TY - JOUR
T1 - Rapid Classification of Sarcomas Using Methylation Fingerprint
T2 - A Pilot Study
AU - Iluz, Aviel
AU - Maoz, Myriam
AU - Lavi, Nir
AU - Charbit, Hanna
AU - Or, Omer
AU - Olshinka, Noam
AU - Demma, Jonathan Abraham
AU - Adileh, Mohammad
AU - Wygoda, Marc
AU - Blumenfeld, Philip
AU - Gliner-Ron, Masha
AU - Azraq, Yusef
AU - Moss, Joshua
AU - Peretz, Tamar
AU - Eden, Amir
AU - Zick, Aviad
AU - Lavon, Iris
N1 - Publisher Copyright:
© 2023 by the authors.
PY - 2023/8
Y1 - 2023/8
N2 - 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.
AB - 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.
KW - classification
KW - copy-number
KW - machine learning
KW - methylation
KW - nanopore
KW - sarcoma
UR - http://www.scopus.com/inward/record.url?scp=85168916120&partnerID=8YFLogxK
U2 - 10.3390/cancers15164168
DO - 10.3390/cancers15164168
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C2 - 37627196
AN - SCOPUS:85168916120
SN - 2072-6694
VL - 15
JO - Cancers
JF - Cancers
IS - 16
M1 - 4168
ER -