Evidence map›Paper›PMID 38636457›Full record

ArticleCell systems2024

Integrating multiplexed imaging and multiscale modeling identifies tumor phenotype conversion as a critical component of therapeutic T cell efficacy.

John W Hickey, Eran Agmon, Nina Horowitz, Tze-Kai Tan, Matthew Lamore, John B Sunwoo, Markus W Covert, Garry P Nolan

Open access · hybridAbstract read
In one paragraph

Article in Cell systems, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.

0numbers the graph read from it
0cells of the map it votes in
19citing papers in PubMed
6.2field-weighted citation impact, top 3% of its field
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

19 citing papers in PubMed, 23 citations in OpenAlex.

  1. Article
  2. Article
  3. Single-cell multiomics identifies an ALDH9A1-carnitine signaling axis driving resistance of NSCLC to immunotherapy.Proceedings of the National Academy of Sciences of the United States of America · 2026
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  11. Fluid-Squid: DIY Multiplexed Imaging of Cells and Tissues.bioRxiv : the preprint server for biology · 2025
    Article
  12. Review
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  15. Review
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  18. FromFrontiers in molecular biosciences · 2024
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  19. Article
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

8 authors at 2 institutions in 1 country.

John W HickeyDepartment of Microbiology & Immunology, Stanford University School of Medicine, Stanford, CA 94305, USA; Department of Pathology, Stanford University School of Medicine, Stanford, CA 94305, USA; Department of Biomedical Engineering, Duke University, Durham, NC 27708, USA.
Eran AgmonDepartment of Bioengineering, Stanford University, Stanford, CA 94305, USA; Center for Cell Analysis and Modeling, University of Connecticut Health, Farmington, CT 06032, USA.
Nina HorowitzDepartment of Bioengineering, Stanford University, Stanford, CA 94305, USA.
Tze-Kai TanDepartment of Pathology, Stanford University School of Medicine, Stanford, CA 94305, USA; Institute for Stem Cell Biology and Regenerative Medicine, Stanford University School of Medicine, Stanford, CA 94305, USA.
Matthew LamoreDepartment of Biomedical Engineering, Johns Hopkins University, Baltimore, MD 21205, USA.
John B SunwooInstitute for Stem Cell Biology and Regenerative Medicine, Stanford University School of Medicine, Stanford, CA 94305, USA; Department of Otolaryngology, Head and Neck Surgery, Stanford Cancer Institute Stanford University School of Medicine, Stanford, CA 94305, USA.
Markus W CovertDepartment of Bioengineering, Stanford University, Stanford, CA 94305, USA. Electronic address: mcovert@stanford.edu.
Garry P NolanDepartment of Pathology, Stanford University School of Medicine, Stanford, CA 94305, USA. Electronic address: gnolan@stanford.edu.
Stanford University · USJohns Hopkins University · US

Funding

Using a tonsil organoid system to probe conditions for the induction of protective antibody and T cell responses to influenza.U19AI057229 · NIAID · STANFORD UNIVERSITY · PI Mark Morris Davis · 2003 to 2026
$88.5M
Systems Biology CoreU19AI100627 · NIAID · SCRIPPS RESEARCH INSTITUTE, THE · PI NOLAN, GARRY P · 2012 to 2021
$49.1M
Technology CoreU19AI135976 · NIAID · SEATTLE CHILDREN'S HOSPITAL · PI ADEREM, ALAN A · 2018 to 2022
$17.0M
The Cellular Geography of Therapeutic Resistance in CancerU2CCA233195 · NCI · DANA-FARBER CANCER INST · PI VAN ALLEN, ELIEZER M · 2018 to 2023
$13.7M
Project 3: Identifying plasma biomarkers predicting time to HIV rebound after treatment interruptionP01AI131374 · NIAID · J. DAVID GLADSTONE INSTITUTES · PI GREENE, WARNER C. · 2017 to 2021
$8.5M
The Lung PCA: A Multi-Dimensional Atlas of Pulmonary PremalignancyU2CCA233238 · NCI · BOSTON UNIVERSITY MEDICAL CAMPUS · PI DUBINETT, STEVEN M., SPIRA, AVRUM E · 2018 to 2021
$7.0M
Stanford Tissue Mapping CenterU54HG010426 · NHGRI · STANFORD UNIVERSITY · PI SNYDER, MICHAEL P. · 2018 to 2021
$5.1M
Tissue Cytokine Sequestration and Immune Regulation in AutoimmunityU01AI101984 · NIAID · STANFORD UNIVERSITY · PI BOLLYKY, PAUL L · 2012 to 2021
$5.0M
Multiscale Modeling of Myelodysplastic SyndromesR01HL128173 · NHLBI · VIRGINIA COMMONWEALTH UNIVERSITY · PI COREY, SETH JOEL, KIMMEL, MAREK · 2015 to 2019
$3.8M
Balanced signaling cues to guide cell transitions in the blood lineage continuumR01HL120724 · NHLBI · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · PI CHAKRABORTY, ARUP K., NOLAN, GARRY P · 2015 to 2019
$3.7M
T Cell Reagent Research for Monitoring T Cell in Food AllergyU01AI140498 · NIAID · STANFORD UNIVERSITY · PI NADEAU, KARI C. · 2018 to 2022
$1.8M
Cancer-Translational Nanotechnology Training Program (Cancer-TNT)T32CA196585 · NCI · STANFORD UNIVERSITY · PI FELSHER, DEAN W, RAO, JIANGHONG · 2015 to 2019
$1.8M
NCI NIH HHS F32 CA233203NCI NIH HHS F99 CA212231NCI NIH HHS R33 CA183692NCI NIH HHS T32 CA196585NCI NIH HHS U2C CA233195NCI NIH HHS U2C CA233238NHGRI NIH HHS U54 HG010426NHLBI NIH HHS R01 HL120724NHLBI NIH HHS R01 HL128173NIAID NIH HHS P01 AI131374NIAID NIH HHS U01 AI101984NIAID NIH HHS U01 AI140498NIAID NIH HHS U19 AI057229NIAID NIH HHS U19 AI100627NIAID NIH HHS U19 AI135976NIDDK NIH HHS UG3 DK114937NIGMS NIH HHS F32 GM137464
6 · The paper itself

Abstract

Cancer progression is a complex process involving interactions that unfold across molecular, cellular, and tissue scales. These multiscale interactions have been difficult to measure and to simulate. Here, we integrated CODEX multiplexed tissue imaging with multiscale modeling software to model key action points that influence the outcome of T cell therapies with cancer. The initial phenotype of therapeutic T cells influences the ability of T cells to convert tumor cells to an inflammatory, anti-proliferative phenotype. This T cell phenotype could be preserved by structural reprogramming to facilitate continual tumor phenotype conversion and killing. One takeaway is that controlling the rate of cancer phenotype conversion is critical for control of tumor growth. The results suggest new design criteria and patient selection metrics for T cell therapies, call for a rethinking of T cell therapeutic implementation, and provide a foundation for synergistically integrating multiplexed imaging data with multiscale modeling of the cancer-immune interface. A record of this paper's transparent peer review process is included in the supplemental information.

Indexed as

NeoplasmsHumansPhenotypeT-Lymphocytesagent-based modelingcellular therapycomputational analysis of tissue imaging dataimmunotherapymultiplexed tissue imagingmultiscale modelingspatial biologysystems biologyT cellstumor cell phenotype

Identifiers

PMID38636457
PMCPMC11030795
OpenAlexW4394871491

What OpenQuestion holds

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LicenceCC BY
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Registered trials

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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.