文章：

结合基因表达谱和机器学习诊断B细胞非霍奇金淋巴瘤

Combining gene expression profiling and machine learning to diagnose B-cell non-Hodgkin lymphoma

原文发布日期：2020-05-22

DOI: 10.1038/s41408-020-0322-5

类型: Article

开放获取: 是

英文摘要：

Non-Hodgkin B-cell lymphomas (B-NHLs) are a highly heterogeneous group of mature B-cell malignancies. Their classification thus requires skillful evaluation by expert hematopathologists, but the risk of error remains higher in these tumors than in many other areas of pathology. To facilitate diagnosis, we have thus developed a gene expression assay able to discriminate the seven most frequent B-cell NHL categories. This assay relies on the combination of ligation-dependent RT-PCR and next-generation sequencing, and addresses the expression of more than 130 genetic markers. It was designed to retrieve the main gene expression signatures of B-NHL cells and their microenvironment. The classification is handled by a random forest algorithm which we trained and validated on a large cohort of more than 400 annotated cases of different histology. Its clinical relevance was verified through its capacity to prevent important misclassification in low grade lymphomas and to retrieve clinically important characteristics in high grade lymphomas including the cell-of-origin signatures and the MYC and BCL2 expression levels. This accurate pan-B-NHL predictor, which allows a systematic evaluation of numerous diagnostic and prognostic markers, could thus be proposed as a complement to conventional histology to guide the management of patients and facilitate their stratification into clinical trials.
 

摘要翻译： 

非霍奇金B细胞淋巴瘤（B-NHLs）是一组高度异质性的成熟B细胞恶性肿瘤。因此，其分类需要由专业的血液病理学家进行熟练评估，但这类肿瘤的误诊风险仍远高于病理学的许多其他领域。为辅助诊断，我们开发了一种能够区分七种最常见B细胞非霍奇金淋巴瘤亚型的基因表达检测方法。该检测结合连接依赖性逆转录PCR与新一代测序技术，可对130余种遗传标志物的表达进行定量分析。其设计旨在捕捉B-NHL细胞及其微环境的主要基因表达特征。我们采用随机森林算法进行分类，该算法基于400余例不同组织学类型的标注病例进行训练和验证。通过有效避免低度恶性淋巴瘤的重大误分类，并能获取高度恶性淋巴瘤中具有临床重要性的特征（包括细胞起源特征及MYC、BCL2表达水平），验证了该方法的临床适用性。这种精准的全B-NHL预测因子可系统评估多种诊断及预后标志物，因此建议作为传统组织学检测的补充手段，以指导患者治疗管理并促进临床试验分层。

原文链接：

Combining gene expression profiling and machine learning to diagnose B-cell non-Hodgkin lymphoma