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Sarcoma classification by DNA methylation profiling.


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.

SUBMITTER: Koelsche C 

PROVIDER: S-EPMC7819999 | biostudies-literature | 2021 Jan

REPOSITORIES: biostudies-literature

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Sarcoma classification by DNA methylation profiling.

Koelsche Christian C   Schrimpf Daniel D   Stichel Damian D   Sill Martin M   Sahm Felix F   Reuss David E DE   Blattner Mirjam M   Worst Barbara B   Heilig Christoph E CE   Beck Katja K   Horak Peter P   Kreutzfeldt Simon S   Paff Elke E   Stark Sebastian S   Johann Pascal P   Selt Florian F   Ecker Jonas J   Sturm Dominik D   Pajtler Kristian W KW   Reinhardt Annekathrin A   Wefers Annika K AK   Sievers Philipp P   Ebrahimi Azadeh A   Suwala Abigail A   Fernández-Klett Francisco F   Casalini Belén B   Korshunov Andrey A   Hovestadt Volker V   Kommoss Felix K F FKF   Kriegsmann Mark M   Schick Matthias M   Bewerunge-Hudler Melanie M   Milde Till T   Witt Olaf O   Kulozik Andreas E AE   Kool Marcel M   Romero-Pérez Laura L   Grünewald Thomas G P TGP   Kirchner Thomas T   Wick Wolfgang W   Platten Michael M   Unterberg Andreas A   Uhl Matthias M   Abdollahi Amir A   Debus Jürgen J   Lehner Burkhard B   Thomas Christian C   Hasselblatt Martin M   Paulus Werner W   Hartmann Christian C   Staszewski Ori O   Prinz Marco M   Hench Jürgen J   Frank Stephan S   Versleijen-Jonkers Yvonne M H YMH   Weidema Marije E ME   Mentzel Thomas T   Griewank Klaus K   de Álava Enrique E   Martín Juan Díaz JD   Gastearena Miguel A Idoate MAI   Chang Kenneth Tou-En KT   Low Sharon Yin Yee SYY   Cuevas-Bourdier Adrian A   Mittelbronn Michel M   Mynarek Martin M   Rutkowski Stefan S   Schüller Ulrich U   Mautner Viktor F VF   Schittenhelm Jens J   Serrano Jonathan J   Snuderl Matija M   Büttner Reinhard R   Klingebiel Thomas T   Buslei Rolf R   Gessler Manfred M   Wesseling Pieter P   Dinjens Winand N M WNM   Brandner Sebastian S   Jaunmuktane Zane Z   Lyskjær Iben I   Schirmacher Peter P   Stenzinger Albrecht A   Brors Benedikt B   Glimm Hanno H   Heining Christoph C   Tirado Oscar M OM   Sáinz-Jaspeado Miguel M   Mora Jaume J   Alonso Javier J   Del Muro Xavier Garcia XG   Moran Sebastian S   Esteller Manel M   Benhamida Jamal K JK   Ladanyi Marc M   Wardelmann Eva E   Antonescu Cristina C   Flanagan Adrienne A   Dirksen Uta U   Hohenberger Peter P   Baumhoer Daniel D   Hartmann Wolfgang W   Vokuhl Christian C   Flucke Uta U   Petersen Iver I   Mechtersheimer Gunhild G   Capper David D   Jones David T W DTW   Fröhling Stefan S   Pfister Stefan M SM   von Deimling Andreas A  

Nature communications 20210121 1


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. Thi  ...[more]

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