Rapid Classification of Sarcomas Using Methylation Fingerprint: A Pilot Study

Aviel Iluz, Myriam Maoz, Nir Lavi, Hanna Charbit, Omer Or, Noam Olshinka, Jonathan Abraham Demma, Mohammad Adileh, Marc Wygoda, Philip Blumenfeld, Masha Gliner-Ron, Yusef Azraq, Joshua Moss, Tamar Peretz, Amir Eden, Aviad Zick*, Iris Lavon*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

2 Scopus citations


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.

Original languageAmerican English
Article number4168
Issue number16
StatePublished - Aug 2023

Bibliographical note

Publisher Copyright:
© 2023 by the authors.


  • classification
  • copy-number
  • machine learning
  • methylation
  • nanopore
  • sarcoma


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