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CD8 + T cell response to adenovirus vaccination and subsequent suppression of tumor growth: modeling, simulation and analysis

Overview of attention for article published in BMC Systems Biology, June 2015
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Title
CD8 + T cell response to adenovirus vaccination and subsequent suppression of tumor growth: modeling, simulation and analysis
Published in
BMC Systems Biology, June 2015
DOI 10.1186/s12918-015-0168-9
Pubmed ID
Authors

Qing Wang, David J Klinke, Zhijun Wang

Abstract

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. 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. 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. 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.

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 39 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 39 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 6 15%
Researcher 6 15%
Student > Master 5 13%
Student > Doctoral Student 4 10%
Other 4 10%
Other 12 31%
Unknown 2 5%
Readers by discipline Count As %
Agricultural and Biological Sciences 7 18%
Medicine and Dentistry 6 15%
Engineering 4 10%
Pharmacology, Toxicology and Pharmaceutical Science 3 8%
Biochemistry, Genetics and Molecular Biology 3 8%
Other 12 31%
Unknown 4 10%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 1. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 18 May 2016.
All research outputs
#21,296,229
of 23,923,788 outputs
Outputs from BMC Systems Biology
#1,007
of 1,134 outputs
Outputs of similar age
#228,945
of 269,426 outputs
Outputs of similar age from BMC Systems Biology
#24
of 26 outputs
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So far Altmetric has tracked 1,134 research outputs from this source. They receive a mean Attention Score of 3.6. This one is in the 1st percentile – i.e., 1% of its peers scored the same or lower than it.
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We're also able to compare this research output to 26 others from the same source and published within six weeks on either side of this one. This one is in the 1st percentile – i.e., 1% of its contemporaries scored the same or lower than it.