ONCO Subgroup Contributed Talks

Ana Forero Pinto

Moffitt Cancer Center/ University of South Florida

"An agent-based model with ECM to study the mechanics of DCIS microinvasions"

Microinvasions in ductal carcinoma in situ (DCIS) are malignant cells that have broken through the basement membrane (BM) and extend into the stroma with no focus larger than 1 mm. Since microinvasions constitute the first step in the metastatic cascade, identifying the causes of microinvasions will help distinguish between progressors or non-progressors among the DCIS patients, thus improving treatment. The mechanical tumor-stroma interactions play an important role in this process. Studies have shown that elevated collagen stiffening, deposition, and fibril crosslinking are correlated with tumor aggressiveness and invasion in breast cancer. Therefore, here we present SilicoDCIS, a 2D off-lattice center-based agent-based model (ABM) of ductal carcinoma in situ (DCIS) growth and its interaction with the extracellular matrix (ECM) to investigate the mechanical conditions that may lead to tumor microinvasions. SilicoDCIS simulates the division, growth, and migration of tumor cells in DCIS while interacting with other cell types and the ECM. This includes the BM, the myoepithelial and epithelial cell layers, and the collagen in the ECM. The ECM was modeled as a vector field, where the direction of each vector gives the orientation of a collagen bundle, and the vector magnitude is related to the bundle density. The growing DCIS can remodel the ECM (density and orientation), and in turn, the ECM applies a reciprocal force (proportional to the local collagen density) opposite to the tumor growth. With SilicoDCIS, we studied the mechanical effects of cancer cell proliferation and migration on the BM and the ECM. We found that higher cell migration force leads to increased BM stress and ECM density (on the tumor edges where cells migrate) and that the escape of the migrating cells from the duct vs. their intraductal confinement depends on cell speed. SilicoDCIS may provide insights into the mechanics of DCIS microinvasions to guide the design of future experiments.

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Chay Paterson

University of Manchester

"Wave-like behaviour in cancer evolution"

Compound birth-death processes are widely used to model the age-incidence curves of many cancers [1]. There are efficient schemes for directly computing the relevant probability distributions in the context of linear multi-stage clonal expansion (MSCE) models [2]. However, these schemes have not been generalised to models on arbitrary graphs, forcing the use of either full stochastic simulations or mean-field approximations, which can become inaccurate at late times or old ages [3, 4]. Here, we present a numerical integration scheme for directly computing survival probabilities of a first-order birth-death process on an arbitrary directed graph, without the use of stochastic simulations. As a concrete application, we show that this new numerical method can be used to infer the parameters of an example graphical model from simulated data.

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Nathan Schofield

University of Oxford

"Mechanistic modelling of cluster formation in metastatic melanoma"

Melanoma is the most aggressive type of skin cancer, yet survival rates are excellent if it is diagnosed early. However, if metastasis occurs, five-year survival rates drop significantly. During the early stages of tumour initiation, melanoma cells form clusters within the primary tumour which promote metastasis. In the absence of biological tools to visualise cluster formation at primary tumour sites, we develop mathematical models to generate mechanistic insight into their formation. For this work we utilise in vitro data for two distinct melanoma cell phenotypes, one more proliferative and the other more invasive. This data consists of experiments for each phenotype individually, resulting in homogeneous clusters, as well as mixtures of the two phenotypes, resulting in heterogeneous clusters. We develop a series of differential-equation-based models using a coagulation-fragmentation-proliferation framework to describe the growth dynamics of homogeneous clusters, incorporating different functional forms for cell proliferation and cluster splitting. We then extend these models to describe the formation of heterogeneous cell clusters by considering both cluster size and phenotypic composition. We fit the models to experimental data, using a Bayesian framework to perform parameter inference and information criteria to perform model selection. In this way, we characterise and quantify differences in the clustering behaviour of two melanoma phenotypes in homogeneous and heterogeneous clusters, particularly the cluster coagulation, proliferation, and splitting rates. We find that the coagulation rate for the invasive phenotype is much larger than that for the proliferative phenotype, and evaluate how well different modelling assumptions fit the data in order to increase our understanding of the mechanisms driving metastasis. In future work, the models will be used to inform further experiments and, in particular, to suggest and test strategies for inhibiting metastasis.

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Sergio Serrano de Haro Ivanez

University of Oxford

"Topological quantification of colorectal cancer tissue structure"

A hallmark of colorectal cancer is the structural disruption of the colonic tissue, a process correlated with disease progression. Intestinal crypts, glands essential for homeostasis, lose their tubular morphology - and function - due to uncontrolled cell proliferation and tissue invasion. Evaluating this deterioration in biopsied samples is critical for both patient diagnosis and prognosis. Histopathological methods are essential for assessing colorectal cancer status, but their precision and reproducibility can be improved. Spatial biology provides a mathematical framework to analyse the structural properties of biological data; in this work, we apply techniques from topological data analysis and network science to quantify architectural changes in colorectal cancer progression. Using cell point clouds derived from immunohistochemistry imaging, we construct cell networks that encode topological tissue features. We employ these networks to segment large, imaged samples into smaller, biologically meaningful regions of interest that preserve tissue architecture. We compare the performance of our approach to conventional segmentation methods such as quadrat division. Within these segmented regions, we further employ methods from persistent homology to quantify tissue structure, with the long-term goal of identifying novel biomarkers of disease progression.

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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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