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

How to benchmark medical AI agents.

PLoS medicine·2026
Same author

AI-based selection of tumor regions for genomic profiling in neuropathology.

Neuro-oncology advances·2026
Same author

Generalizable and explainable deep learning for brain MRI: a multi-cohort evaluation of 3D architectures for age and sex prediction.

Brain informatics·2026
Same author

Mutation enrichment in targeted panels flags immunotherapy-responsive POLE-driven hypermutated microsatellite-stable colorectal cancers.

NPJ precision oncology·2026
Same author

Established machine learning matches tabular foundation models in clinical predictions.

BMC medical informatics and decision making·2026
Same author

A deep learning framework for efficient pathology image analysis.

Nature communications·2026

Related Experiment Video

Updated: Nov 17, 2025

Visualization, Quantification, and Mapping of Immune Cell Populations in the Tumor Microenvironment
11:00

Visualization, Quantification, and Mapping of Immune Cell Populations in the Tumor Microenvironment

Published on: March 25, 2020

17.6K

Next Generation Imaging Techniques to Define Immune Topographies in Solid Tumors.

Violena Pietrobon1, Alessandra Cesano2, Francesco Marincola1

  • 1Refuge Biotechnologies, Inc., Menlo Park, CA, United States.

Frontiers in Immunology
|February 15, 2021
PubMed
Summary

Cancer immunotherapy shows promise, but solid tumor efficacy is limited by immune cell exclusion. Mapping cancer immune topographies is crucial for understanding and improving treatments.

Keywords:
deep learningimaging techniquesimmune exclusionimmune topographysingle-cell analysissolid tumors

More Related Videos

Author Spotlight: Unlocking Insights into the Immune Cell Landscape of Tumors
06:32

Author Spotlight: Unlocking Insights into the Immune Cell Landscape of Tumors

Published on: August 18, 2023

2.5K
Evaluation of Tumor-infiltrating Leukocyte Subsets in a Subcutaneous Tumor Model
07:49

Evaluation of Tumor-infiltrating Leukocyte Subsets in a Subcutaneous Tumor Model

Published on: April 13, 2015

20.5K

Related Experiment Videos

Last Updated: Nov 17, 2025

Visualization, Quantification, and Mapping of Immune Cell Populations in the Tumor Microenvironment
11:00

Visualization, Quantification, and Mapping of Immune Cell Populations in the Tumor Microenvironment

Published on: March 25, 2020

17.6K
Author Spotlight: Unlocking Insights into the Immune Cell Landscape of Tumors
06:32

Author Spotlight: Unlocking Insights into the Immune Cell Landscape of Tumors

Published on: August 18, 2023

2.5K
Evaluation of Tumor-infiltrating Leukocyte Subsets in a Subcutaneous Tumor Model
07:49

Evaluation of Tumor-infiltrating Leukocyte Subsets in a Subcutaneous Tumor Model

Published on: April 13, 2015

20.5K

Area of Science:

  • Oncology
  • Immunology
  • Bioengineering

Background:

  • Cancer immunotherapy has advanced significantly, particularly for hematological malignancies.
  • Efficacy in solid tumors is often limited by immune cell exclusion due to physical, functional, and dynamic barriers within the tumor microenvironment.
  • A unified understanding of immune exclusion models in solid tumors is lacking.

Purpose of the Study:

  • To systematically map cancer immune landscapes ('topographies') across different histologies.
  • To characterize the spatial and temporal distribution of lymphocytes in the tumor microenvironment.
  • To provide insights into immune exclusion mechanisms and identify potential biomarkers for personalized immunotherapies.

Main Methods:

  • Review of current standard and next-generation approaches for defining Cancer Immune Topographies.
  • Analysis of published studies on immune cell distribution in solid tumors.
  • Focus on spatial mapping techniques and quantitative analysis of immune infiltrates.

Main Results:

  • Immune exclusion presents significant barriers to effective immunotherapy in solid tumors.
  • Systematic mapping of immune landscapes is vital for understanding lymphocyte distribution.
  • Spatial mapping offers quantitative data for biomarker discovery and treatment design.

Conclusions:

  • Characterizing Cancer Immune Topographies is essential for advancing solid tumor immunotherapy.
  • Improved understanding of immune exclusion mechanisms can guide the development of novel therapeutic strategies.
  • Quantitative spatial mapping holds potential for personalized cancer treatment approaches.