Abstract 2839: Understanding the mesenchymal-to-epithelial transition and its drivers in triple-negative breast cancer with continuous normalizing flows

Abstract

Here we focus on understanding mechanisms that drive dynamic changes in gene expression and epigenetic marks that enable triple negative breast cancer cells to change states, and to thereby invade tissues and seed secondary tumors. The epithelial-to-mesenchymal transition (EMT) facilitates invasion and migration away from the primary tumor site. However, it is increasingly apparent that the reverse process, the mesenchymal-to-epithelial transition (MET), enhances metastatic colonization and growth via reacquisition of the epithelial phenotype. With no therapies currently available to stop metastatic tumor growth, we aim to uncover the mechanisms driving the MET towards identifying novel anti-metastatic therapies. We use the 3D in vitro mammosphere model system where single tumor-initiating cells residing in a partial-EMT state develop into a 3D organoid over 30 days. We sampled cells at 5 time points and performed scRNA-seq and scATAC-seq to analyze cell states. We develop a novel computational model of cellular development based on the theory of dynamic optimal transport (OT) and continuous normalizing flows. Our model TrajectoryNet is a neural ODE (ordinary differential equation) network that models the gradient of cell state with respect to time continuously over the input space and over time from cross-sectional single-cell data. TrajectoryNet interpolates between collected timepoints and learns a continuous realistic progression that describes cellular evolution in terms of gene expression and chromatin accessibility. Key to TrajectoryNet is a unique regularization to penalize the magnitude of the gradient over the flow. We prove this results in dynamic OT, thereby discouraging the neural network from taking circuitous or unrealistic paths. In contrast to TrajectoryNet, pseudotime, and RNA velocity are best at analyzing within a particular timepoint and do not handle large gaps in timepoints. We compare TrajectoryNet to RNA velocity and static OT and show that TrajectoryNet achieves better trajectories in terms of predicting withheld timepoints. Using TrajectoryNet, we identify a continuous ordering of events that occur during MET that show when and how the epithelial cell states begin to emerge. Such a continuous ordering can give rise to causal associations that can be inhibited to alter MET mechanisms. We also differentiate between trajectories that show self-renewal and maintenance of the tumor-initiating cells, and trajectories that revert to an epithelial state. Further we find that only ~10% of the initial seeded cells develop into mammospheres and identify which initial cells have the potential to seed secondary tumors. Hence, we can refine features (gene and epigenetic states) that define aggressive tumor-initiating cells in triple negative breast cancer, as well as their dynamics through the MET in order to find therapeutic targets.

Alex Tong

Postdoctoral Fellow

My research interests include optimal transport, graph scattering, and normalizing flows.