ArticleBMC systems biology2015
CD8(+) T cell response to adenovirus vaccination and subsequent suppression of tumor growth: modeling, simulation and analysis.
Article in BMC systems biology, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed, 18 citations in OpenAlex.
- OnabotulinumtoxinA alters inflammatory gene expression and immune cells in chronic headache patients.Brain : a journal of neurology · 2022Article
- Interleukin-12 elicits a non-canonical response in B16 melanoma cells to enhance survival.Cell communication and signaling : CCS · 2020Article
- An in silico exploration of combining Interleukin-12 with Oxaliplatin to treat liver-metastatic colorectal cancer.BMC cancer · 2020Article
- Existence and stability of periodic solutions of an impulsive differential equation and application to CD8 T-cell differentiation.Journal of mathematical biology · 2018Article
- A Dynamic Model of Immune Responses to Antigen Presentation Predicts Different Regions of Tumor or Pathogen Elimination.Cell systems · 2017Article
- Simulation Study on Effects of Order and Step Size of Runge-Kutta Methods that Solve Contagious Disease and Tumor Models.Journal of computer science and systems biology · 2016Article
- IL-2 sensitivity and exogenous IL-2 concentration gradient tune the productive contact duration of CD8(+) T cell-APC: a multiscale modeling study.BMC systems biology · 2016Article
- Inferring the Impact of Regulatory Mechanisms that Underpin CD8+ T Cell Control of B16 Tumor GrowthFrontiers in pharmacology · 2016Article
- Numerical Simulation of a Tumor Growth Dynamics Model Using Particle Swarm Optimization.Journal of computer science and systems biologyArticle
Corrections and comments
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Authors and funding
3 authors at 2 institutions in 1 country.
Funding
Abstract
backgroundUsing 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.
methodsTo 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.
resultsThe 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.
conclusionsUsing 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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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.