Frontiers in Mathematical Oncology

Wednesday, June 16 at 09:30am (PDT)
Wednesday, June 16 at 05:30pm (BST)
Thursday, June 17 01:30am (KST)

SMB2021 SMB2021 Follow Wednesday (Thursday) during the "MS13" time block.
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Kasia Rejniak & Heiko Enderling (Moffitt Cancer Center, USA)


Mathematical models offer an attractive approach to decode the outcome of various pre-clinical experiments and clinical trials as well as design the next set of crucial experiments to perform. They can help evaluate untested pre-clinical perturbation experiments and clinical protocols in silico to identify new treatment targets, and to help reduce the risk of adverse clinical outcomes due to complex nonlinear feedback mechanisms. Thus, mathematical models developed, calibrated and validated in close collaboration with experimental cancer biologists and clinicians can help predict a patient’s response to different treatments – both in terms of combinatorial/sequential therapies and their dosage and timings on a per-patient basis, which is the promise of “precision medicine”. This minisymposium will bring together experts in mathematical oncology to showcase recent advances in the field.

Thomas E. Yankeelov

(The University of Texas at Austin, USA)
"Imaging-based mathematical modeling of brain cancer across scales"
Our lab is focused on developing tumor forecasting methods by integrating advanced imaging technologies with predictive, mathematical models to forecast tumor growth and treatment response. In this presentation, we will provide an overview of three vignettes in mathematical oncology that span the in vitro (cells), in vivo pre-clinical (rats), and in vivo clinical (human) scales in brain cancer. Each project employs quantitative imaging to calibrate an appropriate mathematical model to predict how the tumors grow, how they respond to therapy, or how the therapy is delivered. The first vignette employs time resolved microscopy data to calibrate a system of ordinary differential equations to predict the response of glioma cells to single- and multi-fraction radiation therapy in vitro. We then move to in vivo, pre-clinical studies where we make use of quantitative magnetic resonance imaging (MRI) data reporting on cellularity and perfusion to calibrate a system of reaction diffusion models to predict the response of glioma cells to single- and multi-fraction radiation therapy in a murine model of brain cancer. The final vignette is focused on employing MRI, x-ray computed tomography (CT), and single photon emission computed tomography (SPECT) to calibrate a reaction-diffusion-advection equation to predict and optimize the spatio-temporal distribution of radiolabeled liposomes for the treatment of recurrent glioblastoma multiforme in patients. The long-term goal of these studies is to provide a rigorous, but practical, experimental-computational approach describing tumor development, informed and validated by readily available imaging data.

Arne Traulsen

(Max Planck Institute for Evolutionary Biology, Germany)
"Measuring cancer heterogeneity and possibilities of exploiting it in treatment"
Evolving populations naturally diversify. For populations of cancer cells, this has been extensively explored on the genotypic level and recognized as a potential problem in treatment. Phenotypic diversity, on the other hand, is typically harder to measure, but it may also be directly relevant for treatment, especially when different treatment options are available. Theoretical models show that cancer progression could be delayed substantially if the current phenotypic state can be taken into account in the choice of therapy.

Angélique Stéphanou

(University of Grenoble, France)
"Cell metabolism and intracellular acidity regulation in cancer cells, from experimental characterization to computational models with therapeutic perspectives"
The metabolism of cancer cells is characterized by increased glycolysis due to local hypoxic conditions. Glycolysis in turn induces an increase in acidity which is detrimental to cells. Cancer cells, however, exhibit a higher resistance to acidity than normal cells due to a better ability to regulate their intracellular pH. We have experimentally characterized the regulatory capacity of two glioma cell lines using fluorescence microscopy. We observed that the regulation of acidity is not the same for the two cell lines. This has consequences for cellular aggressiveness, metastatic potential and treatment planning since the main drug used against glioblastoma is highly pH dependent. Theoretically, we revised a model of cellular metabolism to specifically take into account the influence of pH on cellular metabolic adaptation. The model suggests that the Warburg effect, often described as a hallmark of cancer, can actually be viewed as a transient adaptation mechanism to a disturbed environment rather than an inherent characteristic of the cancer cell. As such, targeting the acidic environment rather than targeting the cancer cell could offer a good alternative therapeutic strategy.

Elizabeth Wayne

(Carnegie Mellon University, USA)
"Developing experimental and mathematical models to measure changes in tumor associate macrophage polarization in response to immunotherapy"
Tumor associated macrophages (TAMs) are a significant player in cancer microenvironment. They can comprise 50%-80% of a solid tumor mass and M2, anti-inflammatory polarized TAMs are correlated with poorer clinical outcomes. Numerous therapeutic strategies attempt to modulate TAM polarization to decrease tumor growth. However, macrophage polarization is dependent on a number of intrinsic and extrinsic factors. Understanding the factors government TAM polarization can help us understand therapeutic response heterogeneity. Here the talk will discuss experimental models for deciphering the interplay of TAM polarization, drug accumulation, and tumor growth. Moreover, this talk will discuss ideas for developing models that work in tandem with experimental data. Being able to experimentally and mathematically model the effect of immunomodulatory drugs on TAM polarization could enhance decision making in personalized cancer treatment.

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Virtual conference of the Society for Mathematical Biology, 2021.