Evidence map›Paper›PMID 40265075›Full record

ReviewImmunotherapy advances2025

Imaging antigen processing and presentation in cancer.

Doreen Lau, Tim Elliott

Abstract readReview
In one paragraph

Review in Immunotherapy advances, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing 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

4 citing papers in PubMed.

  1. Review
  2. Article
  3. Article
  4. Review
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

2 authors.

Doreen LauCentre for Inflammation Research and Translational Medicine, Department of Life Sciences, Division of Biosciences, Brunel University London, London, United Kingdom.ORCID https://orcid.org/0000-0002-7623-2401
Tim ElliottCentre for Immuno-Oncology, Nuffield Department of Medicine, University of Oxford, Oxford, United Kingdom.ORCID https://orcid.org/0000-0003-1097-0222

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Introduction: Antigen processing and presentation are vital processes of the adaptive immunity. These processes involve a series of intracellular and extracellular events, including the enzymology within cells during antigen processing, the loading and presentation of antigenic peptides on major histocompatibility complexes, the recruitment of T cells, their interaction with antigen-presenting cells, and the expression of adhesion, co-stimulatory and co-inhibitory molecules at the T cell immunological synapse. These events collectively fine-tune and sustain antigen recognition and T cell function. Dysregulation of this machinery can profoundly impact the efficacy of cancer immunotherapy. Imaging technologies have emerged as powerful tools for elucidating the mechanisms underlying antigen processing and presentation. By providing complementary perspectives into the cellular and molecular interactions at play, imaging has significantly enhanced our understanding of these complex immunological events in cancer. Such insights can improve the monitoring of immunotherapy responses, facilitate the identification of effective treatments, and aid in predicting patient outcomes. Methods: This review explores the role of imaging in studying antigen processing and presentation in the context of cancer. Conclusion: It highlights key considerations for developing imaging tools and biomarkers to detect components of these pathways. Additionally, it examines the strengths and limitations of various imaging approaches and discusses their potential for clinical translation.

Indexed as

antigen presentationantigen processingbiomarkersimaging technologiesimmunotherapy

Identifiers

PMID40265075
PMCPMC12012451

What OpenQuestion holds

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LicenceCC BY
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Registered trials

None linked

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.