Go to JCI Insight
  • About
  • Editors
  • Consulting Editors
  • For authors
  • Publication ethics
  • Publication alerts by email
  • Advertising
  • Job board
  • Contact
  • Clinical Research and Public Health
  • Current issue
  • Past issues
  • By specialty
    • COVID-19
    • Cardiology
    • Gastroenterology
    • Immunology
    • Metabolism
    • Nephrology
    • Neuroscience
    • Oncology
    • Pulmonology
    • Vascular biology
    • All ...
  • Videos
    • ASCI Milestone Awards
    • Video Abstracts
    • Conversations with Giants in Medicine
  • Reviews
    • View all reviews ...
    • The cGAS-STING pathway: DNA sensing in health and disease (Jun 2026)
    • Neurodegeneration (Mar 2026)
    • Clinical innovation and scientific progress in GLP-1 medicine (Nov 2025)
    • Pancreatic Cancer (Jul 2025)
    • Complement Biology and Therapeutics (May 2025)
    • Evolving insights into MASLD and MASH pathogenesis and treatment (Apr 2025)
    • Microbiome in Health and Disease (Feb 2025)
    • View all review series ...
  • Viewpoint
  • Collections
    • In-Press Preview
    • Clinical Research and Public Health
    • Research Letters
    • Letters to the Editor
    • Editorials
    • Commentaries
    • Editor's notes
    • Reviews
    • Viewpoints
    • 100th anniversary
    • Top read articles

  • Current issue
  • Past issues
  • Specialties
  • Reviews
  • Review series
  • ASCI Milestone Awards
  • Video Abstracts
  • Conversations with Giants in Medicine
  • In-Press Preview
  • Clinical Research and Public Health
  • Research Letters
  • Letters to the Editor
  • Editorials
  • Commentaries
  • Editor's notes
  • Reviews
  • Viewpoints
  • 100th anniversary
  • Top read articles
  • About
  • Editors
  • Consulting Editors
  • For authors
  • Publication ethics
  • Publication alerts by email
  • Advertising
  • Job board
  • Contact
Imaging activated T cells predicts response to cancer vaccines
Israt S. Alam, Aaron T. Mayer, Idit Sagiv-Barfi, Kezheng Wang, Ophir Vermesh, Debra K. Czerwinski, Emily M. Johnson, Michelle L. James, Ronald Levy, Sanjiv S. Gambhir
Israt S. Alam, Aaron T. Mayer, Idit Sagiv-Barfi, Kezheng Wang, Ophir Vermesh, Debra K. Czerwinski, Emily M. Johnson, Michelle L. James, Ronald Levy, Sanjiv S. Gambhir
View: Text | PDF
Research Article Immunology Oncology

Imaging activated T cells predicts response to cancer vaccines

  • Text
  • PDF
Abstract

In situ cancer vaccines are under active clinical investigation, given their reported ability to eradicate both local and disseminated malignancies. Intratumoral vaccine administration is thought to activate a T cell–mediated immune response, which begins in the treated tumor and cascades systemically. In this study, we describe a PET tracer (64Cu-DOTA-AbOX40) that enabled noninvasive and longitudinal imaging of OX40, a cell-surface marker of T cell activation. We report the spatiotemporal dynamics of T cell activation following in situ vaccination with CpG oligodeoxynucleotide in a dual tumor–bearing mouse model. We demonstrate that OX40 imaging was able to predict tumor responses on day 9 after treatment on the basis of tumor tracer uptake on day 2, with greater accuracy than both anatomical and blood-based measurements. These studies provide key insights into global T cell activation following local CpG treatment and indicate that 64Cu-DOTA-AbOX40 is a promising candidate for monitoring clinical cancer immunotherapy strategies.

Authors

Israt S. Alam, Aaron T. Mayer, Idit Sagiv-Barfi, Kezheng Wang, Ophir Vermesh, Debra K. Czerwinski, Emily M. Johnson, Michelle L. James, Ronald Levy, Sanjiv S. Gambhir

×

Figure 5

Imaging- and blood-based correlates of response to in situ tumor vaccination with CpG.

Options: View larger image (or click on image) Download as PowerPoint
Imaging- and blood-based correlates of response to in situ tumor vaccina...
(A) Correlelogram of top hits determined by significance analysis of microarrays from a Luminex 38 plex cytokine assay (fold change; MFI/control) and Immuno-PET imaging ROIs (fold change; % ID/g ROI/muscle) with tumor response on day 2: log(fold[tumor volume mm3 on day 2/tumor volume mm3 on day 0]). Color scale and circle size both represent Pearson’s correlation coefficients. Large yellow circle (–1) indicates perfect inverse correlation; no circle (0) indicates no correlation; large blue circle (1) indicates perfect correlation. (B) Univariate regression of day-9 tumor response versus day-2 tumor response according to anatomical measurements. (C) Univariate regression of day-9 tumor response versus day-2 tumor tracer uptake (% ID/g). Blue line indicates vehicle-only fit; yellow line indicates CpG-only fit; black dashed line indicates all. (D) Tumor growth versus time after therapy. Yellow zone designates responders. The cutoff was determined using unsupervised hierarchical clustering. (E) Unsupervised hierarchical clustering and model visualization of the k-means nearest-centroid classifier. Lines indicate predictions; circles indicate truth.

Copyright © 2026 American Society for Clinical Investigation
ISSN: 0021-9738 (print), 1558-8238 (online)

Sign up for email alerts