| Literature DB >> 33479225 |
Christian Koelsche1,2,3, Daniel Schrimpf1,2, Damian Stichel2, Martin Sill4,5, Felix Sahm1,2, David E Reuss1,2, Mirjam Blattner4,6, Barbara Worst4,6,7, Christoph E Heilig8, Katja Beck8,9, Peter Horak8, Simon Kreutzfeldt8, Elke Paff4,6,7, Sebastian Stark4,6,7, Pascal Johann4,6,7, Florian Selt4,7,10, Jonas Ecker4,7,10, Dominik Sturm4,6,7, Kristian W Pajtler4,5,7, Annekathrin Reinhardt1,2, Annika K Wefers1,2, Philipp Sievers1,2, Azadeh Ebrahimi2, Abigail Suwala1,2, Francisco Fernández-Klett1,2, Belén Casalini2, Andrey Korshunov1,2, Volker Hovestadt11,12, Felix K F Kommoss3, Mark Kriegsmann3, Matthias Schick13, Melanie Bewerunge-Hudler13, Till Milde4,7,10, Olaf Witt4,7,10, Andreas E Kulozik4,7, Marcel Kool4,5, Laura Romero-Pérez14, Thomas G P Grünewald14, Thomas Kirchner15, Wolfgang Wick16,17, Michael Platten18,19, Andreas Unterberg20, Matthias Uhl21,22, Amir Abdollahi21,22,23,24, Jürgen Debus21,22,23,24, Burkhard Lehner25, Christian Thomas26, Martin Hasselblatt26, Werner Paulus26, Christian Hartmann27, Ori Staszewski28,29, Marco Prinz28,30,31, Jürgen Hench32, Stephan Frank32, Yvonne M H Versleijen-Jonkers33, Marije E Weidema33, Thomas Mentzel34, Klaus Griewank35, Enrique de Álava36,37, Juan Díaz Martín36, Miguel A Idoate Gastearena38, Kenneth Tou-En Chang39, Sharon Yin Yee Low40, Adrian Cuevas-Bourdier41, Michel Mittelbronn41,42,43,44, Martin Mynarek45, Stefan Rutkowski45, Ulrich Schüller45,46,47, Viktor F Mautner48, Jens Schittenhelm49, Jonathan Serrano50, Matija Snuderl50, Reinhard Büttner51, Thomas Klingebiel52, Rolf Buslei53, Manfred Gessler54, Pieter Wesseling55,56, Winand N M Dinjens57, Sebastian Brandner58,59, Zane Jaunmuktane59,60, Iben Lyskjær61, Peter Schirmacher3, Albrecht Stenzinger3, Benedikt Brors62, Hanno Glimm63,64,65,66, Christoph Heining64,65,66, Oscar M Tirado67, Miguel Sáinz-Jaspeado67, Jaume Mora68, Javier Alonso69, Xavier Garcia Del Muro70, Sebastian Moran71, Manel Esteller72,73,74,75, Jamal K Benhamida76, Marc Ladanyi76, Eva Wardelmann77, Cristina Antonescu76, Adrienne Flanagan78,79, Uta Dirksen80,81, Peter Hohenberger82, Daniel Baumhoer83, Wolfgang Hartmann84, Christian Vokuhl85, Uta Flucke86, Iver Petersen87,88, Gunhild Mechtersheimer3, David Capper89, David T W Jones4,6, Stefan Fröhling8, Stefan M Pfister4,5,7, Andreas von Deimling90,91.
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.Entities:
Mesh：
Year: 2021 PMID： 33479225 PMCID： PMC7819999 DOI： 10.1038/s41467-020-20603-4
Source DB: PubMed Journal: Nat Commun ISSN： 2041-1723 Impact factor: 17.694