Measuring competitive exclusion in non–small cell lung cancer

Abstract

In this study, we experimentally measure the frequency-dependent interactions between a gefitinib-resistant non–small cell lung cancer population and its sensitive ancestor via the evolutionary game assay. We show that cost of resistance is insufficient to accurately predict competitive exclusion and that frequency-dependent growth rate measurements are required. Using frequency-dependent growth rate data, we then show that gefitinib treatment results in competitive exclusion of the ancestor, while the absence of treatment results in a likely, but not guaranteed, exclusion of the resistant strain. Then, using simulations, we demonstrate that incorporating ecological growth effects can influence the predicted extinction time. In addition, we show that higher drug concentrations may not lead to the optimal reduction in tumor burden. Together, these results highlight the potential importance of frequency-dependent growth rate data for understanding competing populations, both in the laboratory and as we translate adaptive therapy regimens to the clinic.

Stackelberg evolutionary game theory: how to manage evolving systems

“…Applications of game theory to understanding cancer and improving treatment have been summarized in a review paper [41] and in multiple publications on this topic [47][48][49][50]. To describe how the SEG theory can be useful in improving cancer treatment, let us consider an SEG of cancer treatment between a physician and a polymorphic population of cancer cells consisting of resistant and sensitive cells.…”

A survey of open questions in adaptive therapy: Bridging mathematics and clinical translation

“…First, how do we design experiments to assess the competitive suppression in a particular cancer and thus the scope for such patients to benefit from adaptive therapy? To date, experiments have employed one of three model systems, or combinations thereof: (i) 2-D in vitro cell culture (e.g., Silva et al, 2012 ; Bacevic et al, 2017 ; Farrokhian et al, 2022 ; Nam et al, 2021 ; Bondarenko et al, 2021 ), (ii) 3-D in vitro spheroids (e.g., Bacevic et al, 2017 ; Strobl et al, 2020 ; Bondarenko et al, 2021 ), and (iii) subcutaneous in vivo mouse models, in which human cells are injected into immunocompromised animals (e.g., Gatenby et al, 2009b ; Enriquez-Navas et al, 2016 ; Smalley et al, 2019 ; Wang et al, 2021b ; Wang et al, 2021a ). 2-D and 3-D in vitro models are inexpensive and quick, and allow for easy manipulation and monitoring of the ‘tumor.’ In contrast, by incorporating vasculature and stroma, orthotopic mouse models are more realistic, but they are expensive.…”

“…But even with more advanced experimental systems, limitations remain. To address these, we need to better understand what cells compete for (as discussed in section 1.2), and how we can best quantify this competition (e.g., the ‘game assay’; Kaznatcheev et al, 2019 ; Farrokhian et al, 2022 ). We propose that by playing out different scenarios in silico, mathematical models can help us to refine what experiments we should perform, and in what experimental system(s), in order to deduce the competitive landscape in tumors and in order to inform on how adaptive therapy will perform in patients.…”

Spatial cumulant models enable spatially informed treatment strategies and analysis of local interactions in cancer systems

“… 2015 ; Farrokhian et al. 2022 ), other ordinary differential equation (ODE) models (Hillen et al. 2013 ; Poleszczuk and Enderling 2018 ; Jenner et al.…”