Evidence map›Paper›PMID 42088352›Full record

ArticleiScience2026

Decoding drug-responsive cell subpopulations in triple-negative breast cancer using single-cell multiomics.

Yue Wang, Santiago Haase, Austin Whitman, Adriana S Beltran, Philip M Spanheimer, Elizabeth Brunk

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in iScience, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. Review
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

6 authors.

Yue WangDepartment of Pharmacology, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.
Santiago HaaseIntegrative Program for Biological and Genome Sciences (IBGS), University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.
Austin WhitmanLineberger Comprehensive Cancer Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.
Adriana S BeltranDepartment of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.
Philip M SpanheimerLineberger Comprehensive Cancer Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.
Elizabeth BrunkDepartment of Pharmacology, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Understanding how individual cancer cells adapt to drug treatment is a fundamental challenge limiting precision medicine cancer therapy strategies. Here, we present a multimodal framework that integrates bulk and single-cell treated and untreated transcriptomics data to identify drug-responsive cell populations in triple-negative breast cancer (TNBC). Our framework defines seven bulk-level "identities," each representing unique combinations of biologically relevant genes. These trackable identities are further mapped onto single cells and uncover global patterns of how cell populations respond to drug treatment. By capturing the evolving nature of cellular states, we show that a select few identities dominate and drive population-level responses during treatment, which allows us to better predict how entire tumors respond to treatment. This insight is essential for designing precise combination therapies tailored to the unique heterogeneity of patient tumors, addressing the single-cell variations that ultimately determine therapeutic outcomes.

Indexed as

CancerClassification of bioinformatical subjectExpression studyTranscriptomics

Identifiers

PMID42088352
PMCPMC13138057

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.