具有治疗意义的弥漫性大 B 细胞淋巴瘤遗传亚型的概率分类工具。
A Probabilistic Classification Tool for Genetic Subtypes of Diffuse Large B Cell Lymphoma with Therapeutic Implications.
机构信息
Biometric Research Branch, Division of Cancer Diagnosis and Treatment, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA.
Lymphoid Malignancies Branch, National Cancer Institute, National Institutes of Health, Bethesda, MD 20892, USA.
出版信息
Cancer Cell. 2020 Apr 13;37(4):551-568.e14. doi: 10.1016/j.ccell.2020.03.015.
Abstract
The development of precision medicine approaches for diffuse large B cell lymphoma (DLBCL) is confounded by its pronounced genetic, phenotypic, and clinical heterogeneity. Recent multiplatform genomic studies revealed the existence of genetic subtypes of DLBCL using clustering methodologies. Here, we describe an algorithm that determines the probability that a patient's lymphoma belongs to one of seven genetic subtypes based on its genetic features. This classification reveals genetic similarities between these DLBCL subtypes and various indolent and extranodal lymphoma types, suggesting a shared pathogenesis. These genetic subtypes also have distinct gene expression profiles, immune microenvironments, and outcomes following immunochemotherapy. Functional analysis of genetic subtype models highlights distinct vulnerabilities to targeted therapy, supporting the use of this classification in precision medicine trials.
摘要
弥漫性大 B 细胞淋巴瘤（DLBCL）的精准医学方法的发展受到其明显的遗传、表型和临床异质性的阻碍。最近的多平台基因组研究使用聚类方法揭示了 DLBCL 的遗传亚型的存在。在这里，我们描述了一种算法，该算法根据其遗传特征确定患者的淋巴瘤属于七种遗传亚型之一的概率。这种分类揭示了这些 DLBCL 亚型与各种惰性和结外淋巴瘤类型之间的遗传相似性，提示存在共同的发病机制。这些遗传亚型也具有不同的基因表达谱、免疫微环境以及免疫化学疗法后的结果。遗传亚型模型的功能分析突出了对靶向治疗的不同脆弱性，支持在精准医学试验中使用这种分类。
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