MS01 - ONCO-2
Senate Chamber (#2145) in The Ohio Union

Evolutionary game theory in cancer

Monday, July 17 at 10:30am

SMB2023 Follow Monday during the "MS01" time block.
Room assignment: Senate Chamber (#2145) in The Ohio Union.

Note: this minisymposia has multiple sessions. The other session is MS02-ONCO-2 (click here).

Anuraag Bukkuri, Katerina 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.

Helena Coggan

University College London (Mathematics)

"Simulations of 3D organoids suggest inhibitory neighbour-neighbour cell signalling as a possible growth mechanism in early lung cancer"

Cancer is driven by the development of genetic mutations. Some mutations which appear in aggressive lung cancers, particularly in people who have never smoked, have also been found to exist quite harmlessly in perfectly healthy people. Although inflammatory cytokines have been highlighted as important promoters of tumour formation, it is unclear what additional stimuli are required in order to drive a `normal cell' harbouring an oncogenic mutation into an invasive tumour. Game-theoretic models suggest that cell fitness may depend on interactions with neighbouring cells. To examine this hypothesis, we looked at the behaviour of stem cells with an activating mutation in EGFR, L858R, when they were given all the nutrients and space required to grow uninhibited in three dimensions. We used computational simulations to model their growth, and predicted that these cells seemed to be suppressing the division of any other cells they touch. We hypothesise that in the very early stages of cancer development, this mutation gives cells a reproductive advantage by preventing the division of non-mutant cells in their environment and driving down competition for space and resources. This also suggests that the success of these pre-cancerous cells depends on their spatial environment and the surrounding cell ecology. We hope that this insight into early cancer development will drive more research into the consequences of cell-cell interaction dysfunction on early tumour initiation.
Additional authors: Clare E. Weeden; Philip Pearce; Mohit P. Dalwadi; Alastair Magness; Charles Swanton; Karen M. Page

Monica Salvioli - Part 1

Delft University of Technology (Institute for Health Systems Science)

"Validation of the polymorphic Gompertzian model of advanced cancer through in vitro and in vivo data"

Mathematical modeling plays an important role in forming our understanding of therapy resistance mechanisms in cancer. Gompertzian model, analyzed recently by Viossat and Noble in the context of adaptive cancer therapy, describes a heterogeneous cancer population consisting of therapy-sensitive and -resistant cells interacting with each other. Their mathematical analysis demonstrates advantages of adaptive therapy in such models. However, before the theoretical findings can be implemented to cancer therapy design, the model should be validated with real-world data. In our study, we show that the polymorphic Gompertzian model successfully captures trends in both in vitro and in vivo data on non-small cell lung cancer (NSCLC) dynamics under treatment. Biological interpretation of the model’s fit to in vitro data allowed us to confirm previously reported anti-treatment effects of cancer-associated fibroblasts. For the in vivo data, we showed the superiority of the polymorphic Gompertzian model over the monomorphic classical models in fitting the U-shape trend of tumor dynamics and comparable accuracy in other trend categories. In general, the polymorphic Gompertzian model corresponds well to real-world data, thus, its theoretical conclusions can be implemented to the development of clinical studies on adaptive cancer therapy.

Christin Nyhoegen

Max Planck Institute for Evolutionary Biology (RG Stochastic Evolutionary Dynamics)

"Mathematical models for the optimization of multi-drug treatment strategies"

The evolution of resistance in bacteria and other pathogens, as well as in cancer, poses a major challenge for patient treatment worldwide. One option to reduce the risk of resistance during treatment is to increase the genetic barrier to resistance, which can, for example, be achieved by increasing the number of drugs applied. Different drugs could be alternated (sequential therapy) or administered simultaneously (combination therapy). With multiple drugs in combination, applying the different drugs at the same concentration as in mono-therapy may not be necessary to clear the population of susceptible cells. In fact, reducing the doses might even be required to avoid toxicity. However, lowering the dose might reduce the benefits of combination therapy in controlling resistance. How should we thus choose the number of drugs and their doses to minimize the risk of resistance while efficiently treating a patient and avoiding side effects? In this talk, I will present a pharmacodynamic model for the combination of multiple antibiotics, which can be adapted to study other resistance problems. This model allows us to compare the probability of resistance under mono-therapy at high drug doses and combination therapies at lower doses, keeping the 'total dose' constant. For most of the parameter space, combination therapy with two drugs leads to a lower probability of resistance than mono-therapy. Still, it is not always superior to the treatment with just one drug. Especially the pharmacodynamic properties and the mode of action of the drugs influence the optimal treatment choice. Our mathematical analysis allows us to disentangle the effects of a strategy on the appearance of mutations from those on their establishment probability, allowing us to understand what leads to a strategy's success.
Additional authors: Hildegard Uecker (Max Planck Institute for Evolutionary Biology); Sebastian Bonhoeffer (ETH Zurich)

Alanna Sholokhova

University of Washington (Applied Mathematics)

"Quantifying neoantigen evolution and response to immunotherapy in colorectal cancer"

Each cancerous colorectal tumor contains tumor-specific antigens (neoantigens). Because these neoantigens are present only in the tumor and not in healthy tissue, they are excellent targets for cancer immunotherapies. Checkpoint-blockade immunotherapy enables the patient’s native immune system to recognize tumor cells that were previously invisible due to immune escape, but this therapy has extremely heterogeneous patient outcomes, ranging from total failure to complete remission. We seek to understand how the mutagenic landscape of the tumor is related to therapeutic outcomes. First, we model neoantigen evolution using a stochastic branching-process model. Next, we use a dynamical model of anti-PD1 checkpoint-blockade therapy to predict response to therapy in these in-silico tumors. We relate therapeutic outcomes to heterogeneity of tumor mutational landscape, quantified by both the number of mutations in the tumor as well as the clonality of the neoantigens present in the tumor. We find that mutational burden, the total number of neoantigenic mutations present in the tumor, is insufficient to determine therapeutic outcome. Neoantigenic clonality, the fraction of tumor cells that contain a particular neoantigen, is key in determining response to therapy.
Additional authors: Kamran Kaveh (University of Washington); Ivana Bozic (University of Washington)

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Annual Meeting for the Society for Mathematical Biology, 2023.