Applied Mathematics Division, Science Institute, University of Iceland, Reykjavík, Iceland.
School of Mathematics, University of Minnesota, Twin Cities, Minnesota, United States of America.
PLoS Comput Biol. 2024 Aug 2;20(8):e1012256. doi: 10.1371/journal.pcbi.1012256. eCollection 2024 Aug.
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.
患者来源的肿瘤类器官（PDTOs）是一种新型的细胞模型，能够保持患者肿瘤组织的遗传、表型和结构特征，可用于研究肿瘤发生和药物反应。当与先进的 3D 成像和分析技术相结合时，PDTOs 可用于建立具有生理相关性的高通量和高内涵药物筛选平台，支持制定针对患者的治疗策略。然而，为了有效地利用高通量 PDTO 观察结果进行临床预测，建立对类器官生长动力学的基本性质和可变性的定量理解至关重要。在这项工作中，我们通过将高通量成像深度学习平台与数学建模相结合，整合了灵活的生长规律和可变休眠时间，引入了一种分析和理解 PDTO 生长动力学的创新工作流程。我们将该工作流程应用于结肠癌类器官，并证明了类器官生长可以很好地用 Gompertz 生长模型来描述。我们的分析表明，PDTO 生长动力学存在显著的个体内异质性，每个数据集内的类器官初始指数生长率呈对数正态分布。个体内异质性的水平在患者之间有所不同，类器官的生长速率和单个种子细胞的休眠时间也有所不同。我们的工作有助于对 PDTO 基本生长特征的理解，并强调了类器官在个体内和个体间生长的异质性。这些结果为进一步的建模工作铺平了道路，旨在预测治疗反应动力学和耐药时间。
