Rapid Classification of Sarcomas Using Methylation Fingerprint: A Pilot Study

SIMPLE SUMMARY: Sarcomas encompass a diverse range of cancers, resulting in intricate classification that contributes to treatment delays. The aim of this pilot study, conducted within a specific subset of sarcoma types, is to demonstrates the feasibility of methylation and copy-number variation data obtained from low-coverage whole-genome sequencing using Oxford Nanopore for rapid point-of-care sarcoma classification. Oxford Nanopore sequencers are relatively affordable for laboratories, unlike other technologies used in previous studies for methylation-based sarcoma classification. Our findings indicate that this method attained an overall correct classification rate of 78%. This study could serve as the foundation for a rapid point-of-care sarcoma classification test, facilitating timely and efficient care across diverse clinical settings. ABSTRACT: 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.