硅纯化提高了儿童低级别胶质瘤的DNA甲基化分类率。

IF 9.3 1区 医学 Q1 CLINICAL NEUROLOGY
Liv Jürgensen, Salvatore Benfatto, Simone Schmid, Bjarne Daenekas, Julia Großer, Pablo Hernáiz Driever, Arend Koch, David Capper, Volker Hovestadt
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引用次数: 0

摘要

使用海德堡分类器的DNA甲基化分类是一种用于中枢神经系统(CNS)肿瘤分子诊断的最新数据驱动方法。然而,许多儿童低级别胶质瘤(pLGG)样本未能产生可靠的基于甲基化的分类,这通常被怀疑是由于肿瘤细胞含量低。在这里,我们提出了一个快速的,基于参考的硅纯化框架,系统地从肿瘤谱中去除五种非恶性细胞类型(小胶质细胞,单核细胞,中性粒细胞,T细胞和神经元)的表观遗传特征,从而能够对以前不可分类的pLGG样本进行分类。为了验证我们的方法,我们分析了来自同一活检的成对DNA甲基化谱,其中一个最初可分类,另一个不可分类。纯化后,所有新可分类样本的预测与相应的初始可分类样本的分类相匹配(9/ 9,100%)。将我们的方法应用于两个独立的pLGG队列,可以对24.1%(26/108)和22.7%(5/22)先前无法分类的病例进行可靠分类。总之,我们的硅净化框架能够对以前无法分类的pLGG样本进行自信的分类,支持准确的分子诊断和及时的临床决策,并且可以无缝集成到当前的分类工作流程中。它与肿瘤类型、分类器和参考特征的独立性进一步表明,它有可能更广泛地应用于其他低纯度肿瘤类型。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
In silico purification improves DNA methylation-based classification rates of pediatric low-grade gliomas

DNA methylation-based classification using the Heidelberg Classifier is a state-of-the-art data-driven method for molecular diagnosis of central nervous system (CNS) tumors. However, many pediatric low-grade glioma (pLGG) samples fail to yield a confident methylation-based classification, often suspected due to low tumor cell content. Here, we present a rapid, reference-based in silico purification framework that systematically removes the epigenetic signatures of five non-malignant cell types—microglia, monocytes, neutrophils, T cells, and neurons—from tumor profiles to enable classification of previously non-classifiable pLGG samples. To validate our approach, we analyzed paired DNA methylation profiles from the same biopsy, where one was initially classifiable and the other was not. After purification, predictions for all newly classifiable samples matched the classification of their corresponding initially classifiable counterparts (9/9, 100%). Application of our method to two independent pLGG cohorts allowed confident classification in 24.1% (26/108) and 22.7% (5/22) of previously non-classifiable cases. In conclusion, our in silico purification framework enables confident classification of previously non-classifiable pLGG samples, supporting accurate molecular diagnosis and timely clinical decision-making, and can seamlessly be integrated into current classification workflows. Its independence from tumor type, classifier, and reference signatures further suggests the potential for broader application to other low-purity tumor types.

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来源期刊
Acta Neuropathologica
Acta Neuropathologica 医学-病理学
CiteScore
23.70
自引率
3.90%
发文量
118
审稿时长
4-8 weeks
期刊介绍: Acta Neuropathologica publishes top-quality papers on the pathology of neurological diseases and experimental studies on molecular and cellular mechanisms using in vitro and in vivo models, ideally validated by analysis of human tissues. The journal accepts Original Papers, Review Articles, Case Reports, and Scientific Correspondence (Letters). Manuscripts must adhere to ethical standards, including review by appropriate ethics committees for human studies and compliance with principles of laboratory animal care for animal experiments. Failure to comply may result in rejection of the manuscript, and authors are responsible for ensuring accuracy and adherence to these requirements.
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