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CD8 (+) T cell response to adenovirusvaccination and subsequent suppression of tumor growth: modeling, simulation and analysis
BACKGROUND: Using immune checkpoint modulators in the clinic to increase the number and activity of cytotoxic T lymphocytes that recognize tumor antigens can prolong survival for metastatic melanoma. Yet, only a fraction of the patient population receives clinical benefit. In short, these clinical t...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2015
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4458046/ https://www.ncbi.nlm.nih.gov/pubmed/26048402 http://dx.doi.org/10.1186/s12918-015-0168-9 |
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author | Wang, Qing J Klinke, David Wang, Zhijun |
author_facet | Wang, Qing J Klinke, David Wang, Zhijun |
author_sort | Wang, Qing |
collection | PubMed |
description | BACKGROUND: Using immune checkpoint modulators in the clinic to increase the number and activity of cytotoxic T lymphocytes that recognize tumor antigens can prolong survival for metastatic melanoma. Yet, only a fraction of the patient population receives clinical benefit. In short, these clinical trials demonstrate proof-of-principle but optimizing the specific therapeutic strategies remains a challenge. In many fields, CAD (computer-aided design) is a tool used to optimize integrated system behavior using a mechanistic model that is based upon knowledge of constitutive elements. The objective of this study was to develop a predictive simulation platform for optimizing anti-tumor immunity using different treatment strategies. METHODS: To better understand the therapeutic role that cytotoxic CD8 (+) T cells can play in controlling tumor growth, we developed a multi-scale mechanistic model of the biology using impulsive differential equations and calibrated it to a self-consistent data set. RESULTS: The multi-scale model captures the activation and differentiation of naïve CD8 (+) T cells into effector cytotoxic T cells in the lymph node following adenovirus-mediated vaccination against a tumor antigen, the trafficking of the resulting cytotoxic T cells into blood and tumor microenvironment, the production of cytokines within the tumor microenvironment, and the interactions between tumor cells, T cells and cytokines that control tumor growth. The calibrated model captures the modest suppression of tumor cell growth observed in the B16F10 model, a transplantable mouse model for metastatic melanoma, and was used to explore the impact of multiple vaccinations on controlling tumor growth. CONCLUSIONS: Using the calibrated mechanistic model, we found that the cytotoxic CD8 (+) T cell response was prolonged by multiple adenovirus vaccinations. However, the strength of the immune response cannot be improved enough by multiple adenovirus vaccinations to reduce tumor burden if the cytotoxic activity or local proliferation of cytotoxic T cells in response to tumor antigens is not greatly enhanced. Overall, this study illustrates how mechanistic models can be used for in silico screening of the optimal therapeutic dosage and timing in cancer treatment. |
format | Online Article Text |
id | pubmed-4458046 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2015 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-44580462015-06-07 CD8 (+) T cell response to adenovirusvaccination and subsequent suppression of tumor growth: modeling, simulation and analysis Wang, Qing J Klinke, David Wang, Zhijun BMC Syst Biol Research Article BACKGROUND: Using immune checkpoint modulators in the clinic to increase the number and activity of cytotoxic T lymphocytes that recognize tumor antigens can prolong survival for metastatic melanoma. Yet, only a fraction of the patient population receives clinical benefit. In short, these clinical trials demonstrate proof-of-principle but optimizing the specific therapeutic strategies remains a challenge. In many fields, CAD (computer-aided design) is a tool used to optimize integrated system behavior using a mechanistic model that is based upon knowledge of constitutive elements. The objective of this study was to develop a predictive simulation platform for optimizing anti-tumor immunity using different treatment strategies. METHODS: To better understand the therapeutic role that cytotoxic CD8 (+) T cells can play in controlling tumor growth, we developed a multi-scale mechanistic model of the biology using impulsive differential equations and calibrated it to a self-consistent data set. RESULTS: The multi-scale model captures the activation and differentiation of naïve CD8 (+) T cells into effector cytotoxic T cells in the lymph node following adenovirus-mediated vaccination against a tumor antigen, the trafficking of the resulting cytotoxic T cells into blood and tumor microenvironment, the production of cytokines within the tumor microenvironment, and the interactions between tumor cells, T cells and cytokines that control tumor growth. The calibrated model captures the modest suppression of tumor cell growth observed in the B16F10 model, a transplantable mouse model for metastatic melanoma, and was used to explore the impact of multiple vaccinations on controlling tumor growth. CONCLUSIONS: Using the calibrated mechanistic model, we found that the cytotoxic CD8 (+) T cell response was prolonged by multiple adenovirus vaccinations. However, the strength of the immune response cannot be improved enough by multiple adenovirus vaccinations to reduce tumor burden if the cytotoxic activity or local proliferation of cytotoxic T cells in response to tumor antigens is not greatly enhanced. Overall, this study illustrates how mechanistic models can be used for in silico screening of the optimal therapeutic dosage and timing in cancer treatment. BioMed Central 2015-06-06 /pmc/articles/PMC4458046/ /pubmed/26048402 http://dx.doi.org/10.1186/s12918-015-0168-9 Text en © Wang et al.; licensee BioMed Central. 2015 This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0), 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 Wang, Qing J Klinke, David Wang, Zhijun CD8 (+) T cell response to adenovirusvaccination and subsequent suppression of tumor growth: modeling, simulation and analysis |
title | CD8 (+) T cell response to adenovirusvaccination and subsequent suppression of tumor growth: modeling, simulation and analysis |
title_full | CD8 (+) T cell response to adenovirusvaccination and subsequent suppression of tumor growth: modeling, simulation and analysis |
title_fullStr | CD8 (+) T cell response to adenovirusvaccination and subsequent suppression of tumor growth: modeling, simulation and analysis |
title_full_unstemmed | CD8 (+) T cell response to adenovirusvaccination and subsequent suppression of tumor growth: modeling, simulation and analysis |
title_short | CD8 (+) T cell response to adenovirusvaccination and subsequent suppression of tumor growth: modeling, simulation and analysis |
title_sort | cd8 (+) t cell response to adenovirusvaccination and subsequent suppression of tumor growth: modeling, simulation and analysis |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4458046/ https://www.ncbi.nlm.nih.gov/pubmed/26048402 http://dx.doi.org/10.1186/s12918-015-0168-9 |
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