Sarcoma classification by DNA methylation profiling
Christian Koelsche(German Cancer Research Center), Daniel Schrimpf(German Cancer Research Center), Damian Stichel(German Cancer Research Center), Martin Sill(German Cancer Research Center), Felix Sahm(German Cancer Research Center), David Reuß(German Cancer Research Center), Mirjam Blattner(German Cancer Research Center), Barbara C. Worst(German Cancer Research Center), Christoph E. Heilig(German Cancer Research Center), Katja Beck(German Cancer Research Center), Peter Horak(German Cancer Research Center), Simon Kreutzfeldt(German Cancer Research Center), Elke Paff(German Cancer Research Center), Sebastian Stark(German Cancer Research Center), Pascal D. Johann(German Cancer Research Center), Florian Selt(German Cancer Research Center), Jonas Ecker(German Cancer Research Center), Dominik Sturm(German Cancer Research Center), Kristian W. Pajtler(German Cancer Research Center), Annekathrin Reinhardt(German Cancer Research Center), Annika K. Wefers(German Cancer Research Center), Philipp Sievers(German Cancer Research Center), Azadeh Ebrahimi(German Cancer Research Center), Abigail K. Suwala(German Cancer Research Center), Francisco Fernández‐Klett(German Cancer Research Center), Belén Casalini(German Cancer Research Center), Andrey Korshunov(German Cancer Research Center), Volker Hovestadt(Broad Institute), F. Kommoss(Heidelberg University), Mark Kriegsmann(Heidelberg University), Matthias Schick(German Cancer Research Center), Melanie Bewerunge‐Hudler(German Cancer Research Center), Till Milde(German Cancer Research Center), Olaf Witt(German Cancer Research Center), Andreas E. Kulozik(Heidelberg University), Marcel Kool(German Cancer Research Center), Laura Romero‐Pérez(Ludwig-Maximilians-Universität München), Thomas G. P. Grünewald(Ludwig-Maximilians-Universität München), Thomas Kirchner(LMU Klinikum), Wolfgang Wick(German Cancer Research Center), Michael Platten(German Cancer Research Center), Andreas Unterberg(Heidelberg University), Matthias Uhl(Heidelberg University), Amir Abdollahi(German Cancer Research Center), Jürgen Debus(German Cancer Research Center), Burkhard Lehner(Heidelberg University), Christian Thomas(University Hospital Münster), Martin Hasselblatt(University Hospital Münster), Werner Paulus(University Hospital Münster), Christian Hartmann(Medizinische Hochschule Hannover), Ori Staszewski(University of Freiburg), Marco Prinz(University of Freiburg), Jürgen Hench(University Hospital of Basel), Stephan Frank(University Hospital of Basel), Yvonne M.H. Versleijen‐Jonkers(Radboud University Nijmegen), Marije E. Weidema(Radboud University Nijmegen), Thomas Mentzel(Dermatopathologie Friedrichshafen), Klaus Griewank, Enrique de Álava(Instituto de Biomedicina de Sevilla), Juan Díaz‐Martín(Instituto de Biomedicina de Sevilla), Miguel Á. Idoate(Clinica Universidad de Navarra), Kenneth Tou En Chang(KK Women's and Children's Hospital), Sharon Y. Y. Low(National Neuroscience Institute), Adrián Cuevas-Bourdier(Laboratoire National de Santé), Michel Mittelbronn(University of Luxembourg), Martin Mynarek(Universität Hamburg), Stefan Rutkowski(Universität Hamburg), Ulrich Schüller(Universität Hamburg), Viktor Mautner(Universität Hamburg), Jens Schittenhelm, Jonathan Serrano(New York University), Matija Snuderl(New York University), Reinhard Büttner(University Hospital Cologne), Thomas Klingebiel(Goethe University Frankfurt), Rolf Buslei(Sozialstiftung Bamberg), Manfred Gessler(University of Würzburg), Pieter Wesseling(Princess Máxima Center), Winand N.M. Dinjens(Erasmus MC Cancer Institute), Sebastian Brandner(University College London Hospitals NHS Foundation Trust), Zane Jaunmuktane(University College London Hospitals NHS Foundation Trust), Iben Lyskjær(University College London), Peter Schirmacher(Heidelberg University), Albrecht Stenzinger(Heidelberg University), Benedikt Brors(German Cancer Research Center), Hanno Glimm(German Cancer Research Center), Christoph Heining(German Cancer Research Center), Òscar M. Tirado(Institut d'Investigació Biomédica de Bellvitge), Miguel Sáinz‐Jaspeado(Institut d'Investigació Biomédica de Bellvitge), Jaume Mora(Hospital Sant Joan de Déu Barcelona), Javier Alonso(Instituto de Salud Carlos III), Xavier García del Muro(Institut d'Investigació Biomédica de Bellvitge), Sebastián Morán(Institut d'Investigació Biomédica de Bellvitge), Manel Esteller(Institució Catalana de Recerca i Estudis Avançats), Jamal Benhamida(Memorial Sloan Kettering Cancer Center), Marc Ladanyi(Memorial Sloan Kettering Cancer Center), Eva Wardelmann(University Hospital Münster), Cristina R. Antonescu(Memorial Sloan Kettering Cancer Center), Adrienne M. Flanagan(Royal National Orthopaedic Hospital), Uta Dirksen(West German Heart and Vascular Center Essen), Peter Hohenberger(Heidelberg University), Daniel Baumhoer(University of Basel), Wolfgang Hartmann(University Hospital Münster), Christian Vokuhl(University Hospital Schleswig-Holstein), Uta Flucke(Radboud University Nijmegen), Iver Petersen(SRH Wald-Klinikum Gera), Gunhild Mechtersheimer(Heidelberg University), David Capper(Charité - Universitätsmedizin Berlin), David Jones(German Cancer Research Center), Stefan Fröhling(German Cancer Research Center), Stefan M. Pfister(German Cancer Research Center), Andreas von Deimling(German Cancer Research Center)
Cited by 537Open Access
Abstract
Sarcomas are malignant soft tissue and bone tumours affecting adults, adolescents and children. They represent a morphologically heterogeneous class of tumours and some entities lack defining histopathological features. Therefore, the diagnosis of sarcomas is burdened with a high inter-observer variability and misclassification rate. Here, we demonstrate classification of soft tissue and bone tumours using a machine learning classifier algorithm based on array-generated DNA methylation data. This sarcoma classifier is trained using a dataset of 1077 methylation profiles from comprehensively pre-characterized cases comprising 62 tumour methylation classes constituting a broad range of soft tissue and bone sarcoma subtypes across the entire age spectrum. The performance is validated in a cohort of 428 sarcomatous tumours, of which 322 cases were classified by the sarcoma classifier. Our results demonstrate the potential of the DNA methylation-based sarcoma classification for research and future diagnostic applications.
Related Papers
No related papers found
Powered by citation graph analysis