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An in silico exploration of combining Interleukin-12 with Oxaliplatin to treat liver-metastatic colorectal cancer
BACKGROUND: Combining anti-cancer therapies with orthogonal modes of action, such as direct cytotoxicity and immunostimulatory, hold promise for expanding clinical benefit to patients with metastatic disease. For instance, a chemotherapy agent Oxaliplatin (OXP) in combination with Interleukin-12 (IL...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6950805/ https://www.ncbi.nlm.nih.gov/pubmed/31914948 http://dx.doi.org/10.1186/s12885-019-6500-9 |
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author | Wang, Qing Wang, Zhijun Wu, Yan Klinke, David J. |
author_facet | Wang, Qing Wang, Zhijun Wu, Yan Klinke, David J. |
author_sort | Wang, Qing |
collection | PubMed |
description | BACKGROUND: Combining anti-cancer therapies with orthogonal modes of action, such as direct cytotoxicity and immunostimulatory, hold promise for expanding clinical benefit to patients with metastatic disease. For instance, a chemotherapy agent Oxaliplatin (OXP) in combination with Interleukin-12 (IL-12) can eliminate pre-existing liver metastatic colorectal cancer and protect from relapse in a murine model. However, the underlying dynamics associated with the targeted biology and the combinatorial space consisting of possible dosage and timing of each therapy present challenges for optimizing treatment regimens. To address some of these challenges, we developed a predictive simulation platform for optimizing dose and timing of the combination therapy involving Mifepristone-induced IL-12 and chemotherapy agent OXP. METHODS: A multi-scale mathematical model comprised of impulsive ordinary differential equations was developed to describe the interaction between the immune system and tumor cells in response to the combined IL-12 and OXP therapy. An ensemble of model parameters were calibrated to published experimental data using a genetic algorithm and used to represent three different phenotypes: responders, partial-responders, and non-responders. RESULTS: The multi-scale model captures tumor growth patterns of the three phenotypic responses observed in mice in response to the combination therapy against a tumor re-challenge and was used to explore the impacts of changing the dose and timing of the mixed immune-chemotherapy on tumor growth subjected to a tumor re-challenge in mice. An increased ratio of CD8 + T effectors to regulatory T cells during and after treatment was key to improve tumor control in the responder cohort. Sensitivity analysis indicates that combined OXP and IL-12 therapy worked more efficiently in responders by increased priming of T cells, enhanced CD8 + T cell-mediated killing, and functional inhibition of regulatory T cells. In a virtual cohort that mimics non-responders and partial-responders, simulations show that an increased dose of OXP alone would improve the response. In addition, enhanced IL-12 expression alone or an increased number of treatment cycles of the mixed immune-chemotherapy can barely improve tumor control for non-responders and partial responders. CONCLUSIONS: Overall, this study illustrates how mechanistic models can be used for in silico screening of the optimal therapeutic dose and timing in combined cancer treatment strategies. |
format | Online Article Text |
id | pubmed-6950805 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-69508052020-01-09 An in silico exploration of combining Interleukin-12 with Oxaliplatin to treat liver-metastatic colorectal cancer Wang, Qing Wang, Zhijun Wu, Yan Klinke, David J. BMC Cancer Research Article BACKGROUND: Combining anti-cancer therapies with orthogonal modes of action, such as direct cytotoxicity and immunostimulatory, hold promise for expanding clinical benefit to patients with metastatic disease. For instance, a chemotherapy agent Oxaliplatin (OXP) in combination with Interleukin-12 (IL-12) can eliminate pre-existing liver metastatic colorectal cancer and protect from relapse in a murine model. However, the underlying dynamics associated with the targeted biology and the combinatorial space consisting of possible dosage and timing of each therapy present challenges for optimizing treatment regimens. To address some of these challenges, we developed a predictive simulation platform for optimizing dose and timing of the combination therapy involving Mifepristone-induced IL-12 and chemotherapy agent OXP. METHODS: A multi-scale mathematical model comprised of impulsive ordinary differential equations was developed to describe the interaction between the immune system and tumor cells in response to the combined IL-12 and OXP therapy. An ensemble of model parameters were calibrated to published experimental data using a genetic algorithm and used to represent three different phenotypes: responders, partial-responders, and non-responders. RESULTS: The multi-scale model captures tumor growth patterns of the three phenotypic responses observed in mice in response to the combination therapy against a tumor re-challenge and was used to explore the impacts of changing the dose and timing of the mixed immune-chemotherapy on tumor growth subjected to a tumor re-challenge in mice. An increased ratio of CD8 + T effectors to regulatory T cells during and after treatment was key to improve tumor control in the responder cohort. Sensitivity analysis indicates that combined OXP and IL-12 therapy worked more efficiently in responders by increased priming of T cells, enhanced CD8 + T cell-mediated killing, and functional inhibition of regulatory T cells. In a virtual cohort that mimics non-responders and partial-responders, simulations show that an increased dose of OXP alone would improve the response. In addition, enhanced IL-12 expression alone or an increased number of treatment cycles of the mixed immune-chemotherapy can barely improve tumor control for non-responders and partial responders. CONCLUSIONS: Overall, this study illustrates how mechanistic models can be used for in silico screening of the optimal therapeutic dose and timing in combined cancer treatment strategies. BioMed Central 2020-01-08 /pmc/articles/PMC6950805/ /pubmed/31914948 http://dx.doi.org/10.1186/s12885-019-6500-9 Text en © The Author(s) 2019 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, Qing Wang, Zhijun Wu, Yan Klinke, David J. An in silico exploration of combining Interleukin-12 with Oxaliplatin to treat liver-metastatic colorectal cancer |
title | An in silico exploration of combining Interleukin-12 with Oxaliplatin to treat liver-metastatic colorectal cancer |
title_full | An in silico exploration of combining Interleukin-12 with Oxaliplatin to treat liver-metastatic colorectal cancer |
title_fullStr | An in silico exploration of combining Interleukin-12 with Oxaliplatin to treat liver-metastatic colorectal cancer |
title_full_unstemmed | An in silico exploration of combining Interleukin-12 with Oxaliplatin to treat liver-metastatic colorectal cancer |
title_short | An in silico exploration of combining Interleukin-12 with Oxaliplatin to treat liver-metastatic colorectal cancer |
title_sort | in silico exploration of combining interleukin-12 with oxaliplatin to treat liver-metastatic colorectal cancer |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6950805/ https://www.ncbi.nlm.nih.gov/pubmed/31914948 http://dx.doi.org/10.1186/s12885-019-6500-9 |
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