Chen Lei, Pan Xiaoyong, Zhang Yu-Hang, Hu Xiaohua, Feng KaiYan, Huang Tao, Cai Yu-Dong
Shanghai Institute of Nutrition and Health, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai, China.
College of Information Engineering, Shanghai Maritime University, Shanghai, China.
Front Genet. 2019 Aug 13;10:738. doi: 10.3389/fgene.2019.00738. eCollection 2019.
Patient-derived tumor xenograft (PDX) mouse models are widely used for drug screening. The underlying assumption is that PDX tissue is very similar with the original patient tissue, and it has the same response to the drug treatment. To investigate whether the primary tumor site information is well preserved in PDX, we analyzed the gene expression profiles of PDX mouse models originated from different tissues, including breast, kidney, large intestine, lung, ovary, pancreas, skin, and soft tissues. The popular Monte Carlo feature selection method was employed to analyze the expression profile, yielding a feature list. From this list, incremental feature selection and support vector machine (SVM) were adopted to extract distinctively expressed genes in PDXs from different primary tumor sites and build an optimal SVM classifier. In addition, we also set up a group of quantitative rules to identify primary tumor sites. A total of 755 genes were extracted by the feature selection procedures, on which the SVM classifier can provide a high performance with MCC 0.986 on classifying primary tumor sites originated from different tissues. Furthermore, we obtained 16 classification rules, which gave a lower accuracy but clear classification procedures. Such results validated that the primary tumor site specificity was well preserved in PDX as the PDXs from different primary tumor sites were still very different and these PDX differences were similar with the differences observed in patients with tumor. For example, and were highly expressed in the PDX from breast tissue and also highly expressed in breast cancer patients.
患者来源的肿瘤异种移植（PDX）小鼠模型被广泛用于药物筛选。其潜在假设是PDX组织与原始患者组织非常相似，并且对药物治疗具有相同的反应。为了研究原发性肿瘤部位信息在PDX中是否得到很好的保留，我们分析了源自不同组织（包括乳腺、肾脏、大肠、肺、卵巢、胰腺、皮肤和软组织）的PDX小鼠模型的基因表达谱。采用流行的蒙特卡罗特征选择方法分析表达谱，生成一个特征列表。从该列表中，采用增量特征选择和支持向量机（SVM）从不同原发性肿瘤部位的PDX中提取差异表达基因，并构建一个最优的SVM分类器。此外，我们还建立了一组定量规则来识别原发性肿瘤部位。通过特征选择程序共提取了755个基因，基于这些基因，SVM分类器在对源自不同组织的原发性肿瘤部位进行分类时，马修斯相关系数（MCC）为0.986，具有较高的性能。此外，我们获得了16条分类规则，其准确性较低，但分类程序清晰。这些结果证实，原发性肿瘤部位的特异性在PDX中得到了很好的保留，因为来自不同原发性肿瘤部位的PDX仍然非常不同，并且这些PDX差异与肿瘤患者中观察到的差异相似。例如，[具体基因1]和[具体基因2]在乳腺组织来源的PDX中高表达，在乳腺癌患者中也高表达。
