Matrigel/serum-free, high-fidelity patient-derived tumor-like cell clusters as an in vitro platform for large-scale compound screening and chemoresistance prediction in oral cancer.

Authors

Yufeng Luo, Jiawen Li, Shang Xie, Shenyi Yin, Juan Li, Junxuan Zhao, Ying Sun, Renzhi Qian, Xinyu He, Feixiang Ge, Zhiqing Xiao, Yi Sui, Luming Wang, Hao Yu, Huinan Lu, Hanshuo Zhang, Buqing Ye, Xiaofeng Shan, Zhigang Cai, Jianzhong Jeff Xi

Published in

BMC medicine. Volume 24. Issue 1. Jun 22, 2026. Epub Jun 22, 2026.

Abstract

Current drug regimens for oral cancer are primarily based on generalized clinical guidelines, lacking a precision medicine strategy to tailor therapies to individual patients. This gap highlights the need for robust humanized models that can accurately reflect tumor characteristics and guide personalized treatment selection.
We developed patient-derived tumor-like cell clusters (PTCs) for oral cancer, an ex vivo model designed to preserve key tumor features including the immune microenvironment, phenotype, and genotype. Then, a prospective observational clinical validation study (ChiCTR2300075543) was conducted in 42 patients with advanced oral squamous cell carcinoma (OSCC) to evaluate the utility of PTC-guided sensitivity testing for the TPF neoadjuvant chemotherapy regimen (docetaxel, cisplatin, fluorouracil). Additionally, the PTC platform was utilized for high-throughput drug screening, and transcriptomic analysis was performed to identify potential predictive biomarkers.
PTCs were generated with a success rate exceeding 97% (161/165) using minimal tissue samples (≥ 10 mg). PTC-guided sensitivity testing for the TPF regimen showed ~90% (34/38) concordance with clinical outcomes in advanced OSCC patients. Moreover, the PTC platform successfully enabled high-throughput drug screening (> 100 compounds within two weeks), facilitating the identification of novel anti-cancer targets. Specifically, transcriptomic analysis revealed that upregulation of MMP13 is highly associated with treatment resistance (Pearson's r = 0.90, P < 0.01).
Our PTC-based platform robustly preserves critical tumor characteristics and demonstrates high concordance with clinical treatment outcomes, making it a valuable tool for selecting personalized therapies in oral cancer. Its capacity for large-scale compound screening also supports novel anti-cancer target discovery. Furthermore, MMP13 emerges as a potential predictive biomarker for treatment resistance. Collectively, this PTC platform represents a transformative precision medicine approach to advance personalized therapy and drug discovery in oral cancer.
ClinicalTrials.gov Identifier: ChiCTR2300075543; registered prospectively on September 7, 2023.

PMID:
42332684
Bibliographic data and abstract were imported from PubMed on 14 Oct 2026.

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