Cutoff value of IC50 for drug sensitivity in patient-derived tumor organoids in colorectal cancer

Patient-derived tumor organoids (PDTOs) have the potential to be used to predict the patient response to chemotherapy. However, the cutoff value of the half-maximal inhibition concentration (IC50) for PDTO drug sensitivity has not been validated with clinical cohort data. We established PDTOs and pe...

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Published in:iScience Vol. 26; no. 7; p. 107116
Main Authors: Tang, Yuting, Wang, Ting, Hu, Yaowen, Ji, Hongli, Yan, Botao, Hu, Xiarong, Zeng, Yunli, Hao, Yifan, Xue, Weisong, Chen, Zexin, Lan, Jianqiang, Wang, Yanan, Deng, Haijun, Deng, Chuxia, Wu, Xiufeng, Yan, Jun
Format: Journal Article
Language:English
Published: Elsevier Inc 21-07-2023
Elsevier
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Summary:Patient-derived tumor organoids (PDTOs) have the potential to be used to predict the patient response to chemotherapy. However, the cutoff value of the half-maximal inhibition concentration (IC50) for PDTO drug sensitivity has not been validated with clinical cohort data. We established PDTOs and performed a drug test in 277 samples from 242 CRC patients who received FOLFOX or XELOX chemotherapy. After follow-up and comparison of the PDTO drug test and final clinical outcome results, the optimal IC50 cutoff value for PDTO drug sensitivity was 43.26 μmol/L. This PDTO drug test-defined cutoff value could predict patient response with 75.36% sensitivity, 74.68% specificity, and 75% accuracy. Moreover, this value distinguished groups of patients with significant differences in survival benefit. Our study is the first to define the IC50 cutoff value for the PDTO drug test to effectively distinguish CRC patients with chemosensitivity or nonsensitivity and predict survival benefits. [Display omitted] •Generation of PDTOs of CRC with different lesions and biological characteristics•Definition of the IC50 cutoff value for PDTO drug sensitivity based on clinical responses•The defined cutoff value can predict the chemotherapy efficacy and prognosis Cancer; Drugs; Molecular modelling
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ISSN:2589-0042
2589-0042
DOI:10.1016/j.isci.2023.107116