Evidence map›Paper›PMID 40710388›Full record

ReviewJournal of personalized medicine2025

Deciphering Breast Tumor Heterogeneity Through Patient-Derived Organoids and Circulating Tumor Cells.

Benedetta Policastro, Nikoline Nissen, Carla L Alves

Abstract readReview
In one paragraph

Review in Journal of personalized medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing 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

4 citing papers in PubMed.

  1. Article
  2. Review
  3. Review
  4. 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

3 authors.

Benedetta PolicastroCancer Research Unit, Department of Molecular Medicine, University of Southern Denmark, 5000 Odense, Denmark.ORCID 0009-0005-1055-4503
Nikoline NissenCancer Research Unit, Department of Molecular Medicine, University of Southern Denmark, 5000 Odense, Denmark.
Carla L AlvesCancer Research Unit, Department of Molecular Medicine, University of Southern Denmark, 5000 Odense, Denmark.ORCID 0000-0003-2266-9349

Funding

A.P. Møller foundation 2024-00976Danish Cancer Research Foundation 0Pink Tribute 10205
6 · The paper itself

Abstract

Breast cancer is a highly heterogeneous disease, with tumors capable of adapting to shifting conditions, making the development of effective personalized therapies particularly challenging. Patient-derived models, such as patient-derived organoids (PDOs) and circulating tumor cell (CTC) cultures, have emerged as powerful tools for investigating intra- and inter-tumor heterogeneity. These models largely retain the genetic, phenotypic, and microenvironmental features of the original tumors, providing valuable insights into disease progression, drug response, and resistance mechanisms. Furthermore, by enabling tumors' spatiotemporal molecular profiling, PDOs and CTCs offer a dynamic approach to assess treatment efficacy over time. However, to fully capture the complexity of breast cancer heterogeneity, it is required to develop models from multiple tumor and blood samples collected throughout the course of treatment. This review explores the potential of integrating PDOs and CTC models to better understand intra-tumor heterogeneity while addressing key challenges in developing patient-derived models that accurately recapitulate patients' tumors to advance personalized care. The integration of PDOs and CTCs could represent a paradigm shift in the personalized management of metastatic breast cancer.

Indexed as

breast cancercirculating tumor cellsepithelial–mesenchymal transitionpatient-derived organoidstreatment resistancetumor heterogeneitytumor microenvironment

Identifiers

PMID40710388
PMCPMC12299329

What OpenQuestion holds

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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.