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

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.

Wholistic approach: Transcriptomic analysis and beyond using archival material for molecular diagnosis

“…Array-based DNA methylation profiling has been proven to be a powerful tool for the diagnosis of sarcoma and has been successfully applied to show proximity 85 or to discriminate between histologically overlapping neoplasms, [85][86][87] with a diagnostic prediction for 55% of cases in a recent validation study. 88 This method also provides useful information on CNV. Automatically combining and integrating different levels of omics data, such as the methylome, genome, and transcriptome, could reveal new nosological entities.…”

Advances in sarcoma molecular diagnostics

“…Oxford Nanopore Technologies MinION is a novel approach to WGS; instead of traditional polymerase-mediated DNA synthesis, MinION infers the nucleic acid sequence from ionic current changes in a study with a set of 986 sarcomas, where it was able to give a diagnostic prediction for 55% of cases, of which 83% were concordant with histology and six cases led to revisions of the histological diagnosis. 135 Although this low predictive rate and concordance suggest the technology is not yet ready for clinical application, future refinements of the profiler with additional methylomes will likely improve its diagnostic robustness.…”

Osteosarcoma: novel prognostic biomarkers using circulating and cell-free tumour DNA

“…Our in-house dataset consisted of 750 samples of various sarcoma subtypes (Supplementary Table 1) (EMBL-EBI, under accession number E-MTAB-9875) previously published 25 . Infinium 450K methylation data was downloaded from TCGA (https://www.cancer.gov/tcg) using TCGAbiolinks 26 and from Marmal-aid 27 (kindly provided by Dr Robert Lowe, UCL, QMUL, UK) (Supplementary Table 2).…”

“…In-house raw methylation array data have previously been published 25 and are available from the ArrayExpress database at EMBL-EBI (www.ebi.ac.uk/arrayexpress), accession number E-MTAB-9875.…”

“…10,766 samples were eligible for further analysis after filtering for incomplete data (Methods, Supplementary Figure 1A), and were analysed alongside 750 in-house sarcoma methylation profiles (Supplementary Figure 1A). Data from the remaining 10,766 samples were used for bioinformatic analysis together with 750 of our in-house sarcoma samples 25 . Hence, the final discovery group included methylome data from 11,516 samples, including 1,578 blood samples, 202 normal tissues, 9,565 non-OS cancer samples and 171 OS tumour samples (Supplementary Figure 1, Supplementary Tables 1 and 2).…”