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巴雷特食管中克隆多样性标志物的全面调查,作为食管腺癌进展的生物标志物。

A comprehensive survey of clonal diversity measures in Barrett's esophagus as biomarkers of progression to esophageal adenocarcinoma.

机构信息

Molecular and Cellular Oncogenesis Program, The Wistar Institute, School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania19104, USA.

出版信息

Cancer Prev Res (Phila). 2010 Nov;3(11):1388-97. doi: 10.1158/1940-6207.CAPR-10-0108. Epub 2010 Oct 12.

Abstract

Neoplastic progression is an evolutionary process driven by the generation of clonal diversity and natural selection on that diversity within a neoplasm. We hypothesized that clonal diversity is associated with risk of progression to cancer. We obtained molecular data from a cohort of 239 participants with Barrett's esophagus, including microsatellite shifts and loss of heterozygosity, DNA content tetraploidy and aneuploidy, methylation, and sequence mutations. Using these data, we tested all major diversity measurement methods, including genetic divergence and entropy-based measures, to determine which measures are correlated with risk of progression to esophageal adenocarcinoma. We also tested whether the use of different sets of loci and alterations to define clones (e.g., selectively advantageous versus evolutionarily neutral) improved the predictive value of the diversity indices. All diversity measures were strong and highly significant predictors of progression (Cox proportional hazards model, P < 0.001). The type of alterations evaluated had little effect on the predictive value of most of the diversity measures. In summary, diversity measures are robust predictors of progression to cancer in this cohort.

摘要

肿瘤的演进是一个进化过程,是由克隆多样性的产生以及在肿瘤内对这种多样性的自然选择所驱动的。我们假设克隆多样性与进展为癌症的风险相关。我们从一个包含 239 名 Barrett 食管患者的队列中获得了分子数据,包括微卫星改变和杂合性丢失、DNA 含量四倍体和非整倍体、甲基化和序列突变。利用这些数据,我们测试了所有主要的多样性测量方法,包括遗传距离和基于熵的度量方法,以确定哪些度量与进展为食管腺癌的风险相关。我们还测试了使用不同的基因座和改变来定义克隆(例如,选择性有利的与进化中性的)是否可以提高多样性指数的预测价值。所有的多样性测量都是进展的强有力且高度显著的预测因子(Cox 比例风险模型,P < 0.001)。评估的改变类型对大多数多样性测量的预测价值影响很小。总之,在这个队列中,多样性测量是癌症进展的有力预测因子。

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