Ma Yan, Ding Zhenyu, Qian Yong, Wan Ying-Wooi, Tosun Kursad, Shi Xianglin, Castranova Vincent, Harner E James, Guo Nancy L
Mary Babb Randolph Cancer Center, West Virginia University, Morgantown, WV 26506-9300, USA.
Int J Oncol. 2009 Jan;34(1):107-15. doi: 10.3892/ijo_00000134.
New computational approaches are needed to integrate both protein expression and gene expression profiles, extending beyond the correlation analyses of gene and protein expression profiles in the current practices. Here, we developed an algorithm to classify cell line chemosensitivity based on integrated transcriptional and proteomic profiles. We sought to determine whether a combination of gene and protein expression profiles of untreated cells was able to enhance the performance of chemosensitivity prediction. An integrative feature selection scheme was employed to identify chemosensitivity determinants from genome-wide transcriptional profiles and 52 protein expression levels in 60 human cancer cell lines (the NCI-60). A set of 118 anti-cancer drugs whose mechanisms of action were putatively understood was evaluated. Classifiers of the complete range of drug response (sensitive, intermediate, or resistant) were generated for the evaluated anti-cancer drugs, one for each agent. The classifiers were designed to be independent of the cells' tissue origins. The classification accuracy of all the evaluated 118 agents was remarkably better (P<0.001) than that would be achieved by chance. Furthermore, 76 out of the 118 classifiers identified from integrated genomic and protein profiles significantly (P<0.05) improved the accuracy of protein expression-based classifiers identified previously. These results demonstrate that our integrated genomic and proteomic approach enhances the performance of chemosensitivity prediction. This study presents a new analytical framework to identify integrated gene and protein expression signatures for predicting cellular behavior and clinical outcome in general.
需要新的计算方法来整合蛋白质表达和基因表达谱，这超出了当前实践中基因和蛋白质表达谱的相关性分析。在此，我们开发了一种基于整合转录组和蛋白质组谱对细胞系化学敏感性进行分类的算法。我们试图确定未处理细胞的基因和蛋白质表达谱的组合是否能够提高化学敏感性预测的性能。采用一种整合特征选择方案，从60个人类癌细胞系（NCI - 60）的全基因组转录谱和52种蛋白质表达水平中识别化学敏感性决定因素。评估了一组118种作用机制已被初步了解的抗癌药物。针对所评估的抗癌药物，生成了涵盖所有药物反应范围（敏感、中间或耐药）的分类器，每种药物一个。这些分类器设计为独立于细胞的组织来源。所有118种评估药物的分类准确率显著高于随机水平（P<0.001）。此外，从整合基因组和蛋白质谱中识别出的118个分类器中有76个显著（P<0.05）提高了先前基于蛋白质表达的分类器的准确率。这些结果表明，我们的整合基因组和蛋白质组方法提高了化学敏感性预测的性能。本研究提出了一个新的分析框架，用于识别整合的基因和蛋白质表达特征，以总体预测细胞行为和临床结果。
