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Gene expression profiling of 49 human tumor xenografts from in vitro culture through multiple in vivo passages - strategies for data mining in support of therapeutic studies

BACKGROUND: Development of cancer therapeutics partially depends upon selection of appropriate animal models. Therefore, improvements to model selection are beneficial. RESULTS: Forty-nine human tumor xenografts at in vivo passages 1, 4 and 10 were subjected to cDNA microarray analysis yielding a da...

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Autores principales: Hollingshead, Melinda G, Stockwin, Luke H, Alcoser, Sergio Y, Newton, Dianne L, Orsburn, Benjamin C, Bonomi, Carrie A, Borgel, Suzanne D, Divelbiss, Raymond, Dougherty, Kelly M, Hager, Elizabeth J, Holbeck, Susan L, Kaur, Gurmeet, Kimmel, David J, Kunkel, Mark W, Millione, Angelena, Mullendore, Michael E, Stotler, Howard, Collins, Jerry
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2014
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4041995/
https://www.ncbi.nlm.nih.gov/pubmed/24885658
http://dx.doi.org/10.1186/1471-2164-15-393
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author Hollingshead, Melinda G
Stockwin, Luke H
Alcoser, Sergio Y
Newton, Dianne L
Orsburn, Benjamin C
Bonomi, Carrie A
Borgel, Suzanne D
Divelbiss, Raymond
Dougherty, Kelly M
Hager, Elizabeth J
Holbeck, Susan L
Kaur, Gurmeet
Kimmel, David J
Kunkel, Mark W
Millione, Angelena
Mullendore, Michael E
Stotler, Howard
Collins, Jerry
author_facet Hollingshead, Melinda G
Stockwin, Luke H
Alcoser, Sergio Y
Newton, Dianne L
Orsburn, Benjamin C
Bonomi, Carrie A
Borgel, Suzanne D
Divelbiss, Raymond
Dougherty, Kelly M
Hager, Elizabeth J
Holbeck, Susan L
Kaur, Gurmeet
Kimmel, David J
Kunkel, Mark W
Millione, Angelena
Mullendore, Michael E
Stotler, Howard
Collins, Jerry
author_sort Hollingshead, Melinda G
collection PubMed
description BACKGROUND: Development of cancer therapeutics partially depends upon selection of appropriate animal models. Therefore, improvements to model selection are beneficial. RESULTS: Forty-nine human tumor xenografts at in vivo passages 1, 4 and 10 were subjected to cDNA microarray analysis yielding a dataset of 823 Affymetrix HG-U133 Plus 2.0 arrays. To illustrate mining strategies supporting therapeutic studies, transcript expression was determined: 1) relative to other models, 2) with successive in vivo passage, and 3) during the in vitro to in vivo transition. Ranking models according to relative transcript expression in vivo has the potential to improve initial model selection. For example, combining p53 tumor expression data with mutational status could guide selection of tumors for therapeutic studies of agents where p53 status purportedly affects efficacy (e.g., MK-1775). The utility of monitoring changes in gene expression with extended in vivo tumor passages was illustrated by focused studies of drug resistance mediators and receptor tyrosine kinases. Noteworthy observations included a significant decline in HCT-15 colon xenograft ABCB1 transporter expression and increased expression of the kinase KIT in A549 with serial passage. These trends predict sensitivity to agents such as paclitaxel (ABCB1 substrate) and imatinib (c-KIT inhibitor) would be altered with extended passage. Given that gene expression results indicated some models undergo profound changes with in vivo passage, a general metric of stability was generated so models could be ranked accordingly. Lastly, changes occurring during transition from in vitro to in vivo growth may have important consequences for therapeutic studies since targets identified in vitro could be over- or under-represented when tumor cells adapt to in vivo growth. A comprehensive list of mouse transcripts capable of cross-hybridizing with human probe sets on the HG-U133 Plus 2.0 array was generated. Removal of the murine artifacts followed by pairwise analysis of in vitro cells with respective passage 1 xenografts and GO analysis illustrates the complex interplay that each model has with the host microenvironment. CONCLUSIONS: This study provides strategies to aid selection of xenograft models for therapeutic studies. These data highlight the dynamic nature of xenograft models and emphasize the importance of maintaining passage consistency throughout experiments. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi: 10.1186/1471-2164-15-393) contains supplementary material, which is available to authorized users.
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spelling pubmed-40419952014-06-06 Gene expression profiling of 49 human tumor xenografts from in vitro culture through multiple in vivo passages - strategies for data mining in support of therapeutic studies Hollingshead, Melinda G Stockwin, Luke H Alcoser, Sergio Y Newton, Dianne L Orsburn, Benjamin C Bonomi, Carrie A Borgel, Suzanne D Divelbiss, Raymond Dougherty, Kelly M Hager, Elizabeth J Holbeck, Susan L Kaur, Gurmeet Kimmel, David J Kunkel, Mark W Millione, Angelena Mullendore, Michael E Stotler, Howard Collins, Jerry BMC Genomics Research Article BACKGROUND: Development of cancer therapeutics partially depends upon selection of appropriate animal models. Therefore, improvements to model selection are beneficial. RESULTS: Forty-nine human tumor xenografts at in vivo passages 1, 4 and 10 were subjected to cDNA microarray analysis yielding a dataset of 823 Affymetrix HG-U133 Plus 2.0 arrays. To illustrate mining strategies supporting therapeutic studies, transcript expression was determined: 1) relative to other models, 2) with successive in vivo passage, and 3) during the in vitro to in vivo transition. Ranking models according to relative transcript expression in vivo has the potential to improve initial model selection. For example, combining p53 tumor expression data with mutational status could guide selection of tumors for therapeutic studies of agents where p53 status purportedly affects efficacy (e.g., MK-1775). The utility of monitoring changes in gene expression with extended in vivo tumor passages was illustrated by focused studies of drug resistance mediators and receptor tyrosine kinases. Noteworthy observations included a significant decline in HCT-15 colon xenograft ABCB1 transporter expression and increased expression of the kinase KIT in A549 with serial passage. These trends predict sensitivity to agents such as paclitaxel (ABCB1 substrate) and imatinib (c-KIT inhibitor) would be altered with extended passage. Given that gene expression results indicated some models undergo profound changes with in vivo passage, a general metric of stability was generated so models could be ranked accordingly. Lastly, changes occurring during transition from in vitro to in vivo growth may have important consequences for therapeutic studies since targets identified in vitro could be over- or under-represented when tumor cells adapt to in vivo growth. A comprehensive list of mouse transcripts capable of cross-hybridizing with human probe sets on the HG-U133 Plus 2.0 array was generated. Removal of the murine artifacts followed by pairwise analysis of in vitro cells with respective passage 1 xenografts and GO analysis illustrates the complex interplay that each model has with the host microenvironment. CONCLUSIONS: This study provides strategies to aid selection of xenograft models for therapeutic studies. These data highlight the dynamic nature of xenograft models and emphasize the importance of maintaining passage consistency throughout experiments. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi: 10.1186/1471-2164-15-393) contains supplementary material, which is available to authorized users. BioMed Central 2014-05-22 /pmc/articles/PMC4041995/ /pubmed/24885658 http://dx.doi.org/10.1186/1471-2164-15-393 Text en © Hollingshead et al.; licensee BioMed Central Ltd. 2014 This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly credited. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
spellingShingle Research Article
Hollingshead, Melinda G
Stockwin, Luke H
Alcoser, Sergio Y
Newton, Dianne L
Orsburn, Benjamin C
Bonomi, Carrie A
Borgel, Suzanne D
Divelbiss, Raymond
Dougherty, Kelly M
Hager, Elizabeth J
Holbeck, Susan L
Kaur, Gurmeet
Kimmel, David J
Kunkel, Mark W
Millione, Angelena
Mullendore, Michael E
Stotler, Howard
Collins, Jerry
Gene expression profiling of 49 human tumor xenografts from in vitro culture through multiple in vivo passages - strategies for data mining in support of therapeutic studies
title Gene expression profiling of 49 human tumor xenografts from in vitro culture through multiple in vivo passages - strategies for data mining in support of therapeutic studies
title_full Gene expression profiling of 49 human tumor xenografts from in vitro culture through multiple in vivo passages - strategies for data mining in support of therapeutic studies
title_fullStr Gene expression profiling of 49 human tumor xenografts from in vitro culture through multiple in vivo passages - strategies for data mining in support of therapeutic studies
title_full_unstemmed Gene expression profiling of 49 human tumor xenografts from in vitro culture through multiple in vivo passages - strategies for data mining in support of therapeutic studies
title_short Gene expression profiling of 49 human tumor xenografts from in vitro culture through multiple in vivo passages - strategies for data mining in support of therapeutic studies
title_sort gene expression profiling of 49 human tumor xenografts from in vitro culture through multiple in vivo passages - strategies for data mining in support of therapeutic studies
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4041995/
https://www.ncbi.nlm.nih.gov/pubmed/24885658
http://dx.doi.org/10.1186/1471-2164-15-393
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