Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Efficient calculation of fluid transport in porous media with moving boundaries.

bioRxiv : the preprint server for biology·2025
Same author

Optimizing Cancer Vaccinations Using a Physiologically Based Pharmacokinetic (PBPK) Model.

bioRxiv : the preprint server for biology·2025
Same author

Physiologically based pharmacokinetic model for CAR-T cell delivery and efficacy in solid tumors.

Proceedings of the National Academy of Sciences of the United States of America·2025
Same author

The anti-virus T cell response dominates the anti-cancer response in oncolytic virus therapy.

bioRxiv : the preprint server for biology·2025
Same author

Overcoming impaired antigen presentation in tumor draining lymph nodes facilitates immunotherapy.

bioRxiv : the preprint server for biology·2025
Same author

Wnt inhibition alleviates resistance to anti-PD1 therapy and improves antitumor immunity in glioblastoma.

Proceedings of the National Academy of Sciences of the United States of America·2025

Related Experiment Video

Updated: Jan 9, 2026

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma
04:09

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

Published on: October 10, 2018

8.6K

A Hybrid Multiscale Model for Predicting CAR-T Therapy Outcomes in Solid Tumors.

Mohammad R Nikmaneshi, Lance L Munn

    Biorxiv : the Preprint Server for Biology
    |December 3, 2025
    PubMed
    Summary
    This summary is machine-generated.

    Optimizing CAR-T cell therapy requires understanding tumor microenvironment barriers. A 3D model reveals collagen density and metabolic competition significantly impact CAR-T cell efficacy in solid tumors.

    More Related Videos

    Predictive Immune Modeling of Solid Tumors
    08:50

    Predictive Immune Modeling of Solid Tumors

    Published on: February 25, 2020

    7.4K
    Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model
    07:15

    Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

    Published on: August 16, 2020

    7.3K

    Related Experiment Videos

    Last Updated: Jan 9, 2026

    Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma
    04:09

    Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

    Published on: October 10, 2018

    8.6K
    Predictive Immune Modeling of Solid Tumors
    08:50

    Predictive Immune Modeling of Solid Tumors

    Published on: February 25, 2020

    7.4K
    Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model
    07:15

    Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

    Published on: August 16, 2020

    7.3K

    Area of Science:

    • Immunology
    • Biomedical Engineering
    • Computational Biology

    Background:

    • CAR-T cell therapy success hinges on T cell infiltration into tumors, termed 'tumor hotness'.
    • Limited understanding of T cell-tumor microenvironment interactions hinders immunotherapy progress.
    • Existing strategies for enhancing T cell accumulation face challenges due to complex microenvironmental barriers.

    Purpose of the Study:

    • To develop a physiological mechanistic model of the 3D tumor microenvironment (TME).
    • To evaluate CAR-T cell performance under varying environmental conditions and infusion strategies.
    • To identify key barriers limiting CAR-T cell efficacy and inform optimization strategies.

    Main Methods:

    • Developed a 3D mechanistic model integrating vascular (rolling, adhesion, endothelial suppression) and interstitial (ECM density, metabolic competition, chemokine sensitivity) barriers.
    • Simulated CAR-T cell distribution and performance within the TME under different conditions.
    • Quantitatively analyzed the impact of specific microenvironmental factors on CAR-T cell infiltration and efficacy.

    Main Results:

    • Collagen density and metabolic competition were identified as dominant factors limiting CAR-T cell efficacy.
    • Enhanced vascular adhesion improved infiltration but was ultimately constrained by collagen and metabolism.
    • Endothelial suppression significantly reduced tumor hotness; its alleviation improved response.
    • Systemic infusion resulted in higher tumor hotness than intratumoral delivery, with combined routes or reduced collagen restoring efficacy in dense tumors.

    Conclusions:

    • The developed mechanistic framework enables a quantitative understanding of CAR-T cell-TME interactions.
    • Stromal and metabolic constraints are more critical than vascular adhesion for CAR-T cell efficacy.
    • This model provides a foundation for rational optimization of CAR-T cell design and delivery strategies to overcome resistance in solid tumors.