文章：

利用靶向转录组和机器学习算法确定弥漫大B细胞淋巴瘤的临床病程

Determining clinical course of diffuse large B-cell lymphoma using targeted transcriptome and machine learning algorithms

原文发布日期：2022-02-01

DOI: 10.1038/s41408-022-00617-5

类型: Article

开放获取: 是

英文摘要：

Multiple studies have demonstrated that diffuse large B-cell lymphoma (DLBCL) can be divided into subgroups based on their biology; however, these biological subgroups overlap clinically. Using machine learning, we developed an approach to stratify patients with DLBCL into four subgroups based on survival characteristics. This approach uses data from the targeted transcriptome to predict these survival subgroups. Using the expression levels of 180 genes, our model reliably predicted the four survival subgroups and was validated using independent groups of patients. Multivariate analysis showed that this patient stratification strategy encompasses various biological characteristics of DLBCL, and only TP53 mutations remained an independent prognostic biomarker. This novel approach for stratifying patients with DLBCL, based on the clinical outcome of rituximab, cyclophosphamide, doxorubicin, vincristine, and prednisone therapy, can be used to identify patients who may not respond well to these types of therapy, but would otherwise benefit from alternative therapy and clinical trials.
 

摘要翻译： 

多项研究表明，弥漫大B细胞淋巴瘤（DLBCL）可根据生物学特征分为不同亚组，但这些生物学亚组在临床上存在重叠。通过机器学习技术，我们开发出一种根据生存特征将DLBCL患者分为四个亚组的方法。该方法利用靶向转录组数据预测这些生存亚组。通过180个基因的表达水平，我们的模型可稳定预测四个生存亚组，并已在独立患者群体中得到验证。多变量分析显示，这种患者分层策略涵盖了DLBCL的多种生物学特征，仅TP53突变仍保持独立的预后生物标志物地位。这种基于利妥昔单抗、环磷酰胺、多柔比星、长春新碱和泼尼松治疗方案临床结局的新型患者分层方法，可用于识别对常规疗法反应不佳、但可能从替代疗法和临床试验中获益的患者群体。

原文链接：

Determining clinical course of diffuse large B-cell lymphoma using targeted transcriptome and machine learning algorithms