ONCO-05 Contributed Talks

Paulameena Shultes

Case Western Reserve University

"Cell-Cell Fusion in Cancer: Key In Silico Tumor Evolutionary Behaviors"

Cell-cell fusion is a known phenomenon throughout the human body. It characterizes a wide range of physiological and pathological processes, ranging from placentation and embryogenesis to cancer stem cell (CSC) formation. There is increasing evidence that cell-cell fusion can play key roles in the development and progression of cancer, particularly by increasing intratumor heterogeneity and potentiating somatic evolution. There are many unanswered questions surrounding the characteristics that define cancer cell-cell fusion events, their frequency in in vivo tumor conditions, and whether or not cell-cell fusion is a universal phenomenon across cancer. Using a combination of in vitro and in silico approaches, we can begin to answer some of these questions. We have developed a preliminary cellular automata model using HAL to evaluate the effect of variable cell-cell fusion rates and behaviors under a range of tumor microenvironmental conditions. By comparing our spatial model to a suite of ordinary differential equations, we can begin to estimate the effects of cell-cell fusion on the genomic heterogeneity and malignancy potential of cancers in vivo. I demonstrate the importance of improving fusion rate estimates using the simplest iteration of an in silico cellular automata model (coined SimpleFusion). The preliminary SimpleFusion model results illustrate how much the impact of cell fusion, as measured by the percentage of cells that have had a fusion event in their lineage, changes between orders of magnitude of fusion rates. Corresponding ODE models demonstrate similar results despite the lack of encoded spatial information. By studying these two types of models (ABM, ODEs) in combination, we can begin to understand what parameters most directly define the cell-cell fusion population dynamics in our in vitro fusion experiments and, in turn, in vivo conditions as well.

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Thomas Stiehl

Institute for Computational Biomedicine and Disease Modeling, University Hospital RWTH Aachen, Aachen, Germany & Department of Science and Environment, Roskilde University, Roskilde, Denmark

"Computational Modeling of the Aging Human Bone Marrow and Its Role in Blood Cancer Development"

Blood cancers pose a growing medical and economic challenge in aging societies. Every day, the human bone marrow (BM) generates more than 100 billion blood cells. This process is driven by hematopoietic stem cells (HSCs), which retain their ability to proliferate and self-renew throughout life. However, over time, HSCs accumulate mutations that may lead to malignant transformation, as seen in acute myeloid leukemia (AML), one of the most aggressive cancers. Even in healthy individuals, the BM undergoes age-related changes, including a decline in cell numbers, remodeling of the BM micro-environment, and a bias in HSC differentiation. Emerging evidence suggests that these alterations create a favorable environment for the expansion of mutated cells, thereby promoting blood cancer development and progression. Mathematical and computational models facilitate our understanding of how BM aging contributes to malignant cell growth. We propose nonlinear ordinary differential equation models to describe blood cell formation and clonal competition in the human BM. The models incorporate micro-environmental and systemic feedback loops and are informed by data from both healthy individuals and cancer patients. Our findings suggest that the age-related decline in HSC self-renewal, combined with increased chronic inflammation (inflammaging), makes the BM more susceptible to the expansion of mutated cells and at the same time impairs treatment response. Through mathematical analysis, quantitative simulations, and patient data fitting, we study the following questions: 1. How do HSC proliferation & self-renewal change during physiological aging? 2. How do age-related alterations in healthy BM contribute to blood cancer development? 3. What is the impact of chronic inflammation on HSC function and blood cancer progression? 4. How do age-related BM changes affect treatment responses, e.g., in AML patients? 5. How could treatment protocols be adapted to elderly patients?

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Aisha Turysnkozha

Nazarbayev University

"Traveling wave speed and profile of a “go or grow” glioblastoma multiforme model"

Glioblastoma multiforme (GBM) is a fast-growing and deadly brain tumor due to its ability to aggressively invade the nearby brain tissue. A host of mathematical models in the form of reaction–diffusion equations have been formulated and studied in order to assist clinical assessment of GBM growth and its treatment prediction. To better understand the speed of GBM growth and form, we propose a two population reaction–diffusion GBM model based on the ‘go or grow’ hypothesis. Our model is validated by in vitro data and assumes that tumor cells are more likely to leave and search for better locations when resources are more limited at their current positions. Our findings indicate that the tumor progresses slower than the simpler Fisher model, which is known to overestimate GBM progression. Moreover, we obtain accurate estimations of the traveling wave solution profiles under several plausible GBM cell switching scenarios by applying the approximation method introduced by Canosa.

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Brian Johnson

UC San Diego

"Integrating clinical data in mechanistic modeling of colorectal cancer evolution in inflammatory bowel disease"

Patients with inflammatory bowel disease (IBD) face an elevated risk of colorectal cancer (CRC), necessitating lifelong surveillance to find and remove precancers before they become malignant. Current one-size-fits-all approaches are inadequate and tailored strategies that consider cancer evolution are needed. To address this, we developed a mechanistic framework of IBD-CRC progression. Our multi-type branching process model accounts for IBD onset, mutational processes, and both precancerous (adenoma/dysplasia) and malignant clonal expansion. Initial parameter estimation for mutation and growth rates when fitting the multi-stage clonal expansion model to epidemiological IBD-CRC data yielded similar estimates to those found previously in sporadic CRC but suggest higher mutation rates and slightly lower growth rates in IBD. However, this data may not perfectly represent the natural history, as surveillance colonoscopy with lesion removal and colectomy alter the observable progression. Further, fitting to cancer incidence data alone presents parameter identifiability issues, restricting our initial fit to four parameters. To address these limitations, our study draws upon extensive clinical data from the U.S. Veterans Health Administration, employing validated methods using large language models to construct high-quality datasets with detailed information on surveillance colonoscopy timing, colectomies, and intermediate lesions extracted from pathology reports. To integrate these data, we developed a complementary fast simulation model, which will be released as an R package. This simulation model incorporates clinical interventions, such as colonoscopy with size-dependent lesion removal. Our combined analytical and simulation approach captures the complex precancerous evolution in IBD, providing a quantitative foundation for more effective, personalized surveillance guidelines. Further, this approach can be adapted to improve surveillance in the general population.

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