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Updated: Aug 2, 2025
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Quantifying the drug response of patient-derived organoid clusters by aggregated morphological indicators with
Linyi Zhang1, Ling Wang1,2,3, Shanshan Yang1,2
1Hangzhou Dianzi University, Automation College, Hangzhou, Zhejiang, China.
Abstract:
Patient-derived organoids (PDOs) serve as excellent tools for personalized drug screening to predict clinical outcomes of cancer treatment. However, current methods for efficient quantification of drug response are limited. Herein, we develop a method for label-free, continuous tracking imaging and quantitative analysis of drug efficacy using PDOs. A self-developed optical coherence tomography (OCT) system was used to monitor the morphological changes of PDOs within 6 days of drug administration. OCT image acquisition was performed every 24 h. An analytical method for organoid segmentation and morphological quantification was developed based on a deep learning network (EGO-Net) to simultaneously analyze multiple morphological organoid parameters under the drug's effect. Adenosine triphosphate (ATP) testing was conducted on the last day of drug treatment. Finally, a corresponding aggregated morphological indicator (AMI) was established using principal component analysis (PCA) based on the correlation analysis between OCT morphological quantification and ATP testing. Determining the AMI of organoids allowed quantitative evaluation of the PDOs responses to gradient concentrations and combinations of drugs. Results showed that there was a strong correlation (correlation coefficient >90%) between the results using the AMI of organoids and those from ATP testing, which is the standard test used for bioactivity measurement. Compared with single-time-point morphological parameters, the introduction of time-dependent morphological parameters can reflect drug efficacy with improved accuracy. Additionally, the AMI of organoids was found to improve the efficiency of 5-fluorouracil(5FU) against tumor cells by allowing the determination of the optimum concentration, and the discrepancies in response among different PDOs using the same drug combinations could also be measured. Collectively, the AMI established by OCT system combined with PCA could quantify the multidimensional morphological changes of organoids under the drug's effect, providing a simple and efficient tool for drug screening in PDOs.
Insights
We developed a new method using optical coherence tomography (OCT) and deep learning to quantify drug responses in patient-derived organoids (PDOs). This approach accurately predicts treatment outcomes, offering an efficient tool for personalized cancer therapy drug screening.
Area of Science:
- Biomedical Engineering
- Cancer Research
- Medical Imaging
Background:
- Patient-derived organoids (PDOs) are valuable for personalized cancer drug screening.
- Current drug response quantification methods for PDOs are limited.
- Efficient and accurate methods are needed to predict clinical outcomes.
Purpose of the Study:
- To develop a label-free, continuous tracking imaging method for quantitative analysis of drug efficacy in PDOs.
- To establish a correlation between morphological changes and drug response.
- To create an efficient tool for personalized drug screening.
Main Methods:
- Utilized a self-developed optical coherence tomography (OCT) system for label-free, continuous imaging of PDOs over 6 days.
- Developed a deep learning network (EGO-Net) for organoid segmentation and morphological quantification.
- Established an aggregated morphological indicator (AMI) using principal component analysis (PCA) correlated with adenosine triphosphate (ATP) testing.
Main Results:
- The AMI showed a strong correlation (>90%) with standard ATP testing for drug bioactivity.
- Time-dependent morphological parameters improved the accuracy of drug efficacy assessment.
- The AMI method efficiently determined optimal drug concentrations and measured response variations among different PDOs.
Conclusions:
- The developed OCT-based AMI method provides a simple and efficient tool for quantifying multidimensional morphological changes in PDOs under drug treatment.
- This method enhances the accuracy and efficiency of drug screening for personalized cancer therapy.
- The AMI facilitates quantitative evaluation of PDO responses to various drug concentrations and combinations.
