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A robust blood gene expression-based prognostic model for castration-resistant prostate cancer

BACKGROUND: Castration-resistant prostate cancer (CRPC) is associated with wide variations in survival. Recent studies of whole blood mRNA expression-based biomarkers strongly predicted survival but the genes used in these biomarker models were non-overlapping and their relationship was unknown. We...

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Autores principales: Wang, Li, Gong, Yixuan, Chippada-Venkata, Uma, Heck, Matthias Michael, Retz, Margitta, Nawroth, Roman, Galsky, Matthew, Tsao, Che-Kai, Schadt, Eric, de Bono, Johann, Olmos, David, Zhu, Jun, Oh, William K.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2015
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4546313/
https://www.ncbi.nlm.nih.gov/pubmed/26297150
http://dx.doi.org/10.1186/s12916-015-0442-0
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author Wang, Li
Gong, Yixuan
Chippada-Venkata, Uma
Heck, Matthias Michael
Retz, Margitta
Nawroth, Roman
Galsky, Matthew
Tsao, Che-Kai
Schadt, Eric
de Bono, Johann
Olmos, David
Zhu, Jun
Oh, William K.
author_facet Wang, Li
Gong, Yixuan
Chippada-Venkata, Uma
Heck, Matthias Michael
Retz, Margitta
Nawroth, Roman
Galsky, Matthew
Tsao, Che-Kai
Schadt, Eric
de Bono, Johann
Olmos, David
Zhu, Jun
Oh, William K.
author_sort Wang, Li
collection PubMed
description BACKGROUND: Castration-resistant prostate cancer (CRPC) is associated with wide variations in survival. Recent studies of whole blood mRNA expression-based biomarkers strongly predicted survival but the genes used in these biomarker models were non-overlapping and their relationship was unknown. We developed a biomarker model for CRPC that is robust, but also captures underlying biological processes that drive prostate cancer lethality. METHODS: Using three independent cohorts of CRPC patients, we developed an integrative genomic approach for understanding the biological processes underlying genes associated with cancer progression, constructed a novel four-gene model that captured these changes, and compared the performance of the new model with existing gene models and other clinical parameters. RESULTS: Our analysis revealed striking patterns of myeloid- and lymphoid-specific distribution of genes that were differentially expressed in whole blood mRNA profiles: up-regulated genes in patients with worse survival were overexpressed in myeloid cells, whereas down-regulated genes were noted in lymphocytes. A resulting novel four-gene model showed significant prognostic power independent of known clinical predictors in two independent datasets totaling 90 patients with CRPC, and was superior to the two existing gene models. CONCLUSIONS: Whole blood mRNA profiling provides clinically relevant information in patients with CRPC. Integrative genomic analysis revealed patterns of differential mRNA expression with changes in gene expression in immune cell components which robustly predicted the survival of CRPC patients. The next step would be validation in a cohort of suitable size to quantify the prognostic improvement by the gene score upon the standard set of clinical parameters. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s12916-015-0442-0) contains supplementary material, which is available to authorized users.
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spelling pubmed-45463132015-08-23 A robust blood gene expression-based prognostic model for castration-resistant prostate cancer Wang, Li Gong, Yixuan Chippada-Venkata, Uma Heck, Matthias Michael Retz, Margitta Nawroth, Roman Galsky, Matthew Tsao, Che-Kai Schadt, Eric de Bono, Johann Olmos, David Zhu, Jun Oh, William K. BMC Med Research Article BACKGROUND: Castration-resistant prostate cancer (CRPC) is associated with wide variations in survival. Recent studies of whole blood mRNA expression-based biomarkers strongly predicted survival but the genes used in these biomarker models were non-overlapping and their relationship was unknown. We developed a biomarker model for CRPC that is robust, but also captures underlying biological processes that drive prostate cancer lethality. METHODS: Using three independent cohorts of CRPC patients, we developed an integrative genomic approach for understanding the biological processes underlying genes associated with cancer progression, constructed a novel four-gene model that captured these changes, and compared the performance of the new model with existing gene models and other clinical parameters. RESULTS: Our analysis revealed striking patterns of myeloid- and lymphoid-specific distribution of genes that were differentially expressed in whole blood mRNA profiles: up-regulated genes in patients with worse survival were overexpressed in myeloid cells, whereas down-regulated genes were noted in lymphocytes. A resulting novel four-gene model showed significant prognostic power independent of known clinical predictors in two independent datasets totaling 90 patients with CRPC, and was superior to the two existing gene models. CONCLUSIONS: Whole blood mRNA profiling provides clinically relevant information in patients with CRPC. Integrative genomic analysis revealed patterns of differential mRNA expression with changes in gene expression in immune cell components which robustly predicted the survival of CRPC patients. The next step would be validation in a cohort of suitable size to quantify the prognostic improvement by the gene score upon the standard set of clinical parameters. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s12916-015-0442-0) contains supplementary material, which is available to authorized users. BioMed Central 2015-08-21 /pmc/articles/PMC4546313/ /pubmed/26297150 http://dx.doi.org/10.1186/s12916-015-0442-0 Text en © Wang et al. 2015 Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. 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
Wang, Li
Gong, Yixuan
Chippada-Venkata, Uma
Heck, Matthias Michael
Retz, Margitta
Nawroth, Roman
Galsky, Matthew
Tsao, Che-Kai
Schadt, Eric
de Bono, Johann
Olmos, David
Zhu, Jun
Oh, William K.
A robust blood gene expression-based prognostic model for castration-resistant prostate cancer
title A robust blood gene expression-based prognostic model for castration-resistant prostate cancer
title_full A robust blood gene expression-based prognostic model for castration-resistant prostate cancer
title_fullStr A robust blood gene expression-based prognostic model for castration-resistant prostate cancer
title_full_unstemmed A robust blood gene expression-based prognostic model for castration-resistant prostate cancer
title_short A robust blood gene expression-based prognostic model for castration-resistant prostate cancer
title_sort robust blood gene expression-based prognostic model for castration-resistant prostate cancer
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4546313/
https://www.ncbi.nlm.nih.gov/pubmed/26297150
http://dx.doi.org/10.1186/s12916-015-0442-0
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