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

TL;DR

This paper presents a statistical framework based on multi-type branching process models to detect and quantify therapy-induced resistance in tumor cell populations using high throughput drug screening data, aiding in understanding resistance mechanisms.

Contribution

The study introduces a novel statistical approach that models tumor cell population dynamics and resistance development, validated through simulations and real-world data analysis.

Findings

Effective estimation of population dynamic parameters

Successful detection of therapy-induced resistance

Predictive insights from in vitro data

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 introduce a robust statistical framework, based on multi-type branching process models of the evolutionary dynamics of tumor cell populations, to detect and quantify therapy-induced resistance phenomena from high throughput drug screening data. Through comprehensive in silico experiments, we show the efficacy of our framework in estimating parameters governing population dynamics and drug responses in a heterogeneous tumor population where cell state transitions are influenced by the drug. Finally, using recent in vitro data from multiple sources, we demonstrate that our framework is effective for analyzing real-world data and generating meaningful predictions.