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Rapid Classification of Sarcomas Using Methylation Fingerprint: A Pilot Study
Aviel Iluz1,2, Myriam Maoz3, Nir Lavi1,2,4
1Leslie and Michael Gaffin Center for Neuro-Oncology, Hadassah Medical Center and Faculty of Medicine, The Hebrew University of Jerusalem, Jerusalem 9190501, Israel.
Cancers
|August 26, 2023
Summary
Classifying sarcomas using nanopore sequencing and methylation signatures shows promise for faster diagnosis. This approach aids in identifying cancer types, though further validation is needed for clinical application.
Area of Science:
- Oncology
- Genomics
- Bioinformatics
Background:
- Sarcoma classification is complex, often causing treatment delays.
- Previous diagnostic methods relied on DNA aberrations and methylation profiles with machine learning.
- Accurate and timely sarcoma diagnosis is critical for effective patient treatment.
Purpose of the Study:
- To classify sarcomas using methylation signatures from low-coverage whole-genome sequencing.
- To evaluate the utility of nanopore sequencing for identifying copy-number alterations alongside methylation data.
- To develop and test a methylation-based classifier for sarcoma diagnosis.
Main Methods:
- DNA extraction from 23 suspected sarcoma samples.
- Low-coverage whole-genome sequencing using Oxford Nanopore technology.
- Application of a customized methylation classifier (nanoDx pipeline) with Random Forest and t-distributed stochastic neighbor embedding, alongside copy-number alteration detection.
Main Results:
- 20 out of 23 samples were successfully sequenced; 18 contained tumor tissue.
- 14 out of 18 tumor samples achieved classification concordant with pathology reports.
- Four classifications were discordant, highlighting areas for methodological improvement.
Conclusions:
- Nanopore sequencing combined with methylation analysis offers a potential method for sarcoma classification.
- Improvements in tissue handling, DNA extraction, and detection of other genetic alterations are necessary.
- Further validation in diverse cohorts could enable rapid, point-of-care sarcoma classification.
