ArticleJournal of translational medicine2024
CRISPR/Cas9-edited ROS1 + non-small cell lung cancer cell lines highlight differential drug sensitivity in 2D vs 3D cultures while reflecting established resistance profiles.
Article in Journal of translational medicine, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers, 1 of them a synthesis that pooled it.
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Who cites it
8 citing papers in PubMed, 1 synthesis or guideline pooled it, 9 citations in OpenAlex.
- Unlocking the Potential of Spheroids in Personalized Medicine: A Systematic Review of Seeding Methodologies.International journal of molecular sciences · 2025Pooled it
- In vitro and in silico modelling of ROS1-positive non-small cell lung cancer reveals fusion-dependent tyrosine kinase inhibitor responses.Molecular oncology · 2026Article
- An evaluation of taletrectinib for the treatment of ROS1+ non-small cell lung cancer.Expert review of anticancer therapy · 2026Review
- Dose-dependent induction of epithelial-mesenchymal transition in 3D melanoma models by non-thermal plasma treatment.Molecular oncology · 2025Article
- The influence of ROS1 fusion partners and resistance mechanisms in ROS1-TKI-treated non-small cell lung cancer patients.Molecular oncology · 2025Article
- The oncogenic role of SPOCK1 in lung carcinoma by promoting immune evasion and its related mechanisms.Cytotechnology · 2025Article
- Advancements in Gene Therapy for Non-Small Cell Lung Cancer: Current Approaches and Future Prospects.Genes · 2025Review
- 3D cell culture models in research: applications to lung cancer pharmacology.Frontiers in pharmacology · 2024Review
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Authors and funding
7 authors at 1 institution in 1 country.
Funding
Abstract
introductionThe study of resistance-causing mutations in oncogene-driven tumors is fundamental to guide clinical decisions. Several point mutations affecting the ROS1 kinase domain have been identified in the clinical setting, but their impact requires further exploration, particularly in improved pre-clinical models. Given the scarcity of solid pre-clinical models to approach rare cancer subtypes like ROS1 + NSCLC, CRISPR/Cas9 technology allows the introduction of mutations in patient-derived cell lines for which resistant variants are difficult to obtain due to the low prevalence of cases within the clinical setting.
methodsIn the SLC34A2-ROS1 rearranged NSCLC cell line HCC78, we knocked-in through CRISPR/Cas9 technology three ROS1 drug resistance-causing mutations: G2032R, L2026M and S1986Y. Such variants are located in different functional regions of the ROS1 kinase domain, thus conferring TKI resistance through distinct mechanisms. We then performed pharmacological assays in 2D and 3D to assess the cellular response of the mutant lines to crizotinib, entrectinib, lorlatinib, repotrectinib and ceritinib. In addition, immunoblotting assays were performed in 2D-treated cell lines to determine ROS1 phosphorylation and MAP kinase pathway activity. The area over the curve (AOC) defined by the normalized growth rate (NGR_fit) dose-response curves was the variable used to quantify the cellular response towards TKIs.
resultsSpheroids derived from ROS1
conclusionIn this study we knock-in for the first time in a ROS1 + patient-derived cell line, three different known resistance-causing mutations using CRISPR/Cas9 in the endogenous translocated ROS1 alleles. Pharmacological assays performed in 2D and 3D cell culture revealed that spheroids are more sensitive to TKIs than cells cultured as a monolayer. This direct comparison between two culture systems could be done thanks to the implementation of normalized growth rates (NGR) to uniformly quantify drug response between 2D and 3D cell culture. Overall, this study presents the added value of using spheroids and positions lorlatinib and repotrectinib as the most effective TKIs against the studied ROS1 resistance point mutations.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.