Evidence map›Paper›PMID 39329702›Full record

ArticleCells2024

Characterizing the Tumor Microenvironment and Its Prognostic Impact in Breast Cancer.

Wenjuan Zhang, Alex Lee, Amit K Tiwari, Mary Qu Yang

Abstract read
In one paragraph

Article in Cells, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.

0numbers the graph read from it
0cells of the map it votes in
8citing 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

8 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
  4. Hormone Receptor Positive/HER2 Negative Breast Carcinoma: Association ofInternational journal of molecular sciences · 2025
    Article
  5. Review
  6. Article
  7. Article
  8. Organoids technology in cancer research: from basic applications to advancedFrontiers in cell and developmental biology · 2025
    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

4 authors.

Wenjuan ZhangMidSouth Bioinformatics Center and Joint Bioinformatics Graduate Program, University of Arkansas for Medical Sciences, Little Rock, AR 72204, USA.
Alex LeeBiology Department, University of Arkansas at Little Rock, Little Rock, AR 72204, USA.
Amit K TiwariDepartment of Pharmaceutical Sciences, College of Pharmacy, University of Arkansas for Medical Sciences, Little Rock, AR 72205, USA.
Mary Qu YangMidSouth Bioinformatics Center and Joint Bioinformatics Graduate Program, University of Arkansas for Medical Sciences, Little Rock, AR 72204, USA.

Funding

Understanding Hesitant AdoptersP20GM103429 · NIGMS · UNIV OF ARKANSAS FOR MED SCIS · PI Lawrence E Cornett, JERRY WARE · 2012 to 2026
$60.9M
DEVELOP NOVEL DEEP LEARNING AND COMBINATORIAL OPTIMIZATION METHODS TO IDENTIFY KEY DISEASE REGULATORY ELEMENTS FOR SINGLE-CELL DATAR15GM137288 · NIGMS · UNIVERSITY OF ARKANSAS AT LITTLE ROCK · PI YANG, MARY · 2020 to 2020
$444k
National Institutes of Health, USA NIH/1R15GM137288National Science Foundation NSF/Award No.OIA-1946391NIGMS NIH HHS P20 GM103429NIGMS NIH HHS R15 GM137288NIH HHS NIH/P20GM103429
6 · The paper itself

Abstract

The tumor microenvironment (TME) is crucial in cancer development and therapeutic response. Immunotherapy is increasingly recognized as a critical component of cancer treatment. While immunotherapies have shown efficacy in various cancers, including breast cancer, patient responses vary widely. Some patients receive significant benefits, while others experience minimal or no improvement. This disparity underscores the complexity and diversity of the immune system. In this study, we investigated the immune landscape and cell-cell communication within the TME of breast cancer through integrated analysis of bulk and single-cell RNA sequencing data. We established profiles of tumor immune infiltration that span across a broad spectrum of adaptive and innate immune cells. Our clustering analysis of immune infiltration identified three distinct patient groups: high T cell abundance, moderate infiltration, and low infiltration. Patients with low immune infiltration exhibited the poorest survival rates, while those in the moderate infiltration group showed better outcomes than those with high T cell abundance. Moreover, the high cell abundance group was associated with a greater tumor burden and higher rates of TP53 mutations, whereas the moderate infiltration group was characterized by a lower tumor burden and elevated PIK3CA mutations. Analysis of an independent single-cell RNA-seq breast cancer dataset confirmed the presence of similar infiltration patterns. Further investigation into ligand-receptor interactions within the TME unveiled significant variations in cell-cell communication patterns among these groups. Notably, we found that the signaling pathways SPP1 and EGF were exclusively active in the low immune infiltration group, suggesting their involvement in immune suppression. This work comprehensively characterizes the composition and dynamic interplay in the breast cancer TME. Our findings reveal associations between the extent of immune infiltration and clinical outcomes, providing valuable prognostic information for patient stratification. The unique mutations and signaling pathways associated with different patient groups offer insights into the mechanisms underlying diverse tumor immune infiltration and the formation of an immunosuppressive tumor microenvironment.

Indexed as

Breast NeoplasmsTumor MicroenvironmentFemaleHumansLymphocytes, Tumor-InfiltratingMutationPrognosisSingle-Cell Analysiscell communicationimmunosuppressiontumor immune infiltrationtumor microenvironment

Identifiers

PMID39329702
PMCPMC11429566

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.