Evidence map›Paper›PMID 40364843›Full record

ArticleFrontiers in immunology2025

Characterizing spatial immune architecture in metastatic melanoma using high-dimensional multiplex imaging.

Joel Eliason, Santhoshi Krishnan, Yasunari Fukuda, Matias A Bustos, Dan Winkowski, Sungnam Cho, Akshay Basi, Regan Baird, Elizabeth A Grimm, Michael A Davies and 4 more

Abstract read
In one paragraph

Article in Frontiers in immunology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing 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

6 citing papers in PubMed.

  1. Article
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  3. Review
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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

14 authors.

Joel EliasonDepartment of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, United States.
Santhoshi KrishnanDepartment of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, United States.
Yasunari FukudaKindai University, Nara Hospital, Nara, Japan.
Matias A BustosSaint John's Health Center, Santa Monica, CA, United States.
Dan WinkowskiVisiopharm A/S, Horsholm, Denmark, Denmark.
Sungnam ChoDepartment of Melanoma Medical Oncology, Melanoma Medical Oncology, MD Anderson Cancer Center, University of Texas, Houston, TX, United States.
Akshay BasiDepartment of Melanoma Medical Oncology, Melanoma Medical Oncology, MD Anderson Cancer Center, University of Texas, Houston, TX, United States.
Regan BairdVisiopharm A/S, Horsholm, Denmark, Denmark.
Elizabeth A GrimmDepartment of Melanoma Medical Oncology, Melanoma Medical Oncology, MD Anderson Cancer Center, University of Texas, Houston, TX, United States.
Michael A DaviesDepartment of Melanoma Medical Oncology, Melanoma Medical Oncology, MD Anderson Cancer Center, University of Texas, Houston, TX, United States.
Dave S B HoonSaint John's Health Center, Santa Monica, CA, United States.
Arvind RaoDepartment of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, United States.
Jared K BurksDepartment of Melanoma Medical Oncology, Melanoma Medical Oncology, MD Anderson Cancer Center, University of Texas, Houston, TX, United States.
Suhendan EkmekciogluDepartment of Melanoma Medical Oncology, Melanoma Medical Oncology, MD Anderson Cancer Center, University of Texas, Houston, TX, United States.

Funding

XenograftP30CA046592 · NCI · UNIVERSITY OF MICHIGAN AT ANN ARBOR · PI Eric R. Fearon · 1988 to 2026
$178.2M
NCI NIH HHS P30 CA046592
6 · The paper itself

Abstract

Introduction: Immune checkpoint inhibitors (ICIs) have significantly improved survival for patients with metastatic melanoma, yet many experienceresistance due to immunosuppressive mechanisms within the tumor immune microenvironment (TIME). Understanding how the spatial architecture of immune and inflammatory components changes across disease stages may reveal novel prognostic biomarkers and therapeutic targets. Methods: We performed high-dimensional spatial profiling of two melanoma tissue microarrays (TMAs), representing Stage III ( Results: Stage IV tumors exhibited a distinct immune landscape, with increased CD74- and MIF-enriched inflammatory neighborhoods and reduced iNOS-associated regions compared to Stage III. Cytotoxic T lymphocytes (CTLs) and tumor cells were more prevalent in Stage IV TIME, while B cells and NK cells were depleted. Spatial analysis revealed that CTL-Th cell, NK-T cell, and B-NK cell interactions were linked to improved survival, whereas macrophage aggregation and excessive B-Th cell clustering in inflammatory regions correlated with worse outcomes. Organ-specific analyses showed that CTL infiltration near tumor cells predicted survival in gastrointestinal metastases, while NK-T cell interactions were prognostic in lymph node and skin metastases. Discussion: Our results reveal stage-specific shifts in immune composition and spatial organization within the melanoma TIME. In advanced disease, immunosuppressive neighborhoods emerge alongside changes in immune cell localization, with spatial patterns of immune coordination-particularly involving CTLs, NK cells, and B cells-strongly predicting survival. These findings highlight spatial biomarkers that may refine patient stratification and guide combination immunotherapy strategies targeting the inflammatory architecture of the TIME.

Indexed as

MelanomaSkin NeoplasmsTumor MicroenvironmentBiomarkers, TumorFemaleHumansKiller Cells, NaturalLymphocytes, Tumor-InfiltratingMaleMiddle AgedNeoplasm MetastasisNeoplasm StagingPrognosisTissue Array AnalysisBiomarkers, Tumorimmune cell crosstalkimmune exclusioninflammatory biomarkersinflammatory signaling pathwaysmelanoma progressionprognostic immune signaturesspatial immune profilingtumor immune microenvironment (TIME)

Identifiers

PMID40364843
PMCPMC12069457

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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.