Evidence mapPaperPMID 41199747Full record

ReviewFrontiers in genetics2025

Single-cell transcriptomics in metastatic breast cancer: mapping tumor evolution and therapeutic resistance.

Xu Han, Xin Li, Ling Bai, Gangling Zhang

Abstract readReview
In one paragraph

Review in Frontiers in genetics, 2025. 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. Review
  2. Article
  3. 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.

Xu HanBreast Center, Baotou City Cancer Hospital, Baotou, Inner Mongolia, China.
Xin LiBreast Center, Baotou City Cancer Hospital, Baotou, Inner Mongolia, China.
Ling BaiOperating Room, Baotou City Cancer Hospital, Baotou, Inner Mongolia, China.
Gangling ZhangBreast Center, Baotou City Cancer Hospital, Baotou, Inner Mongolia, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Metastatic breast cancer (MBC) remains the primary cause of mortality in breast cancer patients, driven by tumor heterogeneity, cellular evolution, and therapy-resistant clones. Traditional bulk transcriptomics, although informative, fail to capture rare subpopulations and context-specific gene expression, which are crucial for understanding disease progression. Single-cell transcriptomics (SCT) has emerged as a transformative approach, enabling high-resolution analysis of individual cells to reveal tumor composition, lineage dynamics, and transcriptional plasticity. This review highlights how SCT reshapes our understanding of MBC by mapping tumor evolution, identifying cancer stem-like cells, and characterizing states of epithelial-mesenchymal transition. We explore how SCT reveals clonal and spatial heterogeneity, and how tumor microenvironment components, including immune, stromal, and endothelial cells, interact with cancer cells to support immune evasion and the formation of a metastatic niche. SCT also uncovers mechanisms of therapeutic resistance, including transcriptional reprogramming and the survival of drug-tolerant subpopulations. Integrating SCT with spatial transcriptomics and multi-omics platforms offers a comprehensive view of the MBC ecosystem and may uncover novel therapeutic targets. We further discuss the translational potential of SCT for biomarker discovery, liquid biopsy development, and precision oncology. We address current technical challenges and future directions for clinical application. SCT is poised to transform MBC research and guide next-generation therapeutic strategies.

Indexed as

metastatic breast cancerprecision oncologyScRNA-seqtherapeutic resistancetumor heterogeneity

Identifiers

PMID41199747
PMCPMC12588578

What Socratic 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 Socratic graph.