Poster S3 - Defining whether cancer cell motility can predict tumor morphology

Introduction: Studies using cancer cells have begun to shed light on how motility and the microenvironment may contribute to tumor progression and are consistent with the hypothesis that the diversity of motility within a tumor may play a critical role in its malignant progression. However, we are still unable to predict whether a tumor detected at an early stage of its development will become invasive and metastatic. The ability to make the predictions may improve the selection of potential targets for personalized medicine. Here we report an experimental and computational study focused on understanding the role of cancer cell motility in regulating tumor morphology and progression.

Materials and

Methods: We took advantage of the inherently heterogeneous 4T1 mouse mammary carcinoma model of triple-negative breast cancer. Specifically, we compared the behaviors of clonal subpopulations isolated from the parental line, which exhibited epithelial or mesenchymal-like morphologies and gene-expression patterns (1). To characterize cell motility in two-dimensional (2D) culture, we carried out timelapse imaging analysis of cells that were seeded at equal densities and followed for 72 hours. To characterize motility and morphology in three-dimensional (3D) culture, we adapted a micromolding-based approach to generate tumor spheroids. 4T1 cells were seeded into microwells in agarose and allowed to aggregate and form into spheroids. After three days, we collected the spheroids and embedded them within type I collagen gel. Cell motility and tumor (spheroid) morphology were monitored in 3D using confocal imaging analysis. To gain insights into how cellular motility influences the morphology of tumor spheroids, we constructed a minimal agent-based model with density-dependent ccell velocities.

Results, Conclusions, and Discussions: Timelapse imaging analysis in 2D cultures revealed that epithelial-like and mesenchymal-like clonal subpopulations exhibited distinct migratory behaviors. We found that cells in the epithelial population maintain interaction with their neighbors and form clusters that exhibit limited rearrangements. In contrast, cells in the mesenchymal population exhibit a more fluid behavior and frequently exchange neighbors. Timelapse imaging analysis of 3D cultures revealed that epithelial-like spheroids grew and expanded in diameter, while mesenchymal-like spheroids invaded and gave rise to irregular stellate tumor morphologies. To test whether the differences in migratory strategy between the epithelial-like and mesenchymal-like cells might be sufficient to generate the different spheroid morphologies, we created a minimal 3D agent-based model that quantitatively describes individual cell motion in the context of the local microenvironment. The model combines density-dependent velocity, soft repulsive interactions, and neighbor alignment to determine the evolution of tumor morphology over time.

Several studies have uncovered how cancer cells use both individual and collective strategies to detach from the primary tumor and metastasize, but it remains unclear whether and how these migratory strategies contribute to the morphology of the tumor in the first place. Using a combination of experimental and computational approaches, we found that the mode of cancer cell motility is predictive of the large scale tumor morphology. We anticipate that this combination of experiments and computational modeling will enable the prediction of cancer progression across a broad range of cancer types and migratory strategies.

(1) S. E. Leggett, M. C. Brennan, S. Martinez, J. Tien, C. M. Nelson, Relatively Rare Populations of Invasive Cells Drive Progression of Heterogeneous Tumors. Cellular and Molecular Bioengineering 17, 7-24 (2024).

Acknowledgements (Optional): This work was supported in part by grants from the National Institutes of Health (CA187692, CA21492, HL164861, HD099030, HD111539, and the National Science Foundation (2134935)). CT was supported in part by the New Jersey Department of Health, the Division of Office of Research Initiatives, and the New Jersey Commission on Cancer Research (NJCCR) through the 2023 NJCCR Postdoctoral Research Grant, and the Damon Runyon Cancer Research Foundation through the Quantitative Biology Postdoctoral Fellowship. SB was supported by a postdoctoral fellowship from the Momental Foundation and SB is The Shurl and Kay Curci Foundation Awardee of the Life Science Research Foundation.