文章：

利用甲基化指纹快速分类肉瘤：一项初步研究

Rapid Classification of Sarcomas Using Methylation Fingerprint: A Pilot Study

原文发布日期：18 August 2023

DOI: 10.3390/cancers15164168

类型: Article

开放获取: 是

英文摘要：

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.

摘要翻译： 

肉瘤分类具有挑战性，可能导致治疗延误。既往研究多采用DNA畸变及基于甲基化谱的机器学习分类器进行诊断。本研究旨在通过分析低覆盖度全基因组测序获得的甲基化特征（该方法同时可识别拷贝数变异）实现肉瘤分类。从23例疑似肉瘤样本中提取DNA，使用牛津纳米孔测序仪进行测序。基于甲基化的分类器在nanoDx流程中应用，并采用基于Illumina甲基化数据处理后的参考数据集进行定制化调整。分类分析采用随机森林算法和t分布随机邻域嵌入技术，拷贝数变异检测则使用专用R软件包。在涵盖有限肉瘤类型的23例样本中，20例成功完成测序，但病理学家确认其中2例不包含肿瘤组织。在18例肿瘤样本中，14例分类结果与病理报告一致。4例分类结果与病理报告存在差异，其中1例具有兼容性，3例显示明显分歧。改进组织处理、DNA提取方法以及检测点突变和染色体易位可提高准确性。我们设想，通过对多样化肿瘤队列的进一步验证，并结合甲基化分类与其他DNA畸变分析，有望实现基于纳米孔测序的快速、准确、床旁肉瘤分类。

原文链接：

Rapid Classification of Sarcomas Using Methylation Fingerprint: A Pilot Study