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ORIGINAL RESEARCH article

Front. Cell Dev. Biol.

Sec. Cancer Cell Biology

Volume 13 - 2025 | doi: 10.3389/fcell.2025.1639922

This article is part of the Research Topic3D Models in Cancer Research: Bridging Tumor Biology and Personalized MedicineView all 12 articles

Tumor organoids may be more suitable for clinical personalized chemotherapeutic drug screening in lung adenocarcinoma

Provisionally accepted
Wuyang  YunWuyang Yun1Yuyu  LiYuyu Li2Yanlei  GeYanlei Ge3Xiaoyun  ZhangXiaoyun Zhang1Huifeng  LiuHuifeng Liu4*Wen  ChenWen Chen2*Li  XiaoLi Xiao5*
  • 1Hebei North University, Zhangjiakou, China
  • 2Department of Pathology, the 8th Medical Center of PLA General Hospital, Beijing, China
  • 3North China University of Science and Technology Affiliated Hospital, Tangshan, China
  • 4Department of Respiratory and Critical Care Medicine, The 8th Medical Center of PLA General Hospital, Beijing, China
  • 5College of Pulmonary and Critical Care Medicine, Beijing Key Laboratory of Organ Transplantation and Immunology Regulatory, the 8th Medical Centre of Chinese PLA General Hospital, Beijing, China

The final, formatted version of the article will be published soon.

Objective: The formulation of precision treatment strategies and the analysis of drug-resistance mechanisms for lung adenocarcinoma are highly dependent on in vitro models that can faithfully reflect tumor heterogeneity, dynamic drug responses, and tumor-stroma interactions. While existing preclinical models, such as two-dimensional (2D) adherent models and animal models, are widely used, their limitations in accurately recapitulating patient-specific microenvironments and the evolution of drug-resistant clones under chemotherapeutic pressure significantly restrict the reliability of treatment predictions. Methods The study utilized a three-dimensional (3D) organoid model, a 2D adherent model, and an animal model constructed from the A549 cell line to dynamically monitor drug responses to chemotherapeutic treatments. We analyzed cell cycle arrest, proliferation inhibition, and the invasive regulatory features mediated by the human epidermal growth factor receptor 2(HER-2) mediated invasive regulatory features. The evolution of the resistance mutation spectrum was tracked through dynamic gene sequencing and compared with clinical resistance samples. Comparisons between two groups were performed using t-tests, while comparisons involving three or more groups were conducted using one-way analysis of variance (ANOVA). Results In studies of four chemotherapy regimens (etoposide, paclitaxel, cisplatin, and carboplatin), organoid models showed a pharmacodynamic profile highly consistent with animal models. For drug-induced cell cycle block, the organoid model accurately replicated the animal model's G2/M phase block. Analysis showed similar in vitro IC50 values for etoposide and carboplatin. Their tumor suppression rates in animal models also didn't differ significantly (P > 0.05). The organoid model matched the animal model for Ki-67-mediated proliferation dynamics, HER2-mediated invasive phenotype, and early apoptosis (P > 0.05). Drug resistance analysis confirmed that Epidermal Growth Factor Receptor (EGFR)/HER2 mutations in the organoid model closely matched clinical resistance samples. Conclusion The lung adenocarcinoma organoid model accurately simulates drug sensitivity and the evolution of drug resistance, providing a highly predictive in vitro platform for optimizing individualized chemotherapy regimens. This model is anticipated to reduce the costs associated with trial-and-error in clinical settings and to advance the development of precision tumor therapies.

Keywords: lung cancer, Organoid model, chemotherapy response, Resistance evolution, ClinicalPrediction, precision oncology

Received: 03 Jun 2025; Accepted: 22 Sep 2025.

Copyright: Ā© 2025 Yun, Li, Ge, Zhang, Liu, Chen and Xiao. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

* Correspondence:
Huifeng Liu, liuhf309@sina.com
Wen Chen, dr.chen20160224@foxmail.com
Li Xiao, xiaolilab309@163.com

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