Reyal Fabien, van Vliet Martin H, Armstrong Nicola J, Horlings Hugo M, de Visser Karin E, Kok Marlen, Teschendorff Andrew E, Mook Stella, van 't Veer Laura, Caldas Carlos, Salmon Remy J, van de Vijver Marc J, Wessels Lodewyk F A
Department of Pathology, The Netherlands Cancer Institute, Plesmanlaan 121, 1066 CX Amsterdam, The Netherlands.
Breast Cancer Res. 2008;10(6):R93. doi: 10.1186/bcr2192. Epub 2008 Nov 13.
Several gene expression signatures have been proposed and demonstrated to be predictive of outcome in breast cancer. In the present article we address the following issues: Do these signatures perform similarly? Are there (common) molecular processes reported by these signatures? Can better prognostic predictors be constructed based on these identified molecular processes?
We performed a comprehensive analysis of the performance of nine gene expression signatures on seven different breast cancer datasets. To better characterize the functional processes associated with these signatures, we enlarged each signature by including all probes with a significant correlation to at least one of the genes in the original signature. The enrichment of functional groups was assessed using four ontology databases.
The classification performance of the nine gene expression signatures is very similar in terms of assigning a sample to either a poor outcome group or a good outcome group. Nevertheless the concordance in classification at the sample level is low, with only 50% of the breast cancer samples classified in the same outcome group by all classifiers. The predictive accuracy decreases with the number of poor outcome assignments given to a sample. The best classification performance was obtained for the group of patients with only good outcome assignments. Enrichment analysis of the enlarged signatures revealed 11 functional modules with prognostic ability. The combination of the RNA-splicing and immune modules resulted in a classifier with high prognostic performance on an independent validation set.
The study revealed that the nine signatures perform similarly but exhibit a large degree of discordance in prognostic group assignment. Functional analyses indicate that proliferation is a common cellular process, but that other functional categories are also enriched and show independent prognostic ability. We provide new evidence of the potentially promising prognostic impact of immunity and RNA-splicing processes in breast cancer.
已经提出了几种基因表达特征,并证明其可预测乳腺癌的预后。在本文中,我们探讨以下问题:这些特征的表现是否相似?这些特征所报告的(共同)分子过程是什么?基于这些已识别的分子过程能否构建出更好的预后预测指标?
我们对九个基因表达特征在七个不同乳腺癌数据集上的表现进行了全面分析。为了更好地表征与这些特征相关的功能过程,我们通过纳入与原始特征中至少一个基因具有显著相关性的所有探针来扩大每个特征。使用四个本体数据库评估功能组的富集情况。
就将样本分配到预后不良组或预后良好组而言,九个基因表达特征的分类表现非常相似。然而,样本水平上的分类一致性较低,所有分类器将只有50%的乳腺癌样本分类到相同的预后组。预测准确性随着分配给样本的预后不良分类数量的增加而降低。对于只有预后良好分类的患者组,获得了最佳分类表现。对扩大后的特征进行富集分析揭示了11个具有预后能力的功能模块。RNA剪接和免疫模块的组合在独立验证集上产生了具有高预后性能的分类器。
该研究表明,这九个特征表现相似,但在预后组分配方面存在很大程度的不一致。功能分析表明,增殖是一个常见细胞过程,但其他功能类别也有富集并显示出独立的预后能力。我们提供了新的证据,证明免疫和RNA剪接过程在乳腺癌中具有潜在的预后影响。