Evidence map›Paper›PMID 41640877›Full record

ArticleNAR genomics and bioinformatics2026

A highly resolved integrated single-cell atlas of human breast cancers.

Andrew Chen, Lina Kroehling, Christina S Ennis, Gerald V Denis, Stefano Monti

Abstract read
In one paragraph

Article in NAR genomics and bioinformatics, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

3 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

5 authors.

Andrew ChenSection of Computational Biomedicine, Boston University Chobanian and Avesidian School of Medicine, Boston, MA 02118, United States.ORCID https://orcid.org/0000-0002-8508-0227
Lina KroehlingSection of Computational Biomedicine, Boston University Chobanian and Avesidian School of Medicine, Boston, MA 02118, United States.ORCID https://orcid.org/0000-0003-4996-7450
Christina S EnnisBoston University-Boston Medical Center Cancer Center, Boston University Chobanian and Avesidian School of Medicine, Boston, MA 02118, United States.ORCID https://orcid.org/0000-0003-0316-4003
Gerald V DenisBoston University-Boston Medical Center Cancer Center, Boston University Chobanian and Avesidian School of Medicine, Boston, MA 02118, United States.ORCID https://orcid.org/0000-0001-9886-0401
Stefano MontiSection of Computational Biomedicine, Boston University Chobanian and Avesidian School of Medicine, Boston, MA 02118, United States.ORCID https://orcid.org/0000-0002-9376-0660

Funding

Multiscale analysis of metabolic inflammation as a driver of breast cancerU01CA243004 · NCI · BOSTON UNIVERSITY MEDICAL CAMPUS · PI DENIS, GERALD V, EMILI, ANDREW · 2020 to 2024
$2.8M
Predoctoral Training in Bioinformatics and Computational BiologyT32GM100842 · NIGMS · BOSTON UNIVERSITY (CHARLES RIVER CAMPUS) · PI TULLIUS, THOMAS D · 2012 to 2022
$2.8M
NCI NIH HHS U01 CA243004NIGMS NIH HHS T32 GM100842
6 · The paper itself

Abstract

In this study, we developed an integrated single-cell transcriptomic (scRNAseq) atlas of human breast cancer (BC), the largest resource of its kind, totaling >600 000 cells across 138 patients. Rigorous integration and annotation of publicly available scRNAseq data enabled a highly resolved characterization of epithelial, immune, and stromal heterogeneity within the tumor microenvironment (TME). Within the immune compartment, we were able to characterize heterogeneity of CD4, CD8 T cells, and macrophage subpopulations. Within the stromal compartment, subpopulations of endothelial cells (ECs) and cancer-associated fibroblasts (CAFs) were resolved. Within the cancer epithelial compartment, we characterized the functional heterogeneity of cells across the axes of stemness, epithelial-mesenchymal plasticity, and canonical cancer pathways. Across all subpopulations observed in the TME, we performed a multi-resolution survival analysis to identify epithelial cell states and immune and stromal cell types, which conferred a survival advantage in both The Cancer Genome Atlas (TCGA), METABRIC, and SCANB. We also identified robust associations between TME composition and clinical phenotypes such as tumor subtype and grade that were not discernible when the analysis was limited to individual datasets, highlighting the need for atlas-based analyses. This atlas represents a valuable resource for further high-resolution analyses of TME heterogeneity within BC.

Indexed as

Breast NeoplasmsSingle-Cell AnalysisTranscriptomeFemaleHumansSingle-Cell Gene Expression AnalysisTumor Microenvironment

Identifiers

PMID41640877
PMCPMC12867518

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC
Read underepoch 390

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.