Analyzing single-cell trajectories with augmented machine learning methods predicts cancer drugs response in live-cell microscopy assays
Résumé
Cell response heterogeneity upon treatment is a main obstacle in preclinical development of ef-
ficacious cancer drugs, due to the emergence of drug-tolerant cells. We have previously developed a
single-cell pipeline called Fate-seq to profile drug-tolerant persisters, based on predictions of their drug
response. To automatize and increase the prediction throughput, we present a mathematical frame-
work, composed of machine-learning classification models augmented by an ODE model.
First, an ODE model of the extrinsic apoptosis initiation by death ligands is calibrated on time
trajectories of hundreds of treated clonal HeLa cells. The resulting deterministic systems are then
analysed, based on drug response, to highlight mechanistic features with predictive values for cell
decision. In a second step, we combine the predictions of the ODE system with machine-learning
classification models to determine the drug response of each cell before it commits to an irreversible
decision that would alter their states before profiling.
Here we show that the ODE model analysis could detect the time of cell decision shortly after treat-
ment, thanks to the emergence of an additional regulation at the receptor level in drug-sensitive cells.
Moreover, the parameters distribution of the deterministic system provided a biological threshold that
allows the prediction of cell response. Our mechanistic-informed approach, combining our ODE sys-
tem with machine learning classifiers, outperformed classic machine learning approaches and enabled
the accurate cell response prediction of otherwise unpredictable cells (Meyer et al., Cell Systems 2020).