Evidence map›Paper›PMID 41580235›Full record

ArticleThe American journal of pathology2026

Single-Cell Triomics Analysis of Tumor Cells Infiltrating Patient-Derived Breast Cancer Scaffolds.

Stefan Filges, Emma Jonasson, Maria Del Carmen Leiva Arrabal, Lisa Andersson, Anna Gustafsson, Dalia Dhingra, Pedro Mendez, Aik Ooi, Adam Sciambi, Göran Landberg and 2 more

Abstract read
In one paragraph

Article in The American journal of pathology, 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

12 authors.

Stefan FilgesSahlgrenska Center for Cancer Research, Department of Laboratory Medicine, Institute of Biomedicine, University of Gothenburg, Gothenburg, Sweden.
Emma JonassonSahlgrenska Center for Cancer Research, Department of Laboratory Medicine, Institute of Biomedicine, University of Gothenburg, Gothenburg, Sweden.
Maria Del Carmen Leiva ArrabalSahlgrenska Center for Cancer Research, Department of Laboratory Medicine, Institute of Biomedicine, University of Gothenburg, Gothenburg, Sweden.
Lisa AnderssonSahlgrenska Center for Cancer Research, Department of Laboratory Medicine, Institute of Biomedicine, University of Gothenburg, Gothenburg, Sweden.
Anna GustafssonSahlgrenska Center for Cancer Research, Department of Laboratory Medicine, Institute of Biomedicine, University of Gothenburg, Gothenburg, Sweden.
Dalia DhingraMission Bio, Inc., South San Francisco, California.
Pedro MendezMission Bio, Inc., South San Francisco, California.
Aik OoiMission Bio, Inc., South San Francisco, California.
Adam SciambiMission Bio, Inc., South San Francisco, California.
Göran LandbergSahlgrenska Center for Cancer Research, Department of Laboratory Medicine, Institute of Biomedicine, University of Gothenburg, Gothenburg, Sweden.
David RuffMission Bio, Inc., South San Francisco, California.
Anders StåhlbergSahlgrenska Center for Cancer Research, Department of Laboratory Medicine, Institute of Biomedicine, University of Gothenburg, Gothenburg, Sweden; Department of Clinical Genetics and Genomics, Sahlgrenska University Hospital, Region Västra Götaland, Gothenburg, Sweden; Wallenberg Centre for Molecular and Translational Medicine, University of Gothenburg, Gothenburg, Sweden; Science for Life Laboratory, Institute of Biomedicine, University of Gothenburg, Gothenburg, Sweden. Electronic address: anders.stahlberg@gu.se.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Cellular heterogeneity plays a critical role in tissues and diseases, including cancer. Single-cell technologies are required to provide detailed information about the phenotype and genotype of individual cells. Despite several approaches to analyzing different analytes at the single-cell level, it is challenging to assess DNA, RNA, and protein simultaneously. Here, a single-cell triomics method to assess DNA, RNA, and proteins from the same cell using a targeted sequencing approach is shown. Breast cancer cells cultured in monolayers and in patient-derived scaffolds that mimic in vivo-like growth conditions, both with and without chemotherapy treatment, were analyzed. Data showed that DNA, RNA, and protein biomarkers could be reliably analyzed, providing biological insights into breast cancer cell heterogeneity. In addition, chemotherapy treatment caused changes in subpopulations and expressions of biomarkers. Furthermore, cells growing in patient-derived scaffolds generated from various breast cancers affected cell heterogeneity and drug resistance differently as a result of the unique tumor-specific microenvironments. The data show that single-cell triomics provides new means to assess cancer cell heterogeneity at DNA, RNA, and protein levels.

Indexed as

Breast NeoplasmsSingle-Cell AnalysisBiomarkers, TumorFemaleHumansSingle-Cell Gene Expression AnalysisTissue ScaffoldsTumor MicroenvironmentBiomarkers, Tumor

Identifiers

PMID41580235
PMCPMC13084604

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.