Development of a rapid ex vivo tumor platform for drug response prediction in lung cancer
15 affiliations
- 1Department of Radiation Oncology (Maastro Clinic), GROW Research Institute for Oncology and Reproduction, Maastricht University Medical Center+, Maastricht, The Netherlands
- 2Department of Radiotherapy, Erasmus Medical Center Cancer Institute, University Medical Center Rotterdam, Rotterdam, The Netherlands
- 3Department of Pathology, GROW Research Institute for Oncology and Reproduction, Maastricht University Medical Center+, Maastricht, The Netherlands
- 4Department of Pathology and Clinical Bioinformatics, Erasmus University Medical Center, Rotterdam, The Netherlands
- 5Department of Pathology, Zuyderland Medical Center, Heerlen, The Netherlands
- 6Laboratory of Preclinical Investigation, Department of Translational Research, Institut Curie, PSL University Paris, Paris, France
- 7Department of Medical Oncology, Institut Curie, Paris, France
- 8Department of Clinical Genetics, Maastricht University Medical Center+, Maastricht, The Netherlands
- 9Department of Respiratory Medicine, Maastricht University Medical Center+, Maastricht, The Netherlands
- 10Department of Pulmonary Diseases, GROW Research Institute for Oncology and Reproduction, Maastricht University Medical Center+, Maastricht, the Netherlands
- 11Cardiothoracic Surgery, Cardiovascular Research Institute Maastricht (CARIM), Maastricht University, Maastricht, The Netherlands
- 12Department of Respiratory Medicine, Zuyderland Medical Center, Heerlen, The Netherlands
- 13Division of Thoracic Surgery, Department of Surgery, Zuyderland Medical Center, Heerlen, The Netherlands
- 14Department of Pulmonary Medicine, Erasmus MC Cancer Institute, University Medical Center, Rotterdam, The Netherlands
- 15Department of Molecular Genetics, Erasmus MC Cancer Institute, University Medical Center Rotterdam, Rotterdam, The Netherlands
Abstract
Despite advances in systemic therapies for patients with (locally) advanced stage lung cancer, response rates remain poor, underscoring the urgent need for predictive models to guide therapy selection. Patient-derived ex vivo models are promising, however, their clinical utility is restricted by the need for surgical biopsies, low establishment rates, and culture durations exceeding the clinically-relevant therapeutic decision-making window. Here, we developed and clinically validated a rapid, high-throughput ex vivo 3D tumor replica platform that enables functional drug testing from small diagnostic biopsies. In this prospective multicenter cohort study of 129 treatment-naïve lung cancer patients, tumor replicas were successfully established in 65% of biopsies. These cultures retained their original morphological, genetic, and immunophenotypic features. Ex vivo drug responses to chemotherapy and targeted agents were generated within a median of 12 days from biopsy acquisition. The ex vivo drug responses were in concordance with the treatment responses in patient-derived xenografts and lung cancer patients. In the clinical study, the ex vivo platform demonstrated a sensitivity of 73% and a positive predictive value of 92%. External validation confirmed the feasibility and reproducibility of the platform. In conclusion, this platform enables rapid patient-specific drug response assessment from routine biopsies within a clinically relevant time-frame.
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BibTeX
@article{vaes2026development,
title = {{Development of a rapid ex vivo tumor platform for drug response prediction in lung cancer}},
author = {Rianne D. W. Vaes and Zhongli Chen and Iris E. W. G. Laven and Shaowen Lyu and Tessa T. J. Welbers and Jolanda A. F. Piepers and Lisa M. Hillen and Jan H. von der Thüsen and Ruud Clarijs and Didier Decaudin and Elodie Montaudon and Marinus J. Blok and Roy T. M. Sprooten and Lizza E. L. Hendriks and Safiye Dursun and Jos G. Maessen and Juliette H. R. J. Degens and Michiel H. M. Gronenschild and Frank L. J. Custers and Erik R. de Loos and Anne-Marie C. Dingemans and Dik C. van Gent and Marc A. Vooijs and Dirk De Ruysscher},
journal = {NPJ Precision Oncology},
year = {2026},
month = jul,
doi = {10.1038/s41698-026-01598-5},
url = {https://doi.org/10.1038/s41698-026-01598-5},
note = {CC BY-NC-ND 4.0},
}
RIS
TY - JOUR
TI - Development of a rapid ex vivo tumor platform for drug response prediction in lung cancer
AU - Rianne D. W. Vaes
AU - Zhongli Chen
AU - Iris E. W. G. Laven
AU - Shaowen Lyu
AU - Tessa T. J. Welbers
AU - Jolanda A. F. Piepers
AU - Lisa M. Hillen
AU - Jan H. von der Thüsen
AU - Ruud Clarijs
AU - Didier Decaudin
AU - Elodie Montaudon
AU - Marinus J. Blok
AU - Roy T. M. Sprooten
AU - Lizza E. L. Hendriks
AU - Safiye Dursun
AU - Jos G. Maessen
AU - Juliette H. R. J. Degens
AU - Michiel H. M. Gronenschild
AU - Frank L. J. Custers
AU - Erik R. de Loos
AU - Anne-Marie C. Dingemans
AU - Dik C. van Gent
AU - Marc A. Vooijs
AU - Dirk De Ruysscher
PY - 2026
DA - 2026/07/01
JO - NPJ Precision Oncology
DO - 10.1038/s41698-026-01598-5
UR - https://doi.org/10.1038/s41698-026-01598-5
AB - Despite advances in systemic therapies for patients with (locally) advanced stage lung cancer, response rates remain poor, underscoring the urgent need for predictive models to guide therapy selection. Patient-derived ex vivo models are promising, however, their clinical utility is restricted by the need for surgical biopsies, low establishment rates, and culture durations exceeding the clinically-relevant therapeutic decision-making window. Here, we developed and clinically validated a rapid, high-throughput ex vivo 3D tumor replica platform that enables functional drug testing from small diagnostic biopsies. In this prospective multicenter cohort study of 129 treatment-naïve lung cancer patients, tumor replicas were successfully established in 65% of biopsies. These cultures retained their original morphological, genetic, and immunophenotypic features. Ex vivo drug responses to chemotherapy and targeted agents were generated within a median of 12 days from biopsy acquisition. The ex vivo drug responses were in concordance with the treatment responses in patient-derived xenografts and lung cancer patients. In the clinical study, the ex vivo platform demonstrated a sensitivity of 73% and a positive predictive value of 92%. External validation confirmed the feasibility and reproducibility of the platform. In conclusion, this platform enables rapid patient-specific drug response assessment from routine biopsies within a clinically relevant time-frame.
SN - 2397-768X
ER -