Evidence map›Paper›PMID 40675988›Full record

ArticleNPJ breast cancer2025

Modeling tumor relapse using proliferation tracing and ablation transgenic mouse.

Chuang Zhao, Xin-Nan Zheng, Han-Ying Huang, Lin Tian

Abstract read
In one paragraph

Article in NPJ breast cancer, 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. γδ T cells and cancer.The Journal of clinical investigation · 2026
    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.

Chuang ZhaoState Key Laboratory of Oncology in South China, Guangdong Provincial Clinical Research Center for Cancer, Sun Yat-sen University Cancer Center, Guangzhou, China.
Xin-Nan ZhengState Key Laboratory of Oncology in South China, Guangdong Provincial Clinical Research Center for Cancer, Sun Yat-sen University Cancer Center, Guangzhou, China.
Han-Ying HuangState Key Laboratory of Oncology in South China, Guangdong Provincial Clinical Research Center for Cancer, Sun Yat-sen University Cancer Center, Guangzhou, China.
Lin TianState Key Laboratory of Oncology in South China, Guangdong Provincial Clinical Research Center for Cancer, Sun Yat-sen University Cancer Center, Guangzhou, China. tianlin@sysucc.org.cn.

Funding

National Natural Science Foundation of China 82173278
6 · The paper itself

Abstract

Tumor relapse remains a significant obstacle to successful therapy. Preclinical animal models that accurately reflect tumor relapse in patients are urgently needed. Here, we employed a dual recombinase-mediated genetic system to genetically trace and ablate proliferating cells in a polyomavirus middle T antigen (PyMT)-induced spontaneous murine breast cancer model. This system enabled the acute ablation of cells that had undergone proliferation within a defined time window, resulting in a drastic tumor shrinkage, followed by a gradual tumor relapse due to the presence of residual low-cycling cells. We then applied single-cell RNA sequencing (scRNA-seq) to unbiasedly compare the tumor ecosystems of the primary and relapsed PyMT tumors. Compared with the primary tumors, the relapsed tumors exhibited a higher proportion of cancer stem cells and pro-tumor γδ T cells, as well as co-expression of Spp1 and Vegfa in multiple myeloid cell populations - features that predict poor therapeutic response and unfavorable outcomes in human breast cancer patients. Collectively, this proliferation tracing and ablation model emulates chemotherapies that preferentially eliminate proliferating cancer cells, serving as a robust tool and a valuable resource for testing novel therapeutic strategies in relapsed tumors.

Identifiers

PMID40675988
PMCPMC12271533

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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.