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Proteogenomic integration reveals therapeutic targets in breast cancer xenografts
Recent advances in mass spectrometry (MS) have enabled extensive analysis of cancer proteomes. Here, we employed quantitative proteomics to profile protein expression across 24 breast cancer patient-derived xenograft (PDX) models. Integrated proteogenomic analysis shows positive correlation between...
Autores principales: | , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group
2017
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5379071/ https://www.ncbi.nlm.nih.gov/pubmed/28348404 http://dx.doi.org/10.1038/ncomms14864 |
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author | Huang, Kuan-lin Li, Shunqiang Mertins, Philipp Cao, Song Gunawardena, Harsha P. Ruggles, Kelly V. Mani, D. R. Clauser, Karl R. Tanioka, Maki Usary, Jerry Kavuri, Shyam M. Xie, Ling Yoon, Christopher Qiao, Jana W Wrobel, John Wyczalkowski, Matthew A. Erdmann-Gilmore, Petra Snider, Jacqueline E. Hoog, Jeremy Singh, Purba Niu, Beifang Guo, Zhanfang Sun, Sam Qiancheng Sanati, Souzan Kawaler, Emily Wang, Xuya Scott, Adam Ye, Kai McLellan, Michael D. Wendl, Michael C. Malovannaya, Anna Held, Jason M. Gillette, Michael A. Fenyö, David Kinsinger, Christopher R. Mesri, Mehdi Rodriguez, Henry Davies, Sherri R. Perou, Charles M. Ma, Cynthia Reid Townsend, R. Chen, Xian Carr, Steven A. Ellis, Matthew J. Ding, Li |
author_facet | Huang, Kuan-lin Li, Shunqiang Mertins, Philipp Cao, Song Gunawardena, Harsha P. Ruggles, Kelly V. Mani, D. R. Clauser, Karl R. Tanioka, Maki Usary, Jerry Kavuri, Shyam M. Xie, Ling Yoon, Christopher Qiao, Jana W Wrobel, John Wyczalkowski, Matthew A. Erdmann-Gilmore, Petra Snider, Jacqueline E. Hoog, Jeremy Singh, Purba Niu, Beifang Guo, Zhanfang Sun, Sam Qiancheng Sanati, Souzan Kawaler, Emily Wang, Xuya Scott, Adam Ye, Kai McLellan, Michael D. Wendl, Michael C. Malovannaya, Anna Held, Jason M. Gillette, Michael A. Fenyö, David Kinsinger, Christopher R. Mesri, Mehdi Rodriguez, Henry Davies, Sherri R. Perou, Charles M. Ma, Cynthia Reid Townsend, R. Chen, Xian Carr, Steven A. Ellis, Matthew J. Ding, Li |
author_sort | Huang, Kuan-lin |
collection | PubMed |
description | Recent advances in mass spectrometry (MS) have enabled extensive analysis of cancer proteomes. Here, we employed quantitative proteomics to profile protein expression across 24 breast cancer patient-derived xenograft (PDX) models. Integrated proteogenomic analysis shows positive correlation between expression measurements from transcriptomic and proteomic analyses; further, gene expression-based intrinsic subtypes are largely re-capitulated using non-stromal protein markers. Proteogenomic analysis also validates a number of predicted genomic targets in multiple receptor tyrosine kinases. However, several protein/phosphoprotein events such as overexpression of AKT proteins and ARAF, BRAF, HSP90AB1 phosphosites are not readily explainable by genomic analysis, suggesting that druggable translational and/or post-translational regulatory events may be uniquely diagnosed by MS. Drug treatment experiments targeting HER2 and components of the PI3K pathway supported proteogenomic response predictions in seven xenograft models. Our study demonstrates that MS-based proteomics can identify therapeutic targets and highlights the potential of PDX drug response evaluation to annotate MS-based pathway activities. |
format | Online Article Text |
id | pubmed-5379071 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2017 |
publisher | Nature Publishing Group |
record_format | MEDLINE/PubMed |
spelling | pubmed-53790712017-04-11 Proteogenomic integration reveals therapeutic targets in breast cancer xenografts Huang, Kuan-lin Li, Shunqiang Mertins, Philipp Cao, Song Gunawardena, Harsha P. Ruggles, Kelly V. Mani, D. R. Clauser, Karl R. Tanioka, Maki Usary, Jerry Kavuri, Shyam M. Xie, Ling Yoon, Christopher Qiao, Jana W Wrobel, John Wyczalkowski, Matthew A. Erdmann-Gilmore, Petra Snider, Jacqueline E. Hoog, Jeremy Singh, Purba Niu, Beifang Guo, Zhanfang Sun, Sam Qiancheng Sanati, Souzan Kawaler, Emily Wang, Xuya Scott, Adam Ye, Kai McLellan, Michael D. Wendl, Michael C. Malovannaya, Anna Held, Jason M. Gillette, Michael A. Fenyö, David Kinsinger, Christopher R. Mesri, Mehdi Rodriguez, Henry Davies, Sherri R. Perou, Charles M. Ma, Cynthia Reid Townsend, R. Chen, Xian Carr, Steven A. Ellis, Matthew J. Ding, Li Nat Commun Article Recent advances in mass spectrometry (MS) have enabled extensive analysis of cancer proteomes. Here, we employed quantitative proteomics to profile protein expression across 24 breast cancer patient-derived xenograft (PDX) models. Integrated proteogenomic analysis shows positive correlation between expression measurements from transcriptomic and proteomic analyses; further, gene expression-based intrinsic subtypes are largely re-capitulated using non-stromal protein markers. Proteogenomic analysis also validates a number of predicted genomic targets in multiple receptor tyrosine kinases. However, several protein/phosphoprotein events such as overexpression of AKT proteins and ARAF, BRAF, HSP90AB1 phosphosites are not readily explainable by genomic analysis, suggesting that druggable translational and/or post-translational regulatory events may be uniquely diagnosed by MS. Drug treatment experiments targeting HER2 and components of the PI3K pathway supported proteogenomic response predictions in seven xenograft models. Our study demonstrates that MS-based proteomics can identify therapeutic targets and highlights the potential of PDX drug response evaluation to annotate MS-based pathway activities. Nature Publishing Group 2017-03-28 /pmc/articles/PMC5379071/ /pubmed/28348404 http://dx.doi.org/10.1038/ncomms14864 Text en Copyright © 2017, The Author(s) http://creativecommons.org/licenses/by/4.0/ This work is licensed under a Creative Commons Attribution 4.0 International License. The images or other third party material in this article are included in the article's Creative Commons license, unless indicated otherwise in the credit line; if the material is not included under the Creative Commons license, users will need to obtain permission from the license holder to reproduce the material. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/ |
spellingShingle | Article Huang, Kuan-lin Li, Shunqiang Mertins, Philipp Cao, Song Gunawardena, Harsha P. Ruggles, Kelly V. Mani, D. R. Clauser, Karl R. Tanioka, Maki Usary, Jerry Kavuri, Shyam M. Xie, Ling Yoon, Christopher Qiao, Jana W Wrobel, John Wyczalkowski, Matthew A. Erdmann-Gilmore, Petra Snider, Jacqueline E. Hoog, Jeremy Singh, Purba Niu, Beifang Guo, Zhanfang Sun, Sam Qiancheng Sanati, Souzan Kawaler, Emily Wang, Xuya Scott, Adam Ye, Kai McLellan, Michael D. Wendl, Michael C. Malovannaya, Anna Held, Jason M. Gillette, Michael A. Fenyö, David Kinsinger, Christopher R. Mesri, Mehdi Rodriguez, Henry Davies, Sherri R. Perou, Charles M. Ma, Cynthia Reid Townsend, R. Chen, Xian Carr, Steven A. Ellis, Matthew J. Ding, Li Proteogenomic integration reveals therapeutic targets in breast cancer xenografts |
title | Proteogenomic integration reveals therapeutic targets in breast cancer xenografts |
title_full | Proteogenomic integration reveals therapeutic targets in breast cancer xenografts |
title_fullStr | Proteogenomic integration reveals therapeutic targets in breast cancer xenografts |
title_full_unstemmed | Proteogenomic integration reveals therapeutic targets in breast cancer xenografts |
title_short | Proteogenomic integration reveals therapeutic targets in breast cancer xenografts |
title_sort | proteogenomic integration reveals therapeutic targets in breast cancer xenografts |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5379071/ https://www.ncbi.nlm.nih.gov/pubmed/28348404 http://dx.doi.org/10.1038/ncomms14864 |
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