MS02 - ONCO-2
Senate Chamber (#2145) in The Ohio Union

Evolutionary game theory in cancer

Monday, July 17 at 04:00pm

SMB2023 Follow Monday during the "MS02" time block.
Room assignment: Senate Chamber (#2145) in The Ohio Union.

Note: this minisymposia has multiple sessions. The other session is MS01-ONCO-2 (click here).

Anuraag Bukkuri, Katarina Stankova

Description:

Katerina Stankova and Anuraag Bukkuri are proposing a minisymposium on “Evolutionary game theory in cancer”. We have invited six international early career researchers (those that have not yet completed a PhD or similar) that identify as members of underrepresented groups who are working at the interface between game theory and cancer. Complementing our diverse line-up of speakers, our talks will span a wide range of topics in mathematical oncology: immune evasion and transmissibility, angiogenesis, evolutionarily informed therapies, metastasis, cell competition, and growth models. We hope that this symposium will allow us to shine light on the wonderful work being done by diverse early stage researchers in our community, allow these members to forge connections and gain visibility, and showcase the latest results in the applications of evolutionary game theory to the broader mathematical oncology community.

Kanyarat Jitmana

The University of Utah (Department of Mathematics)

"Mathematical modeling of the evolution of resistance and aggressiveness of ovarian cancer from HGSOC patient CA-125 time series."

We use time series of CA-125 levels from high-grade serous ovarian cancer patients from the Australian Ovarian Cancer Study to develop mathematical models of the evolution of resistance and response to therapy. We hypothesize that two key traits determine long-term patient outcomes: resistance as measured by the rate of decline of CA-125 during therapy, and aggressiveness as measured by the rate of CA-125 increase between lines. Statistical analysis shows the level of resistance increases as the number of lines increases. Low initial CA-125, residual disease less than or equal to 1 cm, and a high rate of decline during the first line of therapy predict a longer median of survival of patients after finishing the second line of therapy. We use mathematical models to investigate the mechanisms underlying the differences among HGSOC patients. Our simplest model has two cell types, sensitive and resistant, with resistant cells that could be present before therapy or be generated through mutation from sensitive cells. By fitting the models to HGSOC data using all data points, the first two lines, the first three lines, and the first four lines, these models can successfully capture the dynamic of the CA-125 level of HGSOC patients. Contradictory, the model cannot predict the great detail of the dynamic of CA-125 in the later lines, both the short and long run. By fitting these mathematical models to the clinical data for each patient, we estimate the parameters, which are the growth and death rate of sensitive cells and resistant cells. Despite the inability of the models to predict future CA-125, the patients with the low growth rate of sensitive cells, the low growth rate of the resistant cell, and the high death rate of the resistant cell show better survival chances after finishing the second line of therapy
Additional authors: Sian Fereday Peter MacCallum Cancer Centr, Department of Oncology, The University of Melbourne: Anna DeFazio Centre for Cancer Research, The Westmead Institute for Medical Research, Department of Gynaecological Oncology, Westmead Hospital: David Bowtell Cancer Centre, Department of Oncology, The University of Melbourne: Frederick R. Adler, the University of Utah

Ranjini Bhattacharya

Moffitt Cancer Center (Integrated Mathematical Oncology)

"Angiogenesis: A Tragedy of Commons"

Cancer progression is the result of evolution within the tumor microenvironment. Natural selection selects for cells capable of efficient nutrient uptake. Cancer cells achieve this by overexpressing angiogenic factors (VEGF) that induce the formation of blood vessels that carry nutrients to the tumor. Traditionally, angiogenesis has been viewed as a cooperative phenomenon resulting in the evolution of free-loaders. Using a game theoretic framework, we model VEGF production as an evolutionary strategy and show that the over-production of VEGF is the result of a tragedy of commons. A cell’s investment in VEGF depends on the degree to which it aids its nutrient uptake. If higher production of VEGF leads to higher nutrient uptake, then cells are incentivized to produce VEGF. If nutrients are equally divided within a given neighborhood, an individual cell’s incentive to produce VEGF decreases. Our simulations predict that cancer cells produce 100 times more VEGF than what is typically seen in normal cells, and what would be their collective team optimum. This means that VEGF production by a cancer cell aims to co-opt nutrients from neighboring cells resulting in an evolutionary arms race. Increasing the number of cancer cells in a fixed neighborhood results in lower per-cell VEGF production while exacerbating the tragedy of the commons collectively. We simulate anti-angiogenic therapy and find that while therapy reduces the amount of VEGF in the neighborhood, cells adopt a low VEGF production strategy that can still sustain tumor proliferation. This results in evolutionary rescue. Our model challenges the existing paradigm of angiogenesis as a cooperative activity and provides novel insights into therapy in a clinical setting.
Additional authors: Anuraag Bukkuri; Joel Brown

Monica Salvioli - Part 2

Delft University of Technology (Delft Institute of Applied Mathematics, Delft University of Technology, Delft, The Netherlands)

"Using the Stackelberg evolutionary game approach in cancer treatment"

We present a game-theoretic cancer model based on Darwinian dynamics with two cancer cell types and treatment-induced resistance as an evolving trait. We first investigate whether a constant treatment dose can keep cancer at a viable tumor burden. The game is then expanded into a Stackelberg evolutionary game with the physician as its leader, who chooses drug dosage to maximize the patient's quality of life. The quality of life is modeled by an objective function that takes into account the following three aspects of tumor burden: 1) the population of cancer cells at the ecological equilibrium point, 2) the toxicity of the drug, and 3) treatment-induced resistance. In this study, the game's Stackelberg and Nash outcomes are compared to the maximum tolerable dose. We demonstrate that for large ranges of parameters, the Nash and Stackelberg treatment strategies can stabilize the tumor burden at viable levels even when the maximum tolerable dose cannot. As expected, the Stackelberg solution allows us to aim for a higher quality of life than the Nash solution. In general, we demonstrate that determining a patient's treatment dose by employing the Stackelberg evolutionary game approach results in an improvement in the patient's quality of life.
Additional authors: Monica Salvioli, Faculty of Technology, Policy and Management, Delft University of Technology, Delft, The Netherlands; Johan Dubbeldam, Delft Institute of Applied Mathematics, Delft University of Technology, Delft, The Netherlands; Katerina Stankova, Faculty of Technology, Policy and Management, Delft University of Technology, Delft, The Netherlands; Joel S. Brown, Integrated Mathematical Oncology, H. Lee Moffitt Cancer and Research Institute, Tampa, FL, United States of America.

Shalu Dwivedi

Matthias Schleiden Institute, Friedrich Schiller University, Jena (Department of Bioinformatics)

"Go or grow: Game-theoretical description of metastasis in tumour development"

A medically important feature of several types of cancer is their ability to “decide” between staying at a primary site in the body or to leave it and form metastases. The present theoretical study is aimed at a better understanding of the proximate reasons for this so-called “go-or-grow” dichotomy. To that end, we use game theory, which has turned out to be useful in analyzing the competition between tumours and healthy tissue or among different tumour cells [1]. We start from a game-theoretical model presented by Basanta [2]. We determine the type of game, depending on parameter values, both for the basic model and for five modified variants that we suggest here. For example, in the basic model, the deadlock game, Prisoner’s Dilemma, and hawk-dove game can occur. The modified versions lead to several additional game types such as battle of the sexes, route-choice, and stag-hunt game. For some of the game types, all cells are predicted to stay on their original site (“grow phenotype”), while for other types, only a certain fraction stay and the other cells migrate away (“go phenotype”). If nutrient supply at the distant site is high, all cells are predicted to go. We discuss our predictions in terms of the pros and cons of caloric restriction, limitation of the supply of vitamins or methionine. Our results may help devise treatments that avoid metastases. References: 1. S. Hummert, K. Bohl, D. Basanta, A. Deutsch, S. Werner, G. Theißen, A. Schroeter, S. Schuster (2014). Evolutionary game theory: cells as players. Molecular Biosystems 10 (12), 3044 – 3065. https://doi.org/10.1039/c3mb70602h 2. Basanta, D., Hatzikirou, H., & Deutsch, A. (2008). Studying the emergence of invasiveness in tumours using game theory. The European Physical Journal B, 63(3), 393–397. https://doi.org/10.1140/epjb/e2008-00249-y
Additional authors: Stefan Schuster; Matthias Schleiden Institute, Friedrich Schiller University, Jena Heiko Stark; Matthias Schleiden Institute, Friedrich Schiller University, Jena

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