# Integrating multiscale mathematical modeling and multidimensional data reveals the effects of epigenetic instability on acquired drug resistance in cancer

## TL;DR

This paper uses mathematical models and data to show how epigenetic instability in cancer cells leads to drug resistance and suggests better treatment schedules.

## Contribution

A novel multiscale mathematical model integrating multidimensional data to study epigenetic instability and intermittent treatment in drug resistance.

## Key findings

- Epigenetic instability is a key factor in the emergence of drug-tolerant persister cells.
- Optimal intermittent treatment schedules can be determined using the model.
- New biomarker genes and biological features of DTP cells were identified using single-cell RNA-seq data.

## Abstract

Biological and dynamic mechanisms by which Drug-tolerant persister (DTP) cells contribute to the development of acquired drug resistance have not been fully elucidated. Here, by integrating multidimensional data from drug-treated PC9 cells, we developed a novel multiscale mathematical model from an evolutionary perspective that encompasses epigenetic and cellular population dynamics. By coupling stochastic simulation with quantitative analysis, we identified epigenetic instability as the most prominent kinetic feature related to the emergence of DTP cell subpopulations and the effectiveness of intermittent treatment. Moreover, we revealed the optimal schedule for intermittent treatment, including the optimal area for therapeutic time and drug holidays. By leveraging single-cell RNA-seq data characterizing the drug tolerance of lung cancer, we validated the predictions made by our model and further revealed previously unrecognized biological features of DTP cells, such as cell autophagy and migration, as well as new biomarker genes of therapeutic tolerance. Our work not only provides a paradigm for the integration of multiscale mathematical models with newly emerging genomics data but also improves our understanding of the crucial roles of DTP cells and offers guidance for developing new intermittent treatment strategies against acquired drug resistance in cancer.

Drug-tolerant persister (DTP) cells were recently identified as exhibiting a transient phenotype toward acquired drug resistance, which is one of the main obstacles in the fight against cancer. However, the biological and dynamic mechanisms by which DTP cells contribute to the development of acquired drug resistance in cancer have not been fully elucidated, which hampers the development of more effective anticancer therapies. Currently, multidimensional data of acquired drug resistance in cancer are available, which has great significance for integrating different dimensions of data to systematically understand the development of acquired drug resistance and make dynamic decisions regarding anticancer therapies. Here, we developed a novel multiscale mathematical model and a dynamics-based integrative method for multidimensional data to identify new biomarkers for DTP cells. Furthermore, we proposed the optimal schedule of intermittent treatment using the optimization model and predicted the biomarkers for tolerance of intermittent treatment using scRNA-seq data. Our multiscale modeling framework and computational analytical approach for scRNA-seq data provide a new avenue for predicting the dynamic features of DTP cells leading to the development of acquired drug resistance.