Evidence map›Paper›PMID 42102353›Full record

ArticleAdvanced science (Weinheim, Baden-Wurttemberg, Germany)2026

Bioprinted Tumor Microenvironment Models Reveal Immune Evasion and Guide CAR-NK Therapeutic Strategies.

Dahong Kim, Seona Jo, In-Hwan Jang, Yu-Jin Kim, Youngmee Jung, Junhyoung Ahn, Hyungjun Lim, Jae Jong Lee, Kangwon Lee, Tae-Don Kim and 1 more

Abstract read
In one paragraph

Article in Advanced science (Weinheim, Baden-Wurttemberg, Germany), 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing 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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

11 authors.

Dahong KimNano Lithography & Manufacturing Research Center, Nano-Convergence Manufacturing Research Division, Korea Institute of Machinery and Materials (KIMM), Daejeon, Republic of Korea.
Seona JoCenter for Gene and Cell Therapy, Korea Research Institute of Bioscience and Biotechnology (KRIBB), Daejeon, Republic of Korea.
In-Hwan JangCenter for Gene and Cell Therapy, Korea Research Institute of Bioscience and Biotechnology (KRIBB), Daejeon, Republic of Korea.
Yu-Jin KimCenter for Biomaterials, Korea Institute of Science and Technology, Seoul, Republic of Korea.
Youngmee JungCenter for Biomaterials, Korea Institute of Science and Technology, Seoul, Republic of Korea.
Junhyoung AhnNano Lithography & Manufacturing Research Center, Nano-Convergence Manufacturing Research Division, Korea Institute of Machinery and Materials (KIMM), Daejeon, Republic of Korea.
Hyungjun LimNano Lithography & Manufacturing Research Center, Nano-Convergence Manufacturing Research Division, Korea Institute of Machinery and Materials (KIMM), Daejeon, Republic of Korea.
Jae Jong LeeNano Lithography & Manufacturing Research Center, Nano-Convergence Manufacturing Research Division, Korea Institute of Machinery and Materials (KIMM), Daejeon, Republic of Korea.
Kangwon LeeDepartment of Applied Bioengineering, Graduate School of Convergence Science and Technology, Seoul National University, Seoul, Republic of Korea.
Tae-Don KimCenter for Gene and Cell Therapy, Korea Research Institute of Bioscience and Biotechnology (KRIBB), Daejeon, Republic of Korea.
Su A ParkNano Lithography & Manufacturing Research Center, Nano-Convergence Manufacturing Research Division, Korea Institute of Machinery and Materials (KIMM), Daejeon, Republic of Korea.ORCID https://orcid.org/0000-0001-5878-8054

Funding

Ministry of Food and Drug Safety 20202MFDS002National Research Council of Science and Technology CRC22021-200National Research Council of Science and Technology GTL24021-000National Research Foundation 2710004815National Research Foundation RS-2024-00411892Technology Innovation Program, Ministry of Trade, Industry & Energy RS-2024-00403563
6 · The paper itself

Abstract

The clinical outcome predictions of conventional in vitro and in vivo models are often inaccurate because they cannot replicate the tumor microenvironment (TME) complexity. Existing 3D models encounter challenges regarding TME complexity replication, engineering constraints, and limited capacity in analyzing immune-cancer interactions. This study employs 3D embedded bioprinting to develop a heterogeneous lung spheroid (HLS) model, incorporating key stromal factors to better reflect the TME. Transcriptomic profiling via RNA sequencing reveals gene signatures associated with extracellular matrix remodeling, immune suppression, and tumor progression, demonstrating substantial similarity to patient-derived lung tumor samples and validating the biological fidelity of the model. Functional assays demonstrate that the model effectively replicated TME dynamics, as evidenced by reduced CAR-NK cell infiltration, cytotoxicity, and cytokine secretion with increasing model complexity, indicative of a highly immunosuppressive environment. Advanced CAR-NK cells expressing chemokine receptors are utilized to overcome this immune barrier and enhance migration and infiltration within the physiologically relevant lung TME model. Overall, this model replicates critical features of the lung TME, showing potential for evaluating next-generation immunotherapies targeting complex solid tumors.

Indexed as

BioprintingImmunotherapy, AdoptiveKiller Cells, NaturalLung NeoplasmsReceptors, Chimeric AntigenTumor MicroenvironmentHumansReceptors, Chimeric Antigen3D bioprintingCAR‐NK cell therapyimmune‐tumor interactionimmunosuppressionimmunotherapytumor microenvironment (TME)

Identifiers

PMID42102353
PMCPMC13317579

What OpenQuestion holds

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LicenceCC BY
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Registered trials

None linked

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.