Speaker
Description
Collective cancer invasion often displays a distinct spatial hierarchy, with leader cells at the tips of invasive chains and follower cells trailing behind. Experiments using the Spatiotemporal Genomic and Cellular Analysis (SaGA) platform have shown that these subpopulations differ in motile, signaling, and proliferative behavior. But a fundamental question remains unresolved: what makes a leader? Are leader cells a fixed intrinsic subtype, or do leader-like states emerge from local context, biophysical interactions, and phenotypic plasticity? If leaders are removed, can other cells replace them? Here, we develop a computational analogue of the SaGA protocol using a two-dimensional Cellular Potts Model to address these questions. Each tumor cell is initialized with a randomly assigned combination of adhesion strength, migration coefficient, and proliferative probability, drawn from distributions representing four evolutionary scenarios. Leader- and follower-like behaviors are not imposed a priori, but emerge from the collective invasion dynamics and are classified post hoc by spatial position within the evolving tumor. We systematically investigate how the emergence, stability, and replaceability of leader-like cells depend on four biologically motivated scenarios: an unconstrained random baseline, heritable trait transmission with stochastic perturbation, clonally grouped initialization mimicking discrete sublineages, and biophysical trade-offs among adhesion, motility, and proliferation. By asking when leader cells arise, whether they persist, and whether they can be replaced, this model provides a mechanistic framework for distinguishing fixed cellular identity from context-dependent invasive state. It also offers a platform to study how heterogeneity, plasticity, and trade-offs regulate collective invasion and metastatic potential.
