Department of Haematology, Aalborg University Hospital, Aalborg, Denmark.
Department of Chemistry and Bioscience, Aalborg University, Aalborg, Denmark.
Blood Adv. 2018 Jul 10;2(13):1542-1546. doi: 10.1182/bloodadvances.2018017988.
Gene expression profiling (GEP) by microarrays of diffuse large B-cell lymphoma (DLBCL) has enabled the categorization of DLBCL into activated B-cell-like and germinal center B-cell-like subclasses. However, as this does not fully embrace the great diversity of B-cell subtypes, we recently developed a gene expression assay for B-cell-associated gene signature (BAGS) classification. To facilitate quick and easy-to-use BAGS profiling, we developed in this study the NanoString-based BAGS2Clinic assay. Microarray data from 4 different cohorts (n = 970) were used to select genes and train the assay. The locked assay was validated in an independent cohort of 88 sample biopsies. The assay showed good correspondence with the original BAGS classifier, with an overall accuracy of 84% (95% confidence interval, 72% to 93%) and a subtype-specific accuracy ranging between 80% and 99%. BAGS classification has the potential to provide valuable insight into tumor biology as well as differences in resistance to immuno- and chemotherapy that can lead to novel treatment strategies for DLBCL patients. BAGS2Clinic can facilitate this and the implementation of BAGS classification as a routine clinical tool to improve prognosis and treatment guidance for DLBCL patients.
基因表达谱（GEP）通过弥漫性大 B 细胞淋巴瘤（DLBCL）的微阵列分析，使 DLBCL 能够分为激活 B 细胞样和生发中心 B 细胞样亚类。然而，由于这并不能完全包含 B 细胞亚型的巨大多样性，我们最近开发了一种用于 B 细胞相关基因特征（BAGS）分类的基因表达检测方法。为了方便快速且易于使用的 BAGS 分析，我们在这项研究中开发了基于 NanoString 的 BAGS2Clinic 检测方法。使用来自 4 个不同队列（n = 970）的微阵列数据来选择基因并训练检测方法。锁定的检测方法在 88 个样本活检的独立队列中进行了验证。该检测方法与原始 BAGS 分类器具有良好的一致性，总体准确性为 84%（95%置信区间，72%至 93%），亚型特异性准确性在 80%至 99%之间。BAGS 分类具有提供有价值的肿瘤生物学见解以及对免疫和化疗耐药差异的潜力，这可能为 DLBCL 患者带来新的治疗策略。BAGS2Clinic 可以促进这一点，并将 BAGS 分类作为一种常规临床工具实施，以改善 DLBCL 患者的预后和治疗指导。
