Evidence map›Paper›PMID 42003567›Full record

ArticleThe Journal of pathology2026

Topological analysis of the human lymph node reticular network predicts outcome in breast cancer.

Amy M Llewellyn, Sharon L D'Costa, Cheun Yr Lam, Jasmine A Gore, Veronika Lachina, Daniel J Shewring, Sophie E Acton, Kalnisha Naidoo

Abstract read
In one paragraph

Article in The Journal of pathology, 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. Article
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

8 authors.

Amy M LlewellynTranslational Pathology, Comprehensive Cancer Centre, School of Cancer and Pharmaceutical Sciences, King's College London, London, UK.ORCID https://orcid.org/0009-0007-7023-1399
Sharon L D'CostaSynnovis, Department of Cellular Pathology, a partnership between SYNLAB UK & Ireland, Guy's and St Thomas' NHS Foundation Trust, and King's College Hospital NHS Foundation Trust, London, UK.
Cheun Yr LamSynnovis, Department of Cellular Pathology, a partnership between SYNLAB UK & Ireland, Guy's and St Thomas' NHS Foundation Trust, and King's College Hospital NHS Foundation Trust, London, UK.
Jasmine A GoreTranslational Pathology, Comprehensive Cancer Centre, School of Cancer and Pharmaceutical Sciences, King's College London, London, UK.
Veronika LachinaStromal Immunology Group, Laboratory for Molecular Cell Biology, University College London, London, UK.
Daniel J ShewringStromal Immunology Group, Laboratory for Molecular Cell Biology, University College London, London, UK.ORCID https://orcid.org/0000-0003-2380-4063
Sophie E ActonStromal Immunology Group, Laboratory for Molecular Cell Biology, University College London, London, UK.ORCID https://orcid.org/0000-0003-2704-716X
Kalnisha NaidooTranslational Pathology, Comprehensive Cancer Centre, School of Cancer and Pharmaceutical Sciences, King's College London, London, UK.ORCID https://orcid.org/0000-0002-2140-1191

Funding

Breast Cancer Now 2023·06CRTF1643Cancer Research UK RCCSCF-May22\100001
6 · The paper itself

Abstract

Axillary LNs (ALNs) initiate immune responses in breast cancer (BC) but how and when ALNs become dysfunctional, facilitating metastasis, is unclear. The fibroblastic reticular cell (FRC) network within ALNs provides structural support and mediates immune homeostasis, but we have yet to elucidate whether this network changes during BC progression. An unbiased computational approach was used to quantify features of the immunolabelled FRC network in ALNs derived from patients with BC. Platelet-derived growth factor receptor β (PDGFRβ) was identified as a robust immunomarker for human FRC and used to quantify how FRC network topology changes during BC progression and after treatment. Formalin-fixed paraffin-embedded ALNs (n = 331) from 179 patients with BC and 23 benign reactive controls were assessed for FRC network metrics, including lacunarity and branchpoints, alongside de-identified clinico-pathological data. These data were then integrated using multivariate, principal component and survival analyses. In node-negative, post-neoadjuvant chemotherapy triple-negative BC, denser FRC networks in uninvolved nodes significantly improved survival (p = 0.0365). [Correction added on 09 May 2026, after first online publication: The word 'treatment-naïve' has been corrected to 'post-neoadjuvant chemotherapy' in the preceding sentence.] Conversely, similar changes seen in node-positive BC significantly worsened survival (p = 0.0407), regardless of BC subtype or treatment. In metastatic ALNs, FRC network disruption grew proportionately to axillary tumour burden, and this significantly correlated with poorer outcomes (p = 0.043). Interestingly, increased FRC alignment within these metastases significantly improved survival (p = 0.0205). This study showed that changes in human ALN FRC network topology predicts BC prognosis. This could improve how we risk stratify patients in future, and provide a new avenue for mechanistic, translational research. © 2026 The Author(s). The Journal of Pathology published by John Wiley & Sons Ltd on behalf of The Pathological Society of Great Britain and Ireland.

Indexed as

Breast NeoplasmsFibroblastsLymph NodesTriple Negative Breast NeoplasmsAdultAxillaBiomarkers, TumorFemaleHumansLymphatic MetastasisMiddle AgedNeoadjuvant TherapyPrognosisReceptor, Platelet-Derived Growth Factor betaBiomarkers, TumorPDGFRB protein, humanReceptor, Platelet-Derived Growth Factor betaaxillary lymph nodesbreast cancerconduitsfibroblast reticular cellsimmunohistochemistryneoadjuvant chemotherapy

Identifiers

PMID42003567
PMCPMC13238348

What OpenQuestion holds

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LicenceCC BY
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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.