Evidence map›Paper›PMID 39338178›Full record

ArticleJournal of personalized medicine2024

Analyzing the Spatial Distribution of Immune Cells in Lung Adenocarcinoma.

Florina Almarii, Maria Sajin, George Simion, Simona O Dima, Vlad Herlea

Abstract read
In one paragraph

Article in Journal of personalized medicine, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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.

Florina AlmariiDepartment of Pathology, Fundeni Clinical Institute, 022328 Bucharest, Romania.ORCID 0009-0005-0206-0191
Maria SajinDepartment of Pathology, "Carol Davila" University of Medicine and Pharmacy, 050474 Bucharest, Romania.
George SimionDepartment of Pathology, Emergency University Hospital, 050098 Bucharest, Romania.
Simona O DimaDepartment of Pathology, "Carol Davila" University of Medicine and Pharmacy, 050474 Bucharest, Romania.
Vlad HerleaDepartment of Pathology, Fundeni Clinical Institute, 022328 Bucharest, Romania.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

(1) Background: This study investigates the tumor immune microenvironment, focusing on immune cell distribution in lung adenocarcinoma. (2) Methods: We evaluated fifty cases of lung adenocarcinoma, and suitable areas for further studies were annotated on the histological slides. Two tumor cores per case were obtained, one from the tumor's center and another from its periphery, and introduced into three paraffin receptor blocks for optimized processing efficiency. The 4-micrometer-thick tissue microarray sections were stained for H&E and for CD68, CD163, CD8, CD4, and PD-L1; (3) Results: Our investigation revealed significant correlations between PD-L1 expression in tumor cells and the presence of CD163+ macrophages, between CD4+ cells and CD8+, CD68+, and CD163+ cells, and also between CD8+ T cells and CD163+ cells. Additionally, while we observed some differences in cellular components and densities between the tumor center and periphery, these differences were not statistically significant. However, distinct correlations between PD-L1 and immune cells in these regions were identified, suggesting spatial heterogeneity in the immune landscape. (4) Conclusions: These results emphasize the intricate interactions between immune cells and tumor cells in lung adenocarcinoma. Understanding patient spatial immune profile could improve patient selection for immunotherapy, ensuring that those most likely to benefit are identified.

Indexed as

immune cellNSCLCpathologytumor microenvironment

Identifiers

PMID39338178
PMCPMC11433064

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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.