MS06 - ONCO-1
Ohio Staters Traditions Room (#2120) in The Ohio Union

Integration of cellular processes in cell motility and cancer progression

Thursday, July 20 at 10:30am

SMB2023 Follow Thursday during the "MS06" time block.
Room assignment: Ohio Staters Traditions Room (#2120) in The Ohio Union.

Note: this minisymposia has multiple sessions. The other session is MS07-ONCO-1 (click here).

Yangjin Kim, Magdalena Stolarska

Description:

Cancer is a complex, multi-scale process, in which genetic mutations occurring at a sub-cellular level manifest themselves as functional changes at the cellular and tissue scale. Both the immediate microenvironment (cell-cell or cell-matrix interactions) and the extended microenvironment (e.g. vascular bed, stromal cells) are considered major players in tumour progression as well as suppression. The microenvironment is known to control tumour growth and cancer cell invasion to surrounding stromal tissue. Therefore, a thorough understanding of the interaction of individual cells with the microenvironment would provide a foundation to generate new strategies in cancer treatments. In particular, understanding the effect of the microenvironment on the signal transduction pathways of individual cells can improve cancer therapies by allowing one to target the specific biochemical pathways that are associated with the disease. Therefore, the main aim of this session is to discuss current stages and challenges in modelling tumour growth and the development of therapeutic strategies. Specific goals of the session include: (i) analyzing both computational and analytical solutions to mathematical models of tumor growth and its mechanical and biochemical interaction with the microenvironment, (ii) improving our biochemical/biomechanical understanding of fundamental mechanism of cellular movement in the context of cancer progression, and (iii) suggesting possible laboratory experiments that allow us to better understand cellular processes and lead to the design of platforms for clinical diagnosis. The development of mathematical models and their analysis and simulation allows us to shed light on our understanding of tumour growth in the host tissue environment and on the biochemical and biomechanical interactions between players in cancer progression.

Dumitru Trucu

University of Dundee (Mathematics)

"Multiscale Modelling Glioblastoma Progression within the Fibrous Brain Tissue"

Glioblastoma multiforme (GBM) is the most aggressive brain tumour, with patients having poor survival prospects despite recent surgery, radiotherapy and chemotherapy advancements. A central role in the development and spread of GBM within the brain is played by the collective cancer cell migration within the fibrous brain environment. This talk aims to explore this key invasion aspect through a novel non-local multiscale moving boundary modelling framework that takes into account the intrinsic link between overall macroscale tumour dynamics and both the microscale proteolytic activity at the invasive edge as well as the crucial bulk microdynamics of cancer cell-fibres interactions. T1 weighted and DTI scans are used as initial conditions for our model as well as to parametrize the diffusion tensor. Numerical results will illustrate clinically relevant GBM development patterns.

Junho Lee

Konkuk University (Mathematics / Seoul, Republic of Korea)

"Role of senescent tumor cell in building a cytokine shield in tumor microenvironment: mathematical models"

Cell aging can promote or inhibit cancer progression. Here, it was shown that the proportion of senescent tumor cells (STCs) in colorectal cancer (CRC) supported cancer growth by inhibiting intratumoral infiltration of CD8+ T cells. It has been found that the expression of C-X-C motif chemokines ligand 12 (CXCL12) and colony stimulating factor 1 (CSF1) in senescent tumor cells is increased, and senescent tumor cells secrete high concentrations of CXCL12 to spread chemokine shields. This inhibits the infiltration of CD8+T cells into tumor by causing loss of CXCR4 in T cells and interfering with directional movement. In this study, we investigate the mutual interactions between the CD8+ T cells and the STCs that prevent T cell invasion by developing a mathematical model that involves taxis-reaction-diffusion equations for the critical components in the interaction. We apply the mathematical model to a Boyden invasion assay used in the experiments to demonstrate that the over-expressed CXCL12 can prevent T cell infiltration into tumor. Moreover, we consider tumor-immune dynamics by a hybrid approach, we investigate the fundamental mechanism of STC-mediated cytokine shield and the impact on the migration patterning of T cells. We show that the model can both reproduce the major experimental observation on T cell infiltration and make several important predictions to guide future experiments with the goal of the development of new anti-tumor strategies.
Additional authors: Yangjin Kim, Konkuk University; Chaeyoung Lee, Ohio State University; Sean Lawler, Brown Cancer Center;

Eunjung Kim

Korea Institute of Science and Technology (Natural Product Informatics)

"Acquired resistance shapes the treatment outcomes by modulating the distribution of resistance"

Adaptive therapy (AT) is an evolution-based treatment strategy that exploits cell-cell competition. Acquired resistance can change the competitive nature of cancer cells in a tumor, impacting AT outcomes. We aimed to determine if adaptive therapy can still be effective with cells acquiring resistance. We developed an agent-based model for spatial tumor growth considering three different types of acquired resistance: random genetic mutations during cell division, drug-induced reversible (plastic) phenotypic changes, and drug-induced irreversible phenotypic changes. These three resistance mechanisms lead to different spatial distributions of resistant cells. To quantify the spatial distribution, we propose an extension of Ripley's K-function, Sampled Ripley's K-function (SRKF), which calculates the non-randomness of the resistance distribution over the tumor domain. This model predicts that the emergent spatial distribution of resistance can determine the time to progression under both adaptive and continuous therapy (CT). Notably, a high rate of random genetic mutations leads to quicker progression under AT than CT due to the emergence of many small clumps of resistant cells. Drug-induced phenotypic changes accelerate tumor progression irrespective of the treatment strategy. Low-rate switching to a sensitive state reduces the benefits of AT compared to CT. Furthermore, we also demonstrated that drug-induced resistance necessitates aggressive treatment under CT, regardless of the presence of cancer-associated fibroblasts. However, there is an optimal dose that can most effectively delay tumor relapse under AT by suppressing resistance. In conclusion, this study demonstrates that diverse resistance mechanisms can shape the distribution of resistance and thus determine the efficacy of adaptive therapy.
Additional authors: Masud M A, Natural Product Informatics Research Center, Korea Institute of Science and Technology, Gangneung, Republic of Korea; Jae-Young Kim, Graduate School of Analytical Science and Technology, Chungnam National University, Daejeon, Republic of Korea

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Annual Meeting for the Society for Mathematical Biology, 2023.