Evidence map›Paper›PMID 39856115›Full record

ArticleScientific reports2025

Immune infiltrate populations within distinct tumor immune microenvironments predictive of immune checkpoint treatment outcome.

Brian Z Ring, Catherine T Cronister, Huijun Z Ring, Douglas T Ross, Robert S Seitz

Abstract read
In one paragraph

Article in Scientific reports, 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. Article
  2. Article
  3. Article
  4. Article
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

5 authors.

Brian Z RingHelixHarbor Analytics, San Francisco, CA, USA. bzring@gmail.com.
Catherine T CronisterOncocyte Inc., Irvine, CA, USA.
Huijun Z RingDepartment of Medicine, Stanford University, Stanford, CA, USA.
Douglas T RossTwinStrand Biosciences, Seattle, WA, USA.
Robert S SeitzHelixHarbor Analytics, San Francisco, CA, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Understanding the dynamic tumor immune microenvironment (TIME) is important in guiding immunotherapy. We have previously validated signatures predictive of checkpoint inhibitor efficacy which distinguish immunomodulatory, mesenchymal stem-like, and mesenchymal phenotypes. Here we use twenty tumor types (7162 samples) to identify potentially conserved immune biology within these TIME spaces, genes co-expressed across distinct cell types involved these immune processes, and the association of these signatures with ICI response. One signature, which contained multiple B-cell markers, was associated with immunotherapy efficacy in three cohorts, including IMvigor210. This signature of potentially conserved B-cell biology in co-infiltrated immune cell ecosystems had a more consistent association with outcome than comparable single cell type models and likely reflects a complex immunological response involving multilayered relationships between distinct immune effector cell types. These signatures were most highly expressed in tumors with prominent immune cell invasion, however there was consistent identification of infiltrate presence in relatively immune restricted cases. This suggests that these immune population signatures may identify conserved immune cell type co-infiltrate physiology of the TIME that best captures immune physiology with potential clinical utility.

Indexed as

Immune Checkpoint InhibitorsLymphocytes, Tumor-InfiltratingNeoplasmsTumor MicroenvironmentBiomarkers, TumorB-LymphocytesHumansImmunotherapyTreatment OutcomeBiomarkers, TumorImmune Checkpoint Inhibitors

Identifiers

PMID39856115
PMCPMC11760962

What OpenQuestion holds

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Registered trials

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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.