Abstract A023: Integrating mathematical modeling and in vitro experiments to measure ecological interactions in cancer
Authors: Strobl Maximilian A. R. , Tadele Dagim S. , Maltas Jeffrey , Barker-Clarke Rowan , Dinh Mina , Scott Jacob 
Published: February 2024

Abstract
Integrating mathematical modeling and in vitro experiments to measure ecological interactions in cancer Maximilian Strobl, Dagim Tadele, Jeffrey Maltas, Rowan Barker-Clarke, Mina Dinh, Jacob Scott Over the past two decades it has become clear that tumours are complex and evolving ecosystems, in which different cancer cell populations interact with each other and with their non-tumour microenvironment. This understanding has given rise to the intriguing idea that we might be able to leverage these interactions to slow, or even revert, the expansion of resistant cells. In so-called “Adaptive Therapy” (AT) treatment is dynamically reduced to maintain the tumour burden tolerable whilst also preserving a pool of drug-sensitive cells which competitively suppress resistant cells. However, how strong and how common is this suppression, and how do we stratify patients who might benefit from AT from those who should receive standard-of-care continuous therapy? In this contribution, we present development of methodology and initial experimental results aimed at measuring the distribution of ecological interactions between drug-sensitive and resistant cells. Previously, we pioneered an assay with which to quantify ecological interactions in cancer in vitro. In the “Game Assay”, the two populations are co-cultured at a range of initial ratios in 2-D, and we record how the growth rate of each population is modulated by the relative abundance of the other. In the current study, we begin by using an agent-based mathematical model to characterize the accuracy and precision of this assay and how we may improve these by modifying different aspects of the experimental design (number of replicates, number of proportions) and analysis (regression technique, regression window). This shows that the choice of seeding ratios is important in being able to reliably identify interactions and we discuss guidelines for assay design. Upon identifying an interaction, a key next question is the underlying mechanism. Next, we thus use the model to investigate the degree to which we can use the assay to distinguish between local (e.g. contact inhibition) and non-local interactions (e.g. paracrine signaling). To conclude, we present preliminary experimental data in which we have used our optimized protocol to quantify the distribution of interactions between Osimertinib sensitive and resistant NSCLC cells (PC9). Our data show that while suppressive interactions do occur, they vary in their strength between different resistant lineages and can be attenuated in a frequency-dependent manner. Overall, we demonstrate how mathematical models can help in the design and interpretation of experimental assays and we contribute towards a more systematic and quantitative understanding of tumor ecology as a foundation for adaptive therapy.
Citation Format: Maximilian A. R. Strobl, Dagim S. Tadele, Jeffrey Maltas, Rowan Barker-Clarke, Mina Dinh, Jacob Scott. Integrating mathematical modeling and in vitro experiments to measure ecological interactions in cancer [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Translating Cancer Evolution and Data Science: The Next Frontier; 2023 Dec 3-6; Boston, Massachusetts. Philadelphia (PA): AACR; Cancer Res 2024;84(3 Suppl_2):Abstract nr A023.