Authors: Chenyu Wu, Einar Bjarki Gunnarsson, Jasmine Foo, Kevin Leder

Published on: September 27, 2024

Impact Score: 7.4

Arxiv code: Arxiv:2409.18879

Summary

• What is new: This study introduces a robust statistical framework based on multi-type branching processes to analyze tumor dynamics and the effects of drugs using high throughput drug screening data.
• Why this is important: Resistance to therapy in cancer treatment is a major challenge largely due to the presence of a stem-like cell population that drives tumor recurrence after treatment. Additionally, many anticancer drugs unintentionally induce plasticity, making cancer cells revert to a drug-resistant stem-like state.
• What the research proposes: The proposed solution is a statistical framework that dissects tumor dynamics and drug effects, estimating parameters that govern population dynamics and drug responses, specifically focusing on drug-induced cell plasticity.
• Results: The framework was validated through comprehensive in silico experiments and showed efficacy in analyzing drug effects on tumor populations. Using recent in vitro data with AGS-SORE6+/- cells treated with ciclopirox olamine, it confirmed the presence of drug-induced cell plasticity.

Technical Details

Technological frameworks used: Multi-type branching processes

Models used: Statistical modeling for population dynamics and drug response

Data used: High throughput drug screening data and in vitro data involving AGS-SORE6+/- cells treated with ciclopirox olamine

Potential Impact

Pharmaceutical companies involved in anticancer drug development and biotechnology firms focusing on personalized cancer treatments could benefit from these insights.

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