A statistical framework for detecting therapy-induced resistance from drug screens

作者：Chenyu Wu, Einar Bjarki Gunnarsson, Jasmine Foo, Kevin Leder · 发表于：npj Systems Biology and Applications · 年份：2025 · DOI：10.1038/s41540-025-00560-8 · 被引用次数：2 · 研究领域：Cancer Genomics and Diagnostics、Mathematical Biology Tumor Growth、Single-cell and spatial transcriptomics

Resistance to therapy remains a significant challenge in cancer treatment, often due to the presence of a stem-like cell population that drives tumor recurrence post-treatment. Moreover, many anticancer therapies induce plasticity, converting initially drug-sensitive cells to a more resistant state, e.g. through epigenetic processes and de-differentiation programs. Understanding the balance between therapeutic anti-tumor effects and induced resistance is critical for identifying treatment strategies. In this study, we present a robust statistical framework leveraging multi-type branching process models to characterize the evolutionary dynamics of tumor cell populations. This approach enables the detection and quantification of therapy-induced resistance using high-throughput drug screening data involving total cell counts, without requiring information on subpopulation counts. The framework is validated using both simulated (in silico) and recent experimental (in vitro) datasets, demonstrating its ability to generate meaningful predictions.