Evidence map›Paper›PMID 42316214›Full record

ArticleJournal of translational medicine2026

Patient-specific lung cancer tumoroids recapitulate the tumor microenvironments for functional evaluation of therapeutic responses and immune-stromal interactions.

Yoo Ri Ko, Chae Won Park, Jihye Kim, Da Jung Jung, Sung Min Kim, Hye Seon Park, Bokyung Ahn, Hee Sang Hwang, Chang Ohk Sung, Se Jin Jang

Abstract read
In one paragraph

Article in Journal of translational medicine, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

10 authors.

Yoo Ri KoDepartment of Pathology, Asan Medical Center, University of Ulsan College of Medicine, 88, Olympic-ro 43-gil, Songpa-gu, Seoul, 05505, Republic of Korea.
Chae Won ParkDepartment of Pathology, Asan Medical Center, University of Ulsan College of Medicine, 88, Olympic-ro 43-gil, Songpa-gu, Seoul, 05505, Republic of Korea.
Jihye KimDepartment of Pathology, Asan Medical Center, University of Ulsan College of Medicine, 88, Olympic-ro 43-gil, Songpa-gu, Seoul, 05505, Republic of Korea.
Da Jung JungBiomedical Engineering Research Center, Asan Institute for Life Sciences, Seoul, Republic of Korea.
Sung Min KimDepartment of Pathology, Asan Medical Center, University of Ulsan College of Medicine, 88, Olympic-ro 43-gil, Songpa-gu, Seoul, 05505, Republic of Korea.
Hye Seon ParkDepartment of Pathology, Asan Medical Center, University of Ulsan College of Medicine, 88, Olympic-ro 43-gil, Songpa-gu, Seoul, 05505, Republic of Korea.
Bokyung AhnDepartment of Pathology, Asan Medical Center, University of Ulsan College of Medicine, 88, Olympic-ro 43-gil, Songpa-gu, Seoul, 05505, Republic of Korea.
Hee Sang HwangDepartment of Pathology, Asan Medical Center, University of Ulsan College of Medicine, 88, Olympic-ro 43-gil, Songpa-gu, Seoul, 05505, Republic of Korea.
Chang Ohk SungDepartment of Pathology, Asan Medical Center, University of Ulsan College of Medicine, 88, Olympic-ro 43-gil, Songpa-gu, Seoul, 05505, Republic of Korea.
Se Jin JangDepartment of Pathology, Asan Medical Center, University of Ulsan College of Medicine, 88, Olympic-ro 43-gil, Songpa-gu, Seoul, 05505, Republic of Korea. jangsejin@amc.seoul.kr.ORCID 0000-0001-8239-4362

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundTumor heterogeneity and the complexity of the tumor microenvironment (TME) drive therapeutic resistance in lung cancer, highlighting the critical need for experimental models that faithfully recapitulate tumor-stroma-immune interactions.

methodsWe developed patient-specific lung cancer tumoroid (LCT) models by integrating lung cancer organoids (LCOs), cancer-associated fibroblasts (CAFs) and immune cells isolated from single tumor tissue sources using a modular, transwell-based culture platform. The model supports cryopreservation and reproducible reconstruction of cellular components. Multimodal assessments, including histological characterization, dose-response pharmacologic profiling, immune profiling, and transcriptomic analyses, were performed across diverse co-culture configurations to evaluate patient-specific TME features and treatment-associated responses.

resultsThe LCT models successfully recapitulated key structural and cellular features of native TMEs, demonstrating high reproducibility across patient samples. Functional analyses revealed tumor-intrinsic heterogeneity in responses to chemotherapy and chemo-immunotherapy. CAF integration altered therapeutic responses and was associated with reduced immune activation and cytotoxic efficacy in selected models, suggesting stromal contributions to treatment resistance. Transcriptomic analyses showed that reconstructed TMEs preserved patient-specific stromal and immune programs associated with therapeutic responsiveness, including transcriptional features linked to clinical outcomes in independent immunotherapy-treated cohorts.

conclusionsThis patient-specific LCT model provides a scalable and translationally relevant approach for the ex vivo reconstruction of native TMEs, facilitating the functional interrogation of tumor-stroma-immune interactions. By capturing heterogeneous stromal responses, this platform offers a valuable tool for investigating therapeutic resistance and supports the further development of precision immune-oncology and patient-tailored therapeutic strategies in lung cancer.

Indexed as

Lung NeoplasmsOrganoidsStromal CellsTumor MicroenvironmentCancer-Associated FibroblastsCoculture TechniquesHumansImmunotherapyModels, BiologicalReproducibility of ResultsImmunotherapy modelingLung cancer organoidTranswellTumor microenvironmentTumoroid

Identifiers

PMID42316214
PMCPMC13540959

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.