Program in Immunology, Fred Hutchinson Cancer Research Center, Seattle, Wash. 98109-1024, USA.
Exp Hematol. 2010 Mar;38(3):202-12. doi: 10.1016/j.exphem.2009.12.004. Epub 2009 Dec 24.
Analysis of the alphabeta T-cell receptor (TCR) repertoire in patients with myelodysplastic syndrome (MDS) using the technique of TCR beta-chain spectratyping has provided valuable insight into the pathophysiology of cytopenias in a subset of patients with this heterogeneous disorder. TCR beta-chain spectratypes are complex data sets, however, and statistical tools for their comprehensive analysis are limited. The objective of the present work was to develop a method to enable quantitative evaluation and global comparison of spectratype data from different individuals and to study the prevalence of TCR beta repertoire abnormalities in MDS patients.
We developed a robust statistical method based on k-means clustering analysis, and applied this method to analysis of the alphabeta TCR repertoires in 50 MDS patients and 23 age-matched healthy controls.
Cluster analysis identified a subset of 11 MDS patients with profoundly abnormal alphabeta TCR repertoires. This group of patients was characterized by advanced disease by International Prognostic Scoring System and World Health Organization criteria, increased expression of the Wilms' tumor-1 oncogene, increased bone marrow myeloblast count, and older age.
We have developed a robust analytic algorithm that enables the comparison of alphabeta TCR repertoires between individuals and have shown that abnormal alphabeta TCR repertoire is a feature of a subset of patients with advanced MDS.
通过 TCRβ 链谱系分析技术分析骨髓增生异常综合征(MDS)患者的αβ T 细胞受体(TCR)谱,为该病患者亚群细胞减少的病理生理学提供了有价值的见解。然而,TCRβ 链谱系是复杂的数据集,其综合分析的统计工具有限。本研究旨在开发一种方法,以便对来自不同个体的谱系数据进行定量评估和全面比较,并研究 MDS 患者 TCRβ 库异常的发生率。
我们开发了一种基于 K-均值聚类分析的稳健统计方法,并将该方法应用于 50 例 MDS 患者和 23 例年龄匹配的健康对照者的αβ TCR 库分析。
聚类分析确定了一组 11 例 MDS 患者,其αβ TCR 库存在严重异常。这群患者按国际预后评分系统和世界卫生组织标准处于疾病晚期,Wilms 瘤-1 癌基因表达增加,骨髓中原始细胞计数增加,年龄较大。
我们开发了一种稳健的分析算法,可用于比较个体之间的αβ TCR 库,并表明异常的αβ TCR 库是一部分疾病晚期 MDS 患者的特征。