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000168739 037__ $$aDKFZ-2021-01033
000168739 041__ $$aEnglish
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000168739 1001_ $$00000-0003-3446-1182$$aLyskjaer, Iben$$b0
000168739 245__ $$aDNA methylation-based profiling of bone and soft tissue tumours: a validation study of the 'DKFZ Sarcoma Classifier'.
000168739 260__ $$aChichester$$bWiley$$c2021
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000168739 500__ $$a2021 Jul;7(4):350-360
000168739 520__ $$aDiagnosing bone and soft tissue neoplasms remains challenging because of the large number of subtypes, many of which lack diagnostic biomarkers. DNA methylation profiles have proven to be a reliable basis for the classification of brain tumours and, following this success, a DNA methylation-based sarcoma classification tool from the Deutsches Krebsforschungszentrum (DKFZ) in Heidelberg has been developed. In this study, we assessed the performance of their classifier on DNA methylation profiles of an independent data set of 986 bone and soft tissue tumours and controls. We found that the 'DKFZ Sarcoma Classifier' was able to produce a diagnostic prediction for 55% of the 986 samples, with 83% of these predictions concordant with the histological diagnosis. On limiting the validation to the 820 cases with histological diagnoses for which the DKFZ Classifier was trained, 61% of cases received a prediction, and the histological diagnosis was concordant with the predicted methylation class in 88% of these cases, findings comparable to those reported in the DKFZ Classifier paper. The classifier performed best when diagnosing mesenchymal chondrosarcomas (CHSs, 88% sensitivity), chordomas (85% sensitivity), and fibrous dysplasia (83% sensitivity). Amongst the subtypes least often classified correctly were clear cell CHSs (14% sensitivity), malignant peripheral nerve sheath tumours (27% sensitivity), and pleomorphic liposarcomas (29% sensitivity). The classifier predictions resulted in revision of the histological diagnosis in six of our cases. We observed that, although a higher tumour purity resulted in a greater likelihood of a prediction being made, it did not correlate with classifier accuracy. Our results show that the DKFZ Classifier represents a powerful research tool for exploring the pathogenesis of sarcoma; with refinement, it has the potential to be a valuable diagnostic tool.
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000168739 650_7 $$2Other$$abone
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000168739 650_7 $$2Other$$amethylation profiling
000168739 650_7 $$2Other$$asarcoma
000168739 650_7 $$2Other$$asoft tissue
000168739 7001_ $$00000-0002-6380-8117$$aDe Noon, Solange$$b1
000168739 7001_ $$aTirabosco, Roberto$$b2
000168739 7001_ $$aRocha, Ana Maia$$b3
000168739 7001_ $$aLindsay, Daniel$$b4
000168739 7001_ $$aAmary, Fernanda$$b5
000168739 7001_ $$aYe, Hongtao$$b6
000168739 7001_ $$0P:(DE-He78)e54a1e0999c1d8c95869ef9188b794cc$$aSchrimpf, Daniel$$b7$$udkfz
000168739 7001_ $$0P:(DE-He78)d20d08adc992abdb6ccffa1686f1ba17$$aStichel, Damian$$b8$$udkfz
000168739 7001_ $$0P:(DE-He78)45440b44791309bd4b7dbb4f73333f9b$$aSill, Martin$$b9$$udkfz
000168739 7001_ $$00000-0001-8763-8864$$aKoelsche, Christian$$b10
000168739 7001_ $$0P:(DE-HGF)0$$aPillay, Nischalan$$b11
000168739 7001_ $$0P:(DE-He78)a8a10626a848d31e70cfd96a133cc144$$aVon Deimling, Andreas$$b12$$udkfz
000168739 7001_ $$aBeck, Stephan$$b13
000168739 7001_ $$00000-0002-2832-1303$$aFlanagan, Adrienne M$$b14
000168739 773__ $$0PERI:(DE-600)2814357-7$$a10.1002/cjp2.215$$gp. cjp2.215$$n4$$p350-360$$tThe journal of pathology: clinical research$$v7$$x2056-4538$$y2021
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