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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Main Authors: Lei Chen, Xiaoyong Pan, Yu-Hang Zhang, Xiaohua Hu, KaiYan Feng, Tao Huang, Yu-Dong Cai
Format: Article
Language:English
Published: Frontiers Media S.A. 2019-08-01
Series:Frontiers in Genetics
Subjects:
Online Access:https://www.frontiersin.org/article/10.3389/fgene.2019.00738/full
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spelling 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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