<scp>DNA</scp> methylation‐based profiling of bone and soft tissue tumours: a validation study of the ‘<scp>DKFZ</scp> Sarcoma Classifier’

Iben Lyskjær(Cancer Research UK), Solange De Noon(Royal National Orthopaedic Hospital), Roberto Tirabosco(Royal National Orthopaedic Hospital), Ana Maia Rocha(Royal National Orthopaedic Hospital), Daniel Lindsay(Royal National Orthopaedic Hospital), Fernanda Amary(Royal National Orthopaedic Hospital), Hongtao Ye(Royal National Orthopaedic Hospital), Daniel Schrimpf(German Cancer Research Center), Damian Stichel(German Cancer Research Center), Martin Sill(German Cancer Research Center), Christian Koelsche(German Cancer Research Center), Nischalan Pillay(CRUK Lung Cancer Centre of Excellence), Andreas von Deimling(German Cancer Research Center), Stephan Beck(Cancer Research UK), Adrienne M. Flanagan(Royal National Orthopaedic Hospital)
The Journal of Pathology Clinical Research
May 5, 2021
Cited by 61Open Access
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Abstract

Diagnosing bone and soft tissue neoplasms remains challenging because of the large number of subtypes, many of which lack diagnostic biomarkers. DNA methylation profiles have proven to be a reliable basis for the classification of brain tumours and, following this success, a DNA methylation-based sarcoma classification tool from the Deutsches Krebsforschungszentrum (DKFZ) in Heidelberg has been developed. In this study, we assessed the performance of their classifier on DNA methylation profiles of an independent data set of 986 bone and soft tissue tumours and controls. We found that the 'DKFZ Sarcoma Classifier' was able to produce a diagnostic prediction for 55% of the 986 samples, with 83% of these predictions concordant with the histological diagnosis. On limiting the validation to the 820 cases with histological diagnoses for which the DKFZ Classifier was trained, 61% of cases received a prediction, and the histological diagnosis was concordant with the predicted methylation class in 88% of these cases, findings comparable to those reported in the DKFZ Classifier paper. The classifier performed best when diagnosing mesenchymal chondrosarcomas (CHSs, 88% sensitivity), chordomas (85% sensitivity), and fibrous dysplasia (83% sensitivity). Amongst the subtypes least often classified correctly were clear cell CHSs (14% sensitivity), malignant peripheral nerve sheath tumours (27% sensitivity), and pleomorphic liposarcomas (29% sensitivity). The classifier predictions resulted in revision of the histological diagnosis in six of our cases. We observed that, although a higher tumour purity resulted in a greater likelihood of a prediction being made, it did not correlate with classifier accuracy. Our results show that the DKFZ Classifier represents a powerful research tool for exploring the pathogenesis of sarcoma; with refinement, it has the potential to be a valuable diagnostic tool.


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