Evidence map›Paper›PMID 42410050›Full record

ArticleNature biomedical engineering2026

Identifying and reprogramming softness-driven cancer stem-like cells overcomes CAR-T cell resistance in solid tumours.

Yunjia Qu, Yuxuan Wang, Linshan Zhu, Fengyi Ma, Chi Woo Yoon, Xinyu Chen, Junhang Zhang, Carrie Bishop, Min Tang, Jiaxin Cui and 10 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Nature biomedical engineering, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Mechanical regulation of cell memory.Nature structural & molecular biology · 2026
    Review
  2. 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

20 authors.

Yunjia QuAlfred E. Mann Department of Biomedical Engineering, Department of Quantitative Computational Biology, Department of Stem Cell and Regenerative Medicine, University of Southern California, Los Angeles, CA, USA.
Yuxuan WangAlfred E. Mann Department of Biomedical Engineering, Department of Quantitative Computational Biology, Department of Stem Cell and Regenerative Medicine, University of Southern California, Los Angeles, CA, USA.
Linshan ZhuAlfred E. Mann Department of Biomedical Engineering, Department of Quantitative Computational Biology, Department of Stem Cell and Regenerative Medicine, University of Southern California, Los Angeles, CA, USA.
Fengyi MaAlfred E. Mann Department of Biomedical Engineering, Department of Quantitative Computational Biology, Department of Stem Cell and Regenerative Medicine, University of Southern California, Los Angeles, CA, USA.
Chi Woo YoonAlfred E. Mann Department of Biomedical Engineering, Department of Quantitative Computational Biology, Department of Stem Cell and Regenerative Medicine, University of Southern California, Los Angeles, CA, USA.
Xinyu ChenDepartment of Electrical and Computer Engineering, University of California San Diego, La Jolla, CA, USA.
Junhang ZhangAlfred E. Mann Department of Biomedical Engineering, Department of Quantitative Computational Biology, Department of Stem Cell and Regenerative Medicine, University of Southern California, Los Angeles, CA, USA.ORCID http://orcid.org/0000-0002-7847-3102
Carrie BishopShu Chien - Gene Lay Department of Bioengineering, Institute of Engineering in Medicine, University of California San Diego, La Jolla, CA, USA.ORCID http://orcid.org/0000-0001-5278-1601
Min TangDepartment of NanoEngineering, University of California San Diego, La Jolla, CA, USA.
Jiaxin CuiAlfred E. Mann Department of Biomedical Engineering, Department of Quantitative Computational Biology, Department of Stem Cell and Regenerative Medicine, University of Southern California, Los Angeles, CA, USA.ORCID http://orcid.org/0000-0001-8318-0008
Tianze GuoAlfred E. Mann Department of Biomedical Engineering, Department of Quantitative Computational Biology, Department of Stem Cell and Regenerative Medicine, University of Southern California, Los Angeles, CA, USA.
Phuong HoAlfred E. Mann Department of Biomedical Engineering, Department of Quantitative Computational Biology, Department of Stem Cell and Regenerative Medicine, University of Southern California, Los Angeles, CA, USA.
W Martin KastDepartment of Molecular Microbiology and Immunology, University of Southern California, Los Angeles, CA, USA.
Hongquan XuDepartment of Statistics and Data Science, University of California Los Angeles, Los Angeles, CA, USA.
Qifa ZhouAlfred E. Mann Department of Biomedical Engineering, Department of Quantitative Computational Biology, Department of Stem Cell and Regenerative Medicine, University of Southern California, Los Angeles, CA, USA.ORCID http://orcid.org/0000-0003-1527-3020
Adam J EnglerShu Chien - Gene Lay Department of Bioengineering, Institute of Engineering in Medicine, University of California San Diego, La Jolla, CA, USA.ORCID http://orcid.org/0000-0003-1642-5380
Ning WangInstitute for Mechanobiology, Department of Bioengineering in College of Engineering, Northeastern University, Boston, MA, USA.ORCID http://orcid.org/0000-0001-7531-7385
Longwei LiuAlfred E. Mann Department of Biomedical Engineering, Department of Quantitative Computational Biology, Department of Stem Cell and Regenerative Medicine, University of Southern California, Los Angeles, CA, USA. longweil@usc.edu.ORCID http://orcid.org/0000-0003-4229-9066
Shu ChienShu Chien - Gene Lay Department of Bioengineering, Institute of Engineering in Medicine, University of California San Diego, La Jolla, CA, USA. shuchien@ucsd.edu.ORCID http://orcid.org/0000-0003-0332-285X
Yingxiao WangAlfred E. Mann Department of Biomedical Engineering, Department of Quantitative Computational Biology, Department of Stem Cell and Regenerative Medicine, University of Southern California, Los Angeles, CA, USA. ywang283@usc.edu.ORCID http://orcid.org/0000-0003-0265-326X

Funding

Systems Biology Analyses for Hemodynamic Regulation of Vascular HomeostasisR01HL108735 · NHLBI · UNIVERSITY OF CALIFORNIA, SAN DIEGO · PI CHIEN, SHU, SHYY, JOHN YJ · 2012 to 2024
$13.4M
Bioengineering to map stress propagation in cytoskeletonR01GM072744 · NIGMS · UNIVERSITY OF ILLINOIS URBANA-CHAMPAIGN · PI Ning Wang · 2005 to 2026
$7.6M
Role of Spatiotemporal Epigenetic Dynamics in Regulating Endothelial Gene Expressions under FlowsR01HL121365 · NHLBI · UNIVERSITY OF SOUTHERN CALIFORNIA · PI CHIEN, SHU, WANG, YINGXIAO · 2014 to 2025
$7.5M
Mechanisms of vascular pathology from abnormal protein kinase G signalingR01HL132141 · NHLBI · UNIVERSITY OF CALIFORNIA, SAN DIEGO · PI PILZ, RENATE B · 2016 to 2025
$4.5M
Ultrasensitive kinase biosensors for multiplex imaging of coordinated spatiotemporal signaling in cancer-immune interactionsR01CA262815 · NCI · UNIVERSITY OF CALIFORNIA, SAN DIEGO · PI Yingxiao Wang, Jin Zhang · 2022 to 2026
$3.3M
Single Cell Tracking of 3D Epigenetic Landscape Evolution During Embryonic DevelopmentR01HD107206 · NICHD · UNIVERSITY OF CALIFORNIA, SAN DIEGO · PI Yingxiao Wang, Sheng Zhong · 2022 to 2026
$3.2M
Acoustothermogenetics for Cell EngineeringR35GM140929 · NIGMS · UNIVERSITY OF SOUTHERN CALIFORNIA · PI WANG, YINGXIAO · 2021 to 2025
$2.9M
Shear stress Regulation of Endothelial Glycolysis via METTL3-mediated RNA m6A ModificationR01HL170107 · NHLBI · UNIVERSITY OF CALIFORNIA, SAN DIEGO · PI SHU CHIEN, John YJ Shyy · 2024 to 2026
$2.5M
Ultrasound-controlled remote activation of CAR T cells for localized tumor immunotherapyR01EB029122 · NIBIB · UNIVERSITY OF SOUTHERN CALIFORNIA · PI WANG, YINGXIAO · 2020 to 2023
$1.7M
Training in Bioengineering Research and Technology Development in Cardiovascular in Cardiopulmonary Health and DiseaseT32HL160507 · NHLBI · UNIVERSITY OF CALIFORNIA, SAN DIEGO · PI Karen L Christman, Andrew D. McCulloch · 2022 to 2026
$1.6M
Development of single fluorophore biosensors for multiplex imaging of CAR T SignalingK01EB035649 · NIBIB · UNIVERSITY OF SOUTHERN CALIFORNIA · PI Longwei Liu · 2024 to 2026
$455k
Foundation for the National Institutes of Health (Foundation for the National Institutes of Health, Inc.) EB029122, GM140929, HL121365, HD107206, CA262815Foundation for the National Institutes of Health (Foundation for the National Institutes of Health, Inc.) NIH K01EB035649NCI NIH HHS R01 CA262815NHLBI NIH HHS R01 HL108735NHLBI NIH HHS R01 HL121365NHLBI NIH HHS R01 HL132141NHLBI NIH HHS R01 HL170107NHLBI NIH HHS T32 HL160507NIBIB NIH HHS K01 EB035649NIBIB NIH HHS R01 EB029122NICHD NIH HHS R01 HD107206NIGMS NIH HHS R01 GM072744NIGMS NIH HHS R35 GM140929U.S. Department of Health & Human Services | NIH | National Heart, Lung, and Blood Institute (NHLBI) HL108735, HL170107
6 · The paper itself

Abstract

Solid tumours show substantial mechanical heterogeneity, yet how such cues influence the susceptibility of cancer cells to T cell-based therapies remains unclear. Here we discover that cancer cells grown on soft matrices are less sensitive to chimeric antigen receptor T cell cytotoxicity and exhibit elevated extracellular adenosine triphosphate and sustained calcium activity. To understand the mechanisms underlying this reduced killing, we sought to identify the cells that respond to mechanical softness. We engineered a doxycycline-gated calcium-activated transcriptional mechano-recorder that integrates softness-induced calcium activity over a defined recording window and converts this prior signalling history into a stable fluorescent output that persists for days. Unlike real-time calcium indicators, which report instantaneous calcium activity only at the moment of imaging, this recorder preserves a sortable transcriptional mark, enabling selective labelling and profiling of cells according to their past mechanosensing activity. Transcriptomic analyses showed that recorder-positive cells adopt a stem-like programme, including epithelial-mesenchymal transition, hypoxia responses, oncogenic signalling and elevated stemness markers, across cancer cell lines and patient-derived samples. To render these resistant cells targetable, we rewired the mechano-recorder into a mechano-reprogrammer by replacing the fluorescent output with the clinically validated antigen CD19, enabling softness-responsive cells to be recognized by CD19-directed T cells. This rewired system improved elimination of stem-like cancer cells in culture and animal models, converting mechanobiological resistance into therapeutic vulnerability.

Identifiers

PMID42410050
PMCPMC13633754

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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.