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Multiparametric Tumor Organoid Drug Screening Using Widefield Live-Cell Imaging for Bulk and Single-Organoid Analysis
Published on: December 23, 2022
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Understanding patient-derived tumor organoid growth through an integrated imaging and mathematical modeling framework
Einar Bjarki Gunnarsson1,2, Seungil Kim3, Brandon Choi3,4
1Applied Mathematics Division, Science Institute, University of Iceland, Reykjavík, Iceland.
Plos Computational Biology
|August 2, 2024
Summary
Patient-derived tumor organoids (PDTOs) reveal significant growth differences within and between patients. Understanding this heterogeneity is key for developing personalized cancer treatments and predicting drug resistance.
Area of Science:
- Oncology
- Biotechnology
- Computational Biology
Background:
- Patient-derived tumor organoids (PDTOs) are valuable models for studying cancer and drug response.
- PDTOs preserve patient tumor characteristics, enabling personalized treatment strategy development.
- Quantitative understanding of PDTO growth dynamics is crucial for clinical predictions.
Purpose of the Study:
- To develop and apply an innovative workflow for analyzing PDTO growth dynamics.
- To integrate high-throughput imaging, deep learning, and mathematical modeling for organoid analysis.
- To investigate intrapatient heterogeneity in colon cancer organoid growth.
Main Methods:
- Developed a workflow combining deep learning imaging with mathematical modeling.
- Incorporated flexible growth laws and variable dormancy times into the models.
- Applied the workflow to colon cancer PDTOs to analyze growth dynamics.
Main Results:
- Organoid growth was accurately described by the Gompertz model.
- Significant intrapatient heterogeneity in PDTO growth rates was observed, following a lognormal distribution.
- Heterogeneity levels, growth rates, and dormancy times varied considerably between patients.
Conclusions:
- PDTO growth dynamics exhibit significant heterogeneity both within and between patients.
- This heterogeneity impacts the potential for predicting treatment response and drug resistance timing.
- The findings advance the understanding of PDTO growth characteristics for future modeling efforts.
