ArticleJournal of experimental & clinical cancer research : CR2023
Cancer organoid-based diagnosis reactivity prediction (CODRP) index-based anticancer drug sensitivity test in ALK-rearrangement positive non-small cell lung cancer (NSCLC).
Article in Journal of experimental & clinical cancer research : CR, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 14 papers.
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14 citing papers in PubMed, 18 citations in OpenAlex.
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- Application of organoid models in basic and translational research of lung cancer: a narrative review.Translational lung cancer research · 2026Review
- Organoids in cancer therapy: translational applications and clinical promise.Molecular cancer · 2026Review
- Patient-Derived Lung Cancer Organoids for Precision Oncology: A Comprehensive Analysis.JTO clinical and research reports · 2026Article
- Organoid models in oncology: advancing precision cancer therapy and vaccine development.Cancer biology & medicine · 2025Review
- Cryopreservation of human lung tissue for 3D ex vivo analysis.Respiratory research · 2025Review
- Lung cancer organoids: a new strategy for precision medicine research.Translational lung cancer research · 2025Review
- [Prospects of Smoking Hazards Research Using Lung Organoid].Jugan geon-gang gwa jilbyeong · 2025Review
- Progress and prospects of organoids in the pathogenesis of lung cancer and screening of antitumor drugs.Frontiers in oncology · 2025Review
- Lung cancer organoid-based drug evaluation models and new drug development application trends.Translational lung cancer research · 2024Review
- Advancing cancer research through organoid technology.Journal of translational medicine · 2024Review
- From Cancer to Immune Organoids: Innovative Preclinical Models to Dissect the Crosstalk between Cancer Cells and the Tumor Microenvironment.International journal of molecular sciences · 2024Review
- Correction: Cancer organoid-based diagnosis reactivity prediction (CODRP) index-based anticancer drug sensitivity test in ALK-rearrangement positive non-small cell lung cancer (NSCLC).Journal of experimental & clinical cancer research : CR · 2023Article
Corrections and comments
- Erratum issued
Authors and funding
23 authors at 5 institutions in 1 country.
Funding
Abstract
backgroundRecently, cancer organoid-based drug sensitivity tests have been studied to predict patient responses to anticancer drugs. The area under curve (AUC) or IC
methodsOn the CODRP platform, patient-derived organoids (PDOs) that recapitulate patients with lung cancer were implemented by applying a mechanical dissociation method capable of high yields and proliferation rates. A disposable nozzle-type cell spotter with efficient high-throughput screening (HTS) has also been developed to dispense a very small number of cells due to limited patient cells. A drug sensitivity test was performed using PDO from the patient tissue and the primary cancer characteristics of PDOs were confirmed by pathological comparision with tissue slides.
resultsThe conventional index of drug sensitivity is the AUC of the DRC. In this study, the CODRP index for drug sensitivity test was proposed through multi-parameter analyses considering cancer cell proliferation rate, the cancer diagnosis stage, and AUC values. We tested PDOs from eight patients with lung cancer to verify the CODRP index. According to the anaplastic lymphoma kinase (ALK) rearrangement status, the conventional AUC index for the three ALK-targeted drugs (crizotinib, alectinib, and brigatinib) did not classify into sensitive and resistant groups. The proposed CODRP index-based drug sensitivity test classified ALK-targeted drug responses according to ALK rearrangement status and was verified to be consistent with the clinical drug treatment response.
conclusionsTherefore, the PDO-based HTS and CODRP index drug sensitivity tests described in this paper may be useful for predicting and analyzing promising anticancer drug efficacy for patients with lung cancer and can be applied to a precision medicine platform.
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