# A morphology-based machine learning model for scoring epithelial-mesenchymal plasticity using organelle dynamics

## TL;DR

This paper introduces a machine learning model that uses organelle dynamics to score EMT, a process linked to cancer progression and drug resistance.

## Contribution

A novel machine learning approach to quantify EMT states using organelle morphology data across diverse cancer models.

## Key findings

- The model accurately captures EMT kinetics, hybrid states, and reversal in multiple cell types.
- It performs robustly across species, inducers, and cancer types, showing broad applicability.
- The method provides a scalable framework for drug discovery targeting EMT.

## Abstract

Re-activation of epithelial–mesenchymal transition (EMT), a key developmental process, contributes to cancer progression and therapy resistance. Modulating EMT could be attractive as a therapeutic strategy, but there is a lack of methods that can quantify EMT states, including hybrid phenotypes. Here, we developed a morphology-based machine learning approach to score EMT based on changes in organelle dynamics. Using the Cell Painting assay and high-throughput microscopy, we trained a histogram gradient boosting classifier to identify stage-specific organelle remodeling during a time course of TGF-β1-induced EMT in mammary epithelial cells. The model achieved robust performance across datasets, capturing EMT kinetics, hybrid states, and reversal by mesenchymal–epithelial transition (MET). Importantly, the method accurately scored EMT in human breast cancer cells and lung cancer cells undergoing hypoxia-induced EMT, demonstrating cross-species, cross-inducer, and cross-cancer applicability. The results establish organelle morphology profiling as a scalable framework for quantifying epithelial-mesenchymal plasticity. The method offers a platform for drug discovery and identifying strategies to overcome EMT-associated resistance.

Epithelial–mesenchymal plasticity drives metastasis and therapy resistance, but the lack of scalable quantitative assays has limited discovery of EMT-targeting drugs. Here, the authors present a morphology-based machine learning model scoring EMT from organelle dynamics.