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Abstract Colorectal cancer, a significant cause of cancer-related mortality, often exhibits drug resistance, highlighting the need for improved tumor models to advance personalized drug testing and precision therapy. We generated organoids from primary colorectal cancer cells cultured through the conditional reprogramming technique, establishing a framework to perform short-term drug testing studies on patient-derived cells. To model interactions with stromal cells in the tumor microenvironment, we combined cancer cell organoids with carcinoma-associated fibroblasts, a cell type implicated in disease progression and drug resistance. Our organotypic models revealed that carcinoma-associated fibroblasts promote cancer cell proliferation and stemness primarily through hepatocyte growth factor–MET paracrine signaling and activation of cyclin-dependent kinases. Disrupting these tumor–stromal interactions reduced organoid size while limiting oncogenic signals and cancer stemness. Leveraging this tumor model, we identified effective drug combinations targeting colorectal cancer cells and their tumorigenic activities. Our study highlights a path to incorporate patient-derived cells and tumor–stromal interactions into a drug testing workflow that could identify effective therapies for individual patients.
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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.
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- Award ID(s):
- 2228034
- PAR ID:
- 10583974
- Editor(s):
- Wodarz, Dominik
- Publisher / Repository:
- PLoS Computational Biology
- Date Published:
- Journal Name:
- PLOS Computational Biology
- Volume:
- 20
- Issue:
- 8
- ISSN:
- 1553-7358
- Page Range / eLocation ID:
- e1012256
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1371/journal.pcbi.1012256
