Understanding patient-derived tumor organoid growth through an integrated imaging and mathematical modeling framework

Patient-derived tumor organoids (PDTOs) are novel cellular models that maintain the genetic, phenotypic and structural features of patient tumor tissue and are useful for studying tumorigenesis and drug response. When integrated with advanced 3D imaging and analysis techniques, PDTOs can be used to establish physiologically relevant high-throughput and high-content drug screening platforms that support the development of patient-specific treatment strategies. However, in order to effectively leverage high-throughput PDTO observations for clinical predictions, it is critical to establish a quantitative understanding of the basic properties and variability of organoid growth dynamics. In this work, we introduced an innovative workflow for analyzing and understanding PDTO growth dynamics, by integrating a high-throughput imaging deep learning platform with mathematical modeling, incorporating flexible growth laws and variable dormancy times. We applied the workflow to colon cancer organoids and demonstrated that organoid growth is well-described by the Gompertz model of growth. Our analysis showed significant intrapatient heterogeneity in PDTO growth dynamics, with the initial exponential growth rate of an organoid following a lognormal distribution within each dataset. The level of intrapatient heterogeneity varied between patients, as did organoid growth rates and dormancy times of single seeded cells. Our work contributes to an emerging understanding of the basic growth characteristics of PDTOs, and it highlights the heterogeneity in organoid growth both within and between patients. These results pave the way for further modeling efforts aimed at predicting treatment response dynamics and drug resistance timing.Author summary: Patient-derived tumor organoids (PDTOs) are miniaturized models of tumors, developed using a patient’s own tumor tissue, which can be grown outside of the body in a laboratory setting. PDTOs enable researchers to better understand tumor biology and to model how an individual’s tumor may respond to various cancer treatments. In this work, by integrating PDTOs with dynamic imaging and mathematical modeling, we develop a method for investigating the fundamental laws of tumor organoid growth on a patient-by-patient basis (S1 Fig). We identify a simple mathematical model which applies to the growth of PDTOs derived from three different patients, and we quantify variability in organoid growth both within and between patients. Our work is ultimately motivated by the potential of combining mathematical modeling with drug screening data for personalized treatment optimization.