ArticleNPJ systems biology and applications2025
A statistical framework for detecting therapy-induced resistance from drug screens.
Article in NPJ systems biology and applications, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
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Who cites it
2 citing papers in PubMed.
- Growth rate-driven modelling suggests that phenotypic adaptation drives drug resistance in BRAFV600E-mutant melanoma.Communications biology · 2026Article
- Optimal dosing of anti-cancer treatment under drug-induced plasticity.NPJ systems biology and applications · 2025Article
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Authors and funding
4 authors.
Funding
Abstract
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.
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Registered trials
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