SMB2023 FollowThursday during the "CT03" time block. Room assignment: Senate Chamber (#2145) in The Ohio Union.
University of Utah
"Mathematical Model of Drug Resistance in Cancer with respect to the Cancer Microenvironment"
One of the main obstacles to treating cancer is its ability to evolve and resist treatment. In this project, we mathematically model how the cancer microenvironment interacts with cancer cells and affects response to therapy in the context of estrogen-receptor positive (ER+) breast cancer, endocrine therapy, and cancer-associated fibroblasts (CAFs). The system is described with ordinary differential equations (ODEs) to investigate the impacts that cancer cells and CAFs have on each other’s population dynamics. We explore two different proposed scenarios of cancer-CAF dynamics: 1) cancer cells can recruit CAFs from an endless supply of fibroblasts, 2) a constant total population of fibroblasts that can switch between healthy and cancer-associated. In each scenario, we analyze stability of fixed points to determine the impacts of endocrine treatment and CAFs on the long-term behavior of cancer to address the questions: What role does estrogen/endocrine therapy play in resistance acquisition? Is resistance inevitable? If not, what can we do to prevent it? If resistance is inevitable, can we reverse it? and how? Both systems exhibit vastly different long-term outcomes dependent on estrogen availability in the system. For example, the models predict that a mere 20-hour difference in the initiation of endocrine therapy dictates the difference between the population of cancer dying off or growing infinitely. Furthermore, constant lower levels of available estrogen or constant small populations of CAFs prolong the systems' periods of slow growth. In the recruitment model, we also find that the existence of enough CAFs is necessary for the cancer population to grow exponentially under endocrine therapy or survive. Thus, the model suggests rapid tumor growth can be delayed by increasing the death rate of CAFs. By including CAFs in our model, we hope to provide new insights into how ER+ breast cancer develops resistance to endocrine therapy.
Additional authors: Dr. Linh Hyunh, Prof. Fred Adler (Senior Advisor)
Texas A&M University
"Microenvironmental Modulation of the Cancer-Immune Interaction"
This talk will describe our recent modeling effort to understand the stochastic dynamics of cancer dormancy, which refers to the ability of cancer cells to remain inactive below detection thresholds for prolonged time periods despite therapeutic interventions. There are different types of cancer dormancy, including cellular and immune-mediated dormancy. The balance between pro-tumor and anti-tumor immunity plays a critical role in cancer elimination or progression, resulting in cancer escape, elimination, or equilibrium. This equilibrium phase is associated with immune-mediated dormancy, where T cell killing matches the cancer division rate. Previous mathematical models that have been proposed to study dormancy, such as those using ordinary differential equations (ODEs), have limitations like neglecting the distributional behavior of cells and failing to make predictions with equilibrium population sizes close to zero, which may overlook the extinction probability of this absorbing state. To address these limitations, this talk will present a new stochastic model based on non-linear birth-death processes to more accurately describe dormancy dynamics. The model assumes a cancer population undergoing stochastic birth and death with an exponential growth rate, modified by an immunomodulation function that depends on the population size and an inhibitory element. This modeling framework can be used to identify the immunomodulatory effects of cancer therapy and the tumor microenvironment on the timing and likelihood of cancer elimination.
Additional authors: Jason T. George, Department of Biomedical Engineering/ School of Engineering Medicine, Texas A&M University
Texas A&M University
"Stochastic modeling of extracellular matrix spatial and geometric cues in the tumor microenvironment: insights into cancer evasion and T-cell dysfunction"
The identification of optimal cancer therapies is significantly complicated by the dynamic interplay between tumor immune evasion and T-cell exhaustion. Cytotoxic T-cell immunosurveillance plays a vital role in immunoediting cancers, and understanding the effects of immunoediting on cancer progression to escape is an ongoing work in progress. One critical factor that remains poorly understood is how the spatial and geometric cues of the extracellular matrix (ECM) in the tumor microenvironment affect the tumor-T-cell interaction. This is further complicated by ECM remodeling by primary cancer en route to metastasis. To address these challenges, we have developed a dual-agent-based model (ABM) to explore the relationship between ECM fiber geometry, tumor spatial growth, and the adaptive process of T-cell recognition of tumor-associated antigens. Using this model, we demonstrate the influence of ECM fiber orientation on cancer spatiotemporal progression. We compare and contrast the spatial dependence of tumor progression in the setting of circumferentially versus radially packed ECM fibers. By studying the balance of T cell accessibility on tumor recognition and antigen loss. Immune microenvironmental factors, including hypoxia and nutrient concentration, can explain cancer progression secondary to T-cell dysfunction. Overall, our preliminary findings provide a more detailed description of cancer spatiotemporal progression, and our model provides a computational means by which ECM geometry and microenvironmental parameters can be integrated for predicting the outcome of tumor-immune evolution across a number of contexts.
Additional authors: Jason T. George (Intercollegiate School of Engineering Medicine, Texas A&M University)
Zahra S. Ghoreyshi
Texas A&M University, College Station, TX, USA
"Optimal cellular phenotypic adaptation in fluctuating nutrient and drug environments"
Despite recent improvements in cancer therapy, phenotypic adaptation persists as a significant barrier in overcoming therapeutic resistance. Recent experimental efforts have attempted to minimize cancer cell growth by using increasingly sophisticated drug cycling strategies. However, this search has been slowed owing to the sheer complexity in the number of allowable temporally varying policies, thereby necessitating more efficient computational identification of optimal dosing strategies. In this study, we develop a stochastic description of cellular adaptation wherein temporally adaptive cells select their phenotype based on their prior encounter with an uncertain environmental landscape. We first apply this model to explain distinct growth phenotypes observed experimentally in bacterial systems navigating fluctuating nutrient landscapes. We then extend and apply our stochastic model in experimental collaboration to study prostate cell line-specific optimal adaptation to temporally varying enzalutamide therapy. Using this approach, we predict that under specific drug cycling frequencies, adaptive cells' nutrient availability is universally reduced compared to cells in constant ones, which confirms empirical observations about cancer cell growth.
Additional authors: Pelumi Olawuni, Department of Medicine, Duke University, Durham, NC, USA. Shibjyoti Debnath, Department of Medicine, Duke University, Durham, NC, USA. Jason Somarelli, Department of Medicine, Duke University, Durham, NC, USA. Jason T. George, Department of Biomedical Engineering, Texas A&M University, College Station, TX, USA, Engineering Medicine Program, Texas A&M University, Houston, TX, USA, Center for Theoretical Biological Physics, Rice University, Houston, TX, USA