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SCHOLARLY PUBLICATION ✓ Open Access

Sarcoma classification by DNA methylation profiling

Christian Koelsche, Daniel Schrimpf, Damian Stichel, Martin Sill, Felix Sahm, David Reuß, Mirjam Blattner, Barbara Christine Worst, Christoph E. Heilig, Katja Beck, Peter Horak, Simon Kreutzfeldt, Elke Paff, Sebastian Stark, Pascal David Johann, Florian Selt, Jonas Ecker, Dominik Sturm, Kristian W. Pajtler, Annekathrin Reinhardt, Annika K. Wefers, Philipp Sievers, Azadeh Ebrahimi, Abigail Kora Suwala, Francisco Fernández‐Klett, Belén Casalini, Andrey Korshunov, Volker Hovestadt, Felix K. F. Kommoss, Mark Kriegsmann

📄 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.

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