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

## TL;DR

This study shows that lung cancer organoid models accurately predict drug responses and resistance, making them useful for personalized chemotherapy.

## Contribution

The study demonstrates that 3D organoid models better simulate clinical drug responses and resistance evolution compared to traditional models.

## Key findings

- Organoid models showed pharmacodynamic profiles consistent with animal models for chemotherapy regimens.
- Organoid models accurately replicated cell cycle blocks and proliferation dynamics seen in animal models.
- Drug resistance mutations in organoid models closely matched clinical resistance samples.

## Abstract

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.

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).

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.

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, Clinical Prediction, Precision Oncology.