For each molecule, a probability of BDDCS class membership was given, based on predicted EoM, FDA solubility (FDAS) and their confidence scores. The accuracy in predicting FDAS was 78% in training and 77% in validation, while for EoM prediction the accuracy was 82% in training and 79% in external validation. The actual BDDCS class corresponded to the highest ranked calculated class for 55% of the validation molecules, and it was within the top two ranked more than 92% of the time. The unbalanced stratification of the data set did not affect the prediction, which showed highest accuracy in predicting classes 2 and 3 with respect to the most populated class 1. For class 4 drugs a general lack of predictability was observed. A linear discriminant analysis (LDA) confirming the degree of accuracy for the prediction of the different BDDCS classes is tied to the structure of the data set. This model could routinely be used in early drug discovery to prioritize in vitro tests for NMEs (e.g., affinity to transporters, intestinal metabolism, intestinal absorption and plasma protein binding). We further applied the BDDCS prediction model on a large set of medicinal chemistry compounds (over 30,000 chemicals). Based on this application, we suggest that solubility, and not permeability, is the major difference between NMEs and drugs. We anticipate that the forecast of BDDCS categories in early drug discovery may lead to a significant R&D cost reduction.

Project description:BackgroundMolecular analyses have shown that tumours diagnosed as supratentorial primitive neuro-ectodermal tumours of the central nervous system (CNS-PNETs) in the past represent a heterogenous group of rare childhood tumours including high-grade gliomas (HGG), ependymomas, atypical teratoid/rhabdoid tumours (AT/RT), CNS neuroblastoma with forkhead box R2 (FOXR2) activation and embryonal tumour with multi-layered rosettes (ETMR). All these tumour types are rare and long-term clinical follow-up data are sparse. We retrospectively re-evaluated all children (0-18 years old) diagnosed with a CNS-PNET in Sweden during 1984-2015 and collected clinical data.MethodsIn total, 88 supratentorial CNS-PNETs were identified in the Swedish Childhood Cancer Registry and from these formalin-fixed paraffin-embedded tumour material was available for 71 patients. These tumours were histopathologically re-evaluated and, in addition, analysed using genome-wide DNA methylation profiling and classified by the MNP brain tumour classifier.ResultsThe most frequent tumour types, after histopathological re-evaluation, were HGG (35%) followed by AT/RT (11%), CNS NB-FOXR2 (10%) and ETMR (8%). DNA methylation profiling could further divide the tumours into specific subtypes and with a high accuracy classify these rare embryonal tumours. The 5 and 10-year overall survival (OS) for the whole CNS-PNET cohort was 45% ± 12% and 42% ± 12%, respectively. However, the different groups of tumour types identified after re-evaluation displayed very variable survival patterns, with a poor outcome for HGG and ETMR patients with 5-year OS 20% ± 16% and 33% ± 35%, respectively. On the contrary, high PFS and OS was observed for patients with CNS NB-FOXR2 (5-year 100% for both). Survival rates remained stable even after 15-years of follow-up.ConclusionsOur findings demonstrate, in a national based setting, the molecular heterogeneity of these tumours and show that DNA methylation profiling of these tumours provides an indispensable tool in distinguishing these rare tumours. Long-term follow-up data confirms previous findings with a favourable outcome for CNS NB-FOXR2 tumours and poor chances of survival for ETMR and HGG.

Dataset Information

New Brain Tumor Entities Emerge from Molecular Classification of CNS-PNETs.

Publications

New Brain Tumor Entities Emerge from Molecular Classification of CNS-PNETs.

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