Development of a rapid ex vivo tumor platform for drug response prediction in lung cancer.

Authors

Rianne D W Vaes, Zhongli Chen, Iris E W G Laven, Shaowen Lyu, Tessa T J Welbers, Jolanda A F Piepers, Lisa M Hillen, Jan H von der Thüsen, Ruud Clarijs, Didier Decaudin, Elodie Montaudon, Marinus J Blok, Roy T M Sprooten, Lizza E L Hendriks, Safiye Dursun, Jos G Maessen, Juliette H R J Degens, Michiel H M Gronenschild, Frank L J Custers, Erik R de Loos, Anne-Marie C Dingemans, Dik C van Gent, Marc A Vooijs, Dirk De Ruysscher

Published in

NPJ precision oncology. Jul 09, 2026. Epub Jul 09, 2026.

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.

PMID:
42426227
Bibliographic data and abstract were imported from PubMed on 10 Jul 2026.

Read full publication at:
Please sign in to see all details.

Stats

1-terrible, 9-excellent. How would you rate this publication? Sign in in to submit your rating.

Post a comment

You need to be signed in to post comments. You can sign in here.

Comments

There are no comments yet.