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利用信息学工具在弥漫性大 B 细胞淋巴瘤中寻找精准医学的机会。

Using Informatics Tools to Identify Opportunities for Precision Medicine in Diffuse Large B-cell Lymphoma.

机构信息

Emory University, Atlanta, GA.

Emory University School of Medicine, Atlanta, GA.

出版信息

Clin Lymphoma Myeloma Leuk. 2020 Apr;20(4):234-243.e10. doi: 10.1016/j.clml.2019.12.003. Epub 2019 Dec 24.

Abstract

INTRODUCTION

Diffuse large B-cell lymphoma (DLBCL) is genetically and clinically heterogeneous. Despite advances in genomic subtyping, standard frontline chemoimmunotherapy has remained unchanged for years. As high-throughput analysis becomes more accessible, characterizing drug-gene interactions in DLBCL could support patient-specific treatment strategies.

MATERIALS AND METHODS

From our systematic literature review, we compiled a comprehensive list of somatic mutations implicated in DLBCL. We extracted reported and primary sequencing data for these mutations and assessed their association with signaling pathways, cell-of-origin subtypes, and clinical outcomes.

RESULTS

Twenty-two targetable mutations present in ≥ 5% of patients with DLBCL were associated with unfavorable outcomes, yielding a predicted population of 31.7% of DLBCL cases with poor-risk disease and candidacy for targeted therapy. A second review identified 256 studies that had characterized the drug-gene interactions for these mutations via in vitro studies, mouse models, and/or clinical trials.

CONCLUSIONS

Our novel approach linking the data from our systematic reviews with informatics tools identified high-risk DLBCL subgroups, DLBCL-specific drug-gene interactions, and potential populations for precision medicine trials.

摘要

简介

弥漫性大 B 细胞淋巴瘤(DLBCL)在遗传和临床上具有异质性。尽管在基因组亚分型方面取得了进展,但多年来标准的一线化疗免疫治疗方案仍未改变。随着高通量分析变得更加普及,对 DLBCL 中的药物-基因相互作用进行特征描述可能支持针对患者的治疗策略。

材料和方法

从我们的系统文献综述中,我们编制了一份综合的 DLBCL 中涉及的体细胞突变清单。我们提取了这些突变的报告和原始测序数据,并评估了它们与信号通路、细胞起源亚型和临床结局的关联。

结果

在≥5%的 DLBCL 患者中存在的 22 个可靶向突变与不良结局相关,这意味着有 31.7%的 DLBCL 病例具有高危疾病特征,适合进行靶向治疗。第二项综述确定了 256 项研究,这些研究通过体外研究、小鼠模型和/或临床试验对这些突变的药物-基因相互作用进行了特征描述。

结论

我们将系统综述数据与信息学工具相结合的新方法确定了高危 DLBCL 亚组、DLBCL 特异性药物-基因相互作用以及精准医学试验的潜在人群。

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