MS01 - ONCO-10

Systems Approaches to Cancer Biology

Monday, July 14 at 10:20am

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Ashlee N. Ford Versypt (University at Buffalo, State University of New York), John Metzcar, University of Minnesota

Description:

Cancer systems biology aims to understand cancer as an integrated system of genes, proteins, cells, tissue microenvironments, treatments, whole body physiology, and environmental factors. Cancer is connected in interacting networks across scales, rather than an entity of isolated molecular and cellular components. Many mathematical approaches are powerful tools for both mechanistic modeling and data-driven analyses that enable new insights into various scales of cancer systems biology. A growing community of interdisciplinary researchers in cancer systems biology have assembled at a meeting called Systems Approaches to Cancer Biology, held five times since 2016. A motivation for this minisymposium is to feature researchers from the cancer systems biology community to broaden the exposure and connections with the mathematical oncology and wider community through SMB. This minisymposium will focus on the mathematical methods and models applied to understand the complexities of cancer systems biology. Potential topics include: - Dynamics for cancer cell signaling and metabolic interactions - Tensor-based/multilinear modeling for integrating cancer omics data - Agent-based models of interactions in the tumor microenvironments - Boolean network modeling for cancer cell functions - Machine learning to infer targetable pathways and treatment options

Ashlee N. Ford Versypt

University at Buffalo, State University of New York

"Agent-Based Modeling of the Transwell Migration Assay to Inform Tumor-Immune Microenvironment Simulations"

Cell migration in tumor microenvironments is crucial in disease progression and treatment efficacy. Recent experimental studies reported variations in chemotactic migration of cancer and immune cells. A popular method to study chemotaxis is the transwell migration assay. To complement the in vitro experiments to characterize tumor and immune cells in this assay, we developed a 3D agent-based model with Compucell3D to simulate the effects of random and directed cell migration in response to chemokines. To accommodate various cell lines, we categorized targeted cell lines into 6 groups based on their size and adhesion to the membrane. The model shows a 3D column space of the transwell device with 400 moving agents and periodic boundary conditions applied to vertical surfaces of the domain to simulate the dynamics of the in vitro transwell experiments where cells have realistic biomechanics of neighboring cells and tissue-mimic biomaterials. A solid plane contains randomly distributed pores that mimic the structure of the collagen-coated membrane with the same level of pore density. Chemokines are initiated from the bottom half of the assay below the membrane and can diffuse upwards to generate a concentration gradient. Several parameters, including chemical concentrations, diffusion coefficients, chemotactic potential coefficient, an external potential energy term, and a contact energy term are included with a direct connection to published data. The randomized external potential energy simulates the intrinsic Brownian motion of cells and drives cells to move through the membrane in the negative control group without chemokines. Smaller contact energy between cells and the membrane mimics stronger cell-collagen adhesion. The chemotactic energy term and heterogeneous chemical field regulate directional chemotaxis. We observed that larger external potential energy can induce more cells to migrate through the membrane. Thus, we calibrate this energy term with negative control group data from different cell lines (e.g., Panc1, MiaPaCa2, HCT116, U937, and THP-1). Our simulated results also predicted variations in cell migration with cell density and pore density of the membrane in the negative control groups. We employed various methods to generate Brownian motion and analyzed the resulting trajectories with their velocity profiles and mean squared displacements (MSD), finding these methods affected cell persistence, average velocity, and diffusivity. Next, we are extending the model to investigate the effects of chemokine concentrations and diffusion. In the future, we will implement these validated mechanisms and physiological properties into larger systems of agent-based models to simulate cancer pathology and therapy inside the body, considering cells, chemokines, and tumor immune microenvironments in various tissues. Acknowledgments: This work was supported by the National Institutes of Health grant R35GM133763 and the University at Buffalo. Co-author MBD is supported in part by R01 CA226279.

Aaron Meyer

University of California, Los Angeles

"Bridging single cell features to the tissue and patient scale with tensor modeling"

High-dimensional single-cell measurements have revolutionized our ability to study the variation within and between heterogeneous cell populations. As single-cell RNA-sequencing (scRNAseq) and similar technologies have become increasingly accessible, these technologies have extended to studies including multiple experimental conditions, samples, or subjects. Multi-condition single-cell experiments can evaluate how heterogeneous cell populations behave across patients according to disease pathology. Analyzing multi-condition single-cell datasets to link cellular heterogeneity to patient-level features like disease state is crucial, but challenged by the limited sample sizes and the inherent misalignment of cell states across individuals. To overcome these hurdles, we developed ULTRA (Unaligned Low-rank Tensor Regression with Attention). ULTRA leverages a tensor framework to model the multi-way data structure and employs an attention mechanism to derive interpretable, cell-specific gene signatures associated with patient features, crucially without requiring prior cell population alignment across samples. In ULTRA, a fit gene signature scores each cell within a sample; an attention mechanism weights cells based on their expression of this signature. The aggregated cells are then regressed against the patient feature of interest. Importantly, despite using attention, a well-known mechanism enabling transformer models, ULTRA is otherwise a linear model, maximizing data use and interpretability. We applied ULTRA across several scRNAseq datasets, demonstrating its consistently superior ability to identify associative signatures with patient-level features. As one example, I will show how ULTRA identifies features of the tumor microenvironment that are predictive of immunotherapy response across several cancers. A key challenge in deriving associations between single cell measurements and patient-level characteristics is that, while there is an abundance of data, these datasets typically include only a few samples due to cost. Therefore, we used the ULTRA model to optimize future single cell profiling experiments. I will cover several lessons regarding optimal cell numbers and read depths for improving the insights from single cell studies at reduced cost.

Erzsébet Ravasz Regan

The College of Wooster

"Cell Interrupted — Modular Boolean Modeling of the Coordination between Mitochondrial Dysfunction-Associated Senescence, Cell Cycle Control and the Epithelial to Mesenchymal Transition"

The steady accumulation of senescent cells with aging creates tissue environments that aid cancer evolution. Aging cell states are highly heterogeneous. 'Deep senescent' cells rely on healthy mitochondria to fuel a strong proinflammatory secretome, including cytokines, growth and transforming signals. Yet, the physiological triggers of senescence, such as reactive oxygen species (ROS), can also trigger mitochondrial dysfunction. This energy deficit alters the secretome of these cells and causes chronic oxidative stress – a state termed Mitochondrial Dysfunction-Associated Senescence (MiDAS). Here, we offer a mechanistic molecular model for MiDAS in the form of a Boolean regulatory network that qualitatively captures key aspects of mitochondrial dynamics during cell cycle progression (hyper-fusion at the G1/S boundary, fission in mitosis), apoptosis (fission and dysfunction) and glucose starvation (reversible hyperfusion), as well as MiDAS in response to SIRT3 knockdown or oxidative stress. We offer testable predictions about the growth factor- and glucose-dependence of MiDAS and its reversibility at different stages of reactive oxygen species (ROS)-induced senescence. We then link this model to our large modular model of mechano-sensitive Epithelial to Myesenhynal Transition, and show that EMT is incompatible with MiDAS. Our models provide mechanistic insights into the distinct stages of DNA-damage induced senescence, the relationship between senescence and epithelial-to- mesenchymal transition in cancer and offers a foundation for building multiscale models of tissue aging.

Stacey D. Finley

University of Southern California

"Systems biology modeling and analyses of metabolic phenotypes in the tumor microenvironment"

Colorectal cancer (CRC) remains one of the most prevalent and lethal malignancies worldwide, ranking third in cancer incidence and second in mortality. Despite advances in targeted therapies and immunotherapy, clinical outcomes often remain poor, largely due to the complex nature of the disease that extends beyond just the malignant cells themselves. Like all solid tumors, CRC develops as an ecosystem rather than a simple mass of cancer cells. As tumors progress, metabolic interactions between tumor and stromal cells in the tumor microenvironment (TME) lead to altered cellular growth and contribute to invasion, metastasis, and drug resistance. The complexity of metabolic interactions in the TME requires computational approaches that can capture system-level behaviors. We have developed genome-scale metabolic models of cancer cells, cancer-associated fibroblasts, and macrophages. We applied the models to predict the flux distributions and compare the cells’ metabolic phenotypes in the TME. We also apply graph theoretical methods to analyze the structure and organization of the cells’ metabolic networks. The integration of genome-scale metabolic modeling and graph theory produces new insights into metabolism in CRC and identifies strategies to exploit metabolic crosstalk to inhibit tumor progression.

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Annual Meeting for the Society for Mathematical Biology, 2025.