Evidence map›Paper›PMID 41707645›Full record

ArticleCell reports. Medicine2026

Tumor microenvironment transcriptional activity enables robust stratification of chemotherapy response in triple-negative breast cancer.

Yaoyi Dai, Xiaoxi Pan, Shuai Guo, Shuangxi Ji, Shaolong Cao, Matthew D Montierth, Yujie Jiang, Jeffrey T Chang, Leming Shi, Shabnam Shalapour and 6 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Cell reports. Medicine, 2026. 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

16 authors.

Yaoyi DaiDepartment of Bioinformatics and Computational Biology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA; Graduate Program of Quantitative and Computational Biosciences, Baylor College of Medicine, Houston, TX 77030, USA.
Xiaoxi PanDepartment of Translational Molecular Pathology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA; Institute for Data Science in Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA.
Shuai GuoDepartment of Bioinformatics and Computational Biology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA.
Shuangxi JiDepartment of Bioinformatics and Computational Biology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA.
Shaolong CaoDepartment of Bioinformatics and Computational Biology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA.
Matthew D MontierthDepartment of Bioinformatics and Computational Biology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA; Graduate Program of Quantitative and Computational Biosciences, Baylor College of Medicine, Houston, TX 77030, USA.
Yujie JiangDepartment of Bioinformatics and Computational Biology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA.
Jeffrey T ChangMcGovern Medical School, Houston, TX 77030, USA.
Leming ShiState Key Laboratory of Genetic Engineering, School of Life Sciences and Human Phenome Institute, Fudan University, Shanghai 200093, China.
Shabnam ShalapourDepartment of Cancer Biology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA.
Gloria V EcheverriaDepartment of Medicine, Baylor College of Medicine, Houston, TX 77030, USA; Department of Molecular and Cellular Biology, Baylor College of Medicine, Houston, TX 77030, USA; Dan L. Duncan Cancer Center, Baylor College of Medicine, Houston, TX 77030, USA; Lester and Sue Smith Breast Center, Baylor College of Medicine, Houston, TX 77030, USA.
Lucy YatesWellcome Sanger Institute, Hinxton CB10 1SA, UK.
Johan StaafDivision of Translational Cancer Research, Department of Laboratory Medicine, Lund University, Medicon Village, 223 81 Lund, Sweden.
Bora LimDepartment of Breast Medical Oncology, The Division of Cancer Medicine, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA; The Morgan Welch IBC Research Program and Clinic, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA.
Yinyin YuanDepartment of Translational Molecular Pathology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA; Institute for Data Science in Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA.
Wenyi WangDepartment of Bioinformatics and Computational Biology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA. Electronic address: wwang7@mdanderson.org.

Funding

Statistical methods for genomic analysis of heterogeneous tumorsR01CA268380 · NCI · UNIVERSITY OF TX MD ANDERSON CAN CTR · PI Wenyi Wang · 2022 to 2026
$2.4M
NCI NIH HHS R01 CA268380
6 · The paper itself

Abstract

Triple-negative breast cancer (TNBC) exhibits heterogeneous treatment responses, yet molecular subtypes based on predefined biological pathways show limited prognostic value. We introduce tumor-specific total mRNA expression (TmS), a pathway-agnostic deconvolution metric derived from matched RNA/DNA sequencing, as a robust stratification tool. Analyzing 575 TNBC patients across Western and East Asian populations, TmS outperforms established subtypes in predicting chemotherapy outcomes, stratifying patients into high TmS with favorable prognosis and low TmS with poor prognosis. Stromal enrichment with immune exclusion emerges as a universal feature of chemotherapy-resistant low-TmS tumors across all cohorts. Population-specific features distinguish Asian cohorts: high-TmS tumors exhibit cell cycle-driven proliferation programs, and low-TmS tumors display immune dysfunction with memory B cell enrichment and divergent RAS/mitogen-activated protein kinase (MAPK) activation, compared to Western populations. Despite these differences, extracellular matrix organization represents a conserved therapeutic vulnerability in treatment-resistant low-TmS patients. TmS provides a unifying framework for dissecting TNBC heterogeneity and enabling precision therapy across diverse populations.

Indexed as

Transcription, GeneticTriple Negative Breast NeoplasmsTumor MicroenvironmentDrug Resistance, NeoplasmFemaleGene Expression Regulation, NeoplasticHumansPrognosisRNA, MessengerRNA, Messengerprognostic stratificationtranscriptome plasticitytranscriptomic deconvolutiontumor microenvironment

Identifiers

PMID41707645
PMCPMC12923974

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.