Becker Kerstin, Pancoska Petr, Concin Nicole, Vanden Heuvel Kelly, Slade Neda, Fischer Margaret, Chalas Eva, Moll Ute M
Department of Pathology, State University of New York at Stony Brook, Stony Brook, NY, USA.
Int J Oncol. 2006 Oct;29(4):889-902.
The goal of this study was to determine whether patterns of expression profiles of p73 isoforms and of p53 mutational status are useful combinatorial biomarkers for predicting outcome in a gynecological cancer cohort. This is the first such study using matched tumor/normal tissue pairs from each patient. The median follow-up was over two years. The expression of all 5 N-terminal isoforms (TAp73, DeltaNp73, DeltaN'p73, Ex2p73 and Ex2/3p73) was measured by real-time RT-PCR and p53 status was analyzed by immunohistochemistry. TAp73, DeltaNp73 and DeltaN'p73 were significantly upregulated in tumors. Surprisingly, their range of overexpression was age-dependent, with the highest differences delta (tumor-normal) in the youngest age group. Correction of this age effect was important in further survival correlations. We used all 6 variables (five p73 isoform levels plus p53 status) as input into a principal component analysis with Varimax rotation (VrPCA) to filter out noise from non-disease related individual variability of p73 levels. Rationally selected and individually weighted principal components from each patient were then used to train a support vector machine (SVM) algorithm to predict clinical outcome. This SVM algorithm was able to predict correct outcome in 30 of the 35 patients. We use here a mathematical tool for pattern recognition that has been commonly used in e.g. microarray data mining and apply it for the first time in a prognostic model. We find that PCA/SVM is able to test a clinical hypothesis with robust statistics and show that p73 expression profiles and p53 status are useful prognostic biomarkers that differentiate patients with good vs. poor prognosis with gynecological cancers.
本研究的目的是确定p73亚型的表达谱模式和p53突变状态是否为预测妇科癌症队列预后的有用组合生物标志物。这是第一项使用来自每位患者的配对肿瘤/正常组织样本的此类研究。中位随访时间超过两年。通过实时逆转录聚合酶链反应测量所有5种N端亚型（TAp73、DeltaNp73、DeltaN'p73、Ex2p73和Ex2/3p73）的表达，并通过免疫组织化学分析p53状态。TAp73、DeltaNp73和DeltaN'p73在肿瘤中显著上调。令人惊讶的是，它们的过表达范围与年龄有关，在最年轻的年龄组中差异最大（肿瘤-正常）。在进一步的生存相关性分析中，校正这种年龄效应很重要。我们将所有6个变量（5种p73亚型水平加p53状态）作为输入，进行带方差最大化旋转的主成分分析（VrPCA），以滤除非疾病相关的p73水平个体变异性产生的噪声。然后，将从每位患者合理选择并单独加权的主成分用于训练支持向量机（SVM）算法，以预测临床结果。该SVM算法能够在35例患者中的30例中正确预测结果。我们在此使用一种模式识别数学工具，该工具常用于例如微阵列数据挖掘，并首次将其应用于预后模型。我们发现，主成分分析/支持向量机能够通过稳健的统计检验临床假设，并表明p73表达谱和p53状态是区分妇科癌症预后良好与预后不良患者的有用预后生物标志物。
