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.
|Publication status||Published - 21 Jan 2021|
Bibliographical noteFunding Information:
This work was funded by Deutsche Krebshilfe grant 70112499, the NCT Heidelberg and an Illumina Medical Research Grant. Part of this work was funded by the National Institute of Health Research (to S.B. and Z.J.) and to UCLH Biomedical research centre (BRC399/NS/RB/101410). Human tissues were obtained from University College London NHS Foundation Trust as part of the UK Brain Archive Information Network (BRAIN UK, Ref: 18/004) which is funded by the Medical Research Council and Brain Tumour Research UK. The methylation profiling at NYU is supported by a grant from the Friedberg Charitable Foundation (to M.Sn.). M.Mi. would like to thank the Luxembourg National Research Fond (FNR) for the support (FNR PEARL P16/BM/ 11192868 grant).
© 2021, The Author(s).