Primary Tumor Site Specificity is Preserved in Patient-Derived Tumor Xenograft Models
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...
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doaj-dad263d8438e4dcfbc9e27c69ac4f85c2020-11-24T22:15:25ZengFrontiers Media S.A.Frontiers in Genetics1664-80212019-08-011010.3389/fgene.2019.00738465832Primary Tumor Site Specificity is Preserved in Patient-Derived Tumor Xenograft ModelsLei Chen0Lei Chen1Lei Chen2Xiaoyong Pan3Yu-Hang Zhang4Xiaohua Hu5KaiYan Feng6Tao Huang7Yu-Dong Cai8Shanghai Institute of Nutrition and Health, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai, ChinaCollege of Information Engineering, Shanghai Maritime University, Shanghai, ChinaShanghai Key Laboratory of PMMP, East China Normal University, Shanghai, ChinaDepartment of Medical Informatics, Erasmus Medical Center, Rotterdam, NetherlandsShanghai Institute of Nutrition and Health, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai, ChinaDepartment of Biostatistics and Computational Biology, School of Life Sciences, Fudan University, Shanghai, ChinaDepartment of Computer Science, Guangdong AIB Polytechnic, Guangzhou, ChinaShanghai Institute of Nutrition and Health, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai, ChinaSchool of Life Sciences, Shanghai University, Shanghai, ChinaPatient-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.https://www.frontiersin.org/article/10.3389/fgene.2019.00738/fullPatient-derived tumor xenograftgene expression profileMonte Carlo feature selectionsupport vector machinerule learning algorithm |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Lei Chen Lei Chen Lei Chen Xiaoyong Pan Yu-Hang Zhang Xiaohua Hu KaiYan Feng Tao Huang Yu-Dong Cai |
spellingShingle |
Lei Chen Lei Chen Lei Chen Xiaoyong Pan Yu-Hang Zhang Xiaohua Hu KaiYan Feng Tao Huang Yu-Dong Cai Primary Tumor Site Specificity is Preserved in Patient-Derived Tumor Xenograft Models Frontiers in Genetics Patient-derived tumor xenograft gene expression profile Monte Carlo feature selection support vector machine rule learning algorithm |
author_facet |
Lei Chen Lei Chen Lei Chen Xiaoyong Pan Yu-Hang Zhang Xiaohua Hu KaiYan Feng Tao Huang Yu-Dong Cai |
author_sort |
Lei Chen |
title |
Primary Tumor Site Specificity is Preserved in Patient-Derived Tumor Xenograft Models |
title_short |
Primary Tumor Site Specificity is Preserved in Patient-Derived Tumor Xenograft Models |
title_full |
Primary Tumor Site Specificity is Preserved in Patient-Derived Tumor Xenograft Models |
title_fullStr |
Primary Tumor Site Specificity is Preserved in Patient-Derived Tumor Xenograft Models |
title_full_unstemmed |
Primary Tumor Site Specificity is Preserved in Patient-Derived Tumor Xenograft Models |
title_sort |
primary tumor site specificity is preserved in patient-derived tumor xenograft models |
publisher |
Frontiers Media S.A. |
series |
Frontiers in Genetics |
issn |
1664-8021 |
publishDate |
2019-08-01 |
description |
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. |
topic |
Patient-derived tumor xenograft gene expression profile Monte Carlo feature selection support vector machine rule learning algorithm |
url |
https://www.frontiersin.org/article/10.3389/fgene.2019.00738/full |
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