Sarcoma classification is challenging and can lead to treatment delays. Previous studies used DNA aberrations and machine-learning classifiers based on methylation profiles for diagnosis. We aimed to classify sarcomas by analyzing methylation signatures obtained from low-coverage whole-genome sequencing, which also identifies copy-number alterations. DNA was extracted from 23 suspected sarcoma samples and sequenced on an Oxford Nanopore sequencer. The methylation-based classifier, applied in the nanoDx pipeline, was customized using a reference set based on processed Illumina-based methylation data. Classification analysis utilized the Random Forest algorithm and t-distributed stochastic neighbor embedding, while copy-number alterations were detected using a designated R package. Out of the 23 samples encompassing a restricted range of sarcoma types, 20 were successfully sequenced, but two did not contain tumor tissue, according to the pathologist. Among the 18 tumor samples, 14 were classified as reported in the pathology results. Four classifications were discordant with the pathological report, with one compatible and three showing discrepancies. Improving tissue handling, DNA extraction methods, and detecting point mutations and translocations could enhance accuracy. We envision that rapid, accurate, point-of-care sarcoma classification using nanopore sequencing could be achieved through additional validation in a diverse tumor cohort and the integration of methylation-based classification and other DNA aberrations.

Background: Isocitrate dehydrogenase (IDH)-wildtype glioblastoma (GBM) is an aggressive, heterogeneous brain tumor with limited treatment options. This study employs DNA methylation-based deconvolution of GBM to define its cellular composition and its association with patient outcomes. Methods: We generated oligodendroglial precursor cells at various developmental stages from enriched human neural progenitor cultures and used their DNA methylation signatures, along with published signatures of brain tumor cell types and the tumor microenvironment, to deconvolve 263 adult GBMs (Heidelberg cohort). An independent cohort of 199 GBMs from The Cancer Genome Atlas (TCGA) and GEO, all treated with standard-of-care therapy, was similarly deconvolved. Kaplan-Meier survival analysis was used to assess the prognostic value of the neoplastic components. Results: GBM deconvolution uncovered distinct cellular compositions that differed between the neoplastic and non-neoplastic component. The neoplastic fractions averaged 70% of the tumor bulk and were predominantly composed of oligodendrocyte-like (43%) cell populations, in addition to oligodendrocyte precursor-like (27%), astrocyte-like (19%), and mesenchymal stem cell-like (11%) populations. The non-neoplastic fractions were enriched for macrophages, vascular cells, and immune cell populations. A higher oligodendrocyte-like signature was linked to poorer survival (median survival 14.3 vs. 15.3 months; P = .017), while a higher astrocyte-like signature correlated with improved survival (15.3 vs. 13.4 months; P = .044). Further, a higher astrocyte-to-oligodendrocyte ratio was associated with significantly longer survival (15.8 vs. 11.9 months; P < .00011). Conclusions: The methylation-based deconvolution data and analyses provided insight into GBM heterogeneity and highlighted the prognostic potential of the astrocyte-to-oligodendrocyte ratio and its application in personalized treatment strategies.