From concept to clinic: Mathematically informed immunotherapy

Walker, Rachel; Enderling, Heiko

doi:10.1016/j.currproblcancer.2015.10.004

Cited by 42 publications

(24 citation statements)

References 107 publications

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“…These results are promising, but further studies need to be done to demonstrate effectiveness in humans. Mathematical modelling has been demonstrated useful to predict patientspecific responses to treatment (such as anti-angiogenic therapy, chemotherapy, radiotherapy and immunotherapy) and suggest novel therapeutic avenues against cancer [176,[193][194][195][196][197][198][199][200][201][202][203][204][205][206][207][208][209]. In the future, appropriate mathematical models should be considered to optimize treatment plans and help to identify causes underlying glioma treatment resistance.…”

Section: Resultsmentioning

confidence: 99%

The biology and mathematical modelling of glioma invasion: a review

Alfonso

Talkenberger

Seifert

et al. 2017

J. R. Soc. Interface.

187

163

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Adult gliomas are aggressive brain tumours associated with low patient survival rates and limited life expectancy. The most important hallmark of this type of tumour is its invasive behaviour, characterized by a markedly phenotypic plasticity, infiltrative tumour morphologies and the ability of malignant progression from low-to high-grade tumour types. Indeed, the widespread infiltration of healthy brain tissue by glioma cells is largely responsible for poor prognosis and the difficulty of finding curative therapies. Meanwhile, mathematical models have been established to analyse potential mechanisms of glioma invasion. In this review, we start with a brief introduction to current biological knowledge about glioma invasion, and then critically review and highlight future challenges for mathematical models of glioma invasion.

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Section: Resultsmentioning

confidence: 99%

The biology and mathematical modelling of glioma invasion: a review

Alfonso

Talkenberger

Seifert

et al. 2017

J. R. Soc. Interface.

187

163

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“…In conclusion, we have for the first time applied mathematical modeling as a tool to depict the relative strength of a host's immune response after it has been subjected to a lung tumor vaccine. Here, we showed and can infer that if a synthetic epitope is not between 8-11 aa long, which can be substantiated by the TANTIGEN database, a host will produce an immune response, but that is not ideal to the elimination of cancer [26,27]. Studies are ongoing to elucidate the above perspectives by mathematical modeling through different means.…”

Section: Resultsmentioning

confidence: 99%

A Mathematical Investigation on Tumor-Immune Dynamics: The Impact of Vaccines on the Immune Response

Quinonez¹,

Dasu²,

Qureshi³

2017

J Cancer Sci Ther

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Mathematical models analyzing tumor-immune interactions provide a framework by which to address specific scenarios in regard to tumor-immune dynamics. Important aspects of tumor-immune surveillance to consider is the elimination of tumor cells from a host's cell-mediated immunity as well as the implications of vaccines derived from synthetic antigen. In present studies, our mathematical model examined the role of synthetic antigen to the strength of the immune system. The constructed model takes into account accepted knowledge of immune function as well as prior work done by de Pillis et al. All equations describing tumor-immune growth, antigen presentation, immune response, and interaction rates were numerically simulated with MATLAB. Here, our work shows that a robust immune response can be generated if the immune system recognizes epitopes that are between 8 to 11 amino acids long. We show through mathematical modeling of how synthetic tumor vaccines can be utilized to mitigate a developing cancer.utilized mice that were inoculated with chemically-induced cancer cells; such cells lacked the capability to metastasize within a host. Over time, this led to the development of cancer-specific immunity in the recipient mice. This discovery provided the evidence needed for Ehrlich's hypothesis. Such experiments demonstrated that it is essential to have the presence of an antigen to elicit an immune response in the host, because if no distinctive structures exist, then no recognition would be established [9]. F. Macfarlane Burnet and Lewis Thomas, well-known immunologists during the 20 th century, hypothesized that for immunosurveillance to exist, lymphocytes would need to act aggressively akin to sentinels to recognize and eliminate a cancer threat. The cancer immunosurveillance theory revolves around three transitions states, denoted as "E's" [9]:• Elimination-The establishment of a strong cancer surveillance network by both the innate and adaptive immune system that seeks to eliminate cancer populations.• Equilibrium-The long-term process of combat between a cancer population and a host's immunosurveillance network.• Escape-The overpowering of cancer surveillance network by strong tumor variants, which results in host death.

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“…These tumors were the first where immunotherapy was shown to be successful. Immunotherapy, informed by mathematical modeling, may have a greater chance of leading to durable remissions [52]. …”

Section: Compelling Research Questions In (Cancer) Stem Cell Researchmentioning

confidence: 99%

Modeling of Interactions between Cancer Stem Cells and their Microenvironment: Predicting Clinical Response

Sehl

Wicha

2018

Methods in Molecular Biology

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Mathematical models of cancer stem cells are useful in translational cancer research for facilitating the understanding of tumor growth dynamics and for predicting treatment response and resistance to combined targeted therapies. In this chapter, we describe appealing aspects of different methods used in mathematical oncology and discuss compelling questions in oncology that can be addressed with these modeling techniques. We describe a simplified version of a model of the breast cancer stem cell niche, illustrate the visualization of the model, and apply stochastic simulation to generate full distributions and average trajectories of cell type populations over time. We further discuss the advent of single-cell data in studying cancer stem cell heterogeneity and how these data can be integrated with modeling to advance understanding of the dynamics of invasive and proliferative populations during cancer progression and response to therapy.

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From concept to clinic: Mathematically informed immunotherapy

Cited by 42 publications

References 107 publications

The biology and mathematical modelling of glioma invasion: a review

The biology and mathematical modelling of glioma invasion: a review

A Mathematical Investigation on Tumor-Immune Dynamics: The Impact of Vaccines on the Immune Response

Modeling of Interactions between Cancer Stem Cells and their Microenvironment: Predicting Clinical Response

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