Evidence map›Paper›PMID 39891927›Full record

ArticleCancer research2025

In Search of Representative Translational Cancer Model Systems.

Hannah E Trembath, Philip M Spanheimer

Abstract readComment
PubMed Publisher
In one paragraph

Article in Cancer research, 2025. 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

2 authors.

Hannah E TrembathDepartment of Surgery, University of North Carolina, Chapel Hill, North Carolina.ORCID 0000-0001-8655-3551
Philip M SpanheimerDepartment of Surgery, University of North Carolina, Chapel Hill, North Carolina.ORCID 0000-0001-8644-0178

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Racial disparities in cancer outcomes are well documented across tumor types. For patients with breast cancer, Black women are more likely to present with more aggressive molecular features and more likely to die from disease, even after accounting for those features. Recent efforts have been aimed at developing translational model systems for precision medicine strategies, and a major focus has been on patient-derived organoids. Organoids allow for robust in vitro experimental platforms, including drug and CRISPR screens while maintaining more complex cancer and tumor microenvironment subpopulations than cell lines. For results that are broadly translationally relevant, it is important that cancer models are derived from the spectrum of human disease and humans with disease. In this issue of Cancer Research, Madorsky Rowdo and colleagues derive breast cancer organoids from patients with African ancestry and use CRISPR-Cas9 screens to identify novel therapeutic vulnerabilities. These findings demonstrate the promise of representative cancer model systems to facilitate discoveries that are most likely to translate to improved therapy for all patients. See related article by Madorsky Rowdo et al., p. 551.

Indexed as

Breast NeoplasmsNeoplasmsTranslational Research, BiomedicalCRISPR-Cas SystemsFemaleHumansOrganoidsPrecision MedicineTumor Microenvironment

Identifiers

What OpenQuestion holds

Textmetadata
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.