Deben Christophe, Cardenas De La Hoz Edgar, Rodrigues Fortes Felicia, Le Compte Maxim, Seghers Sofie, Vanlanduit Steve, Vercammen Hendrik, Van Den Bogert Bert, Dusetti Nelson, Lin Abraham, Roeyen Geert, Peeters Marc, Prenen Hans, Lardon Filip, Smits Evelien
Center for Oncological Research (CORE), Integrated Personalized & Precision Oncology Network (IPPON), University of Antwerp, Wilrijk, Belgium.
Industrial Vision Lab, University of Antwerp, Wilrijk, Belgium.
Commun Biol. 2024 Dec 3;7(1):1612. doi: 10.1038/s42003-024-07329-5.
This study focuses on refining growth-rate-based drug response metrics for patient-derived tumor organoid screening using brightfield live-cell imaging. Traditional metrics like Normalized Growth Rate Inhibition (GR) and Normalized Drug Response (NDR) have been used to assess organoid responses to anticancer treatments but face limitations in accurately quantifying cytostatic and cytotoxic effects across varying growth rates. Here, we introduce the Normalized Organoid Growth Rate (NOGR) metric, specifically developed for brightfield imaging-based assays. A label-free image analysis model was applied to segment organoids precisely, track their growth rates over time, and classify viable and dead organoids. Testing eleven phenotypically distinct pancreatic cancer organoid models with five chemotherapeutics demonstrates that the NOGR metric more effectively captures cytostatic and cytotoxic drug effects compared to existing methods. This approach enhances the biological relevance of drug sensitivity assessments on organoids and offers a valuable tool for advancing personalized cancer treatment strategies.
本研究聚焦于利用明场活细胞成像技术，优化基于生长速率的药物反应指标，用于患者来源的肿瘤类器官筛选。传统指标如归一化生长速率抑制（GR）和归一化药物反应（NDR）已被用于评估类器官对抗癌治疗的反应，但在准确量化不同生长速率下的细胞生长抑制和细胞毒性作用方面存在局限性。在此，我们引入了归一化类器官生长速率（NOGR）指标，该指标是专门为基于明场成像的检测方法开发的。一个无标记图像分析模型被应用于精确分割类器官，跟踪其随时间的生长速率，并对存活和死亡的类器官进行分类。用五种化疗药物对十一种表型不同的胰腺癌类器官模型进行测试表明，与现有方法相比，NOGR指标能更有效地捕捉细胞生长抑制和细胞毒性药物效应。这种方法增强了类器官药物敏感性评估的生物学相关性，并为推进个性化癌症治疗策略提供了一个有价值的工具。
