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, VIM and ABHD17C were highly expressed in the PDX from breast tissue and also highly expressed in breast cancer patients.

Additional Metadata
Keywords Gene expression profile, Monte Carlo feature selection, Patient-derived tumor xenograft, Rule learning algorithm, Support vector machine
Persistent URL dx.doi.org/10.3389/fgene.2019.00738, hdl.handle.net/1765/119012
Journal Frontiers in Genetics
Citation
Chen, L. (Lei), Pan, X, Zhang, Y.-H. (Yu-Hang), Hu, X. (Xiaohua), Feng, K. (KaiYan), Huang, T. (Tao), & Cai, Y.-D. (Yu-Dong). (2019). Primary tumor site specificity is preserved in patient-derived tumor xenograft models. Frontiers in Genetics, 10(JUL). doi:10.3389/fgene.2019.00738