Self-generated persistent random forces drive phase separation in growing tumors

TL;DR

This study reveals that heterogeneity in tumor cell dynamics, driven by self-generated forces from cell division, leads to phase separation within tumors, with core and peripheral cells exhibiting distinct behaviors, analyzed through machine learning techniques.

Contribution

The paper introduces a novel application of t-SNE to identify phase space partitioning in tumor cell populations driven by active forces, highlighting a new mechanism for intratumor heterogeneity.

Findings

Core and periphery cells show distinct dynamic behaviors.

Self-generated forces from cell division induce phase separation.

t-SNE reveals heterogeneity not captured by traditional metrics.

Abstract

A single solid tumor, composed of nearly identical cells, exhibits heterogeneous dynamics. Cells dynamics in the core is glass-like whereas those in the periphery undergo diffusive or super-diffusive behavior. Quantification of heterogeneity using the mean square displacement or the self-intermediate scattering function, which involves averaging over the cell population, hides the complexity of the collective movement. Using the t-distributed stochastic neighbor embedding (t-SNE), a popular unsupervised machine learning dimensionality reduction technique, we show that the phase space structure of an evolving colony of cells, driven by cell division and apoptosis, partitions into nearly disjoint sets composed principally of core and periphery cells. The non-equilibrium phase separation is driven by the differences in the persistence of self-generated active forces induced by cell division. Extensive heterogeneity revealed by t-SNE paves way towards understanding the origins of intratumor heterogeneity using experimental imaging data.