Evidence map›Paper›PMID 38589466›Full record

ArticleNature biomedical engineering2024

Histopathological biomarkers for predicting the tumour accumulation of nanomedicines.

Jan-Niklas May, Jennifer I Moss, Florian Mueller, Susanne K Golombek, Ilaria Biancacci, Larissa Rizzo, Asmaa Said Elshafei, Felix Gremse, Robert Pola, Michal Pechar and 14 more

Abstract read
In one paragraph

Article in Nature biomedical engineering, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers.

0numbers the graph read from it
0cells of the map it votes in
25citing 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

25 citing papers in PubMed.

  1. Review
  2. Review
  3. Article
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  5. Article
  6. Review
  7. Therapeutic peptides and proteins: Status and developments in drug delivery.Journal of controlled release : official journal of the Controlled Release Society · 2026
    Review
  8. Review
  9. Article
  10. Review
  11. Review
  12. Review
  13. Nanozyme-Driven Ferroptosis-Cuproptosis Interplay in Lung Cancer.International journal of nanomedicine · 2026
    Review
  14. Review
  15. Article
  16. Machine Learning-Enhanced Nanoparticle Design for Precision Cancer Drug Delivery.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025
    Review
  17. Review
  18. Metal-Organic Frameworks: Unlocking New Frontiers in Cardiovascular Diagnosis and Therapy.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025
    Review
  19. Article
  20. 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

24 authors.

Jan-Niklas May *Institute for Experimental Molecular Imaging, University Hospital RWTH Aachen, Aachen, Germany.ORCID http://orcid.org/0000-0003-0327-6267
Jennifer I Moss *Early TDE Discovery, Oncology R&D, AstraZeneca, Cambridge, UK.
Florian MuellerInstitute for Experimental Molecular Imaging, University Hospital RWTH Aachen, Aachen, Germany.ORCID http://orcid.org/0000-0002-9496-4710
Susanne K GolombekInstitute for Experimental Molecular Imaging, University Hospital RWTH Aachen, Aachen, Germany.
Ilaria BiancacciInstitute for Experimental Molecular Imaging, University Hospital RWTH Aachen, Aachen, Germany.
Larissa RizzoInstitute for Experimental Molecular Imaging, University Hospital RWTH Aachen, Aachen, Germany.
Asmaa Said ElshafeiInstitute for Experimental Molecular Imaging, University Hospital RWTH Aachen, Aachen, Germany.
Felix GremseInstitute for Experimental Molecular Imaging, University Hospital RWTH Aachen, Aachen, Germany.
Robert PolaInstitute of Macromolecular Chemistry, Czech Academy of Sciences, Prague, Czech Republic.
Michal PecharInstitute of Macromolecular Chemistry, Czech Academy of Sciences, Prague, Czech Republic.
Tomáš EtrychInstitute of Macromolecular Chemistry, Czech Academy of Sciences, Prague, Czech Republic.ORCID http://orcid.org/0000-0001-5908-5182
Svea BeckerClinic for Gastroenterology, Metabolic Disorders, and Internal Intensive Medicine, University Hospital RWTH Aachen, Aachen, Germany.
Christian TrautweinClinic for Gastroenterology, Metabolic Disorders, and Internal Intensive Medicine, University Hospital RWTH Aachen, Aachen, Germany.ORCID http://orcid.org/0000-0003-2762-8247
Roman D BülowCenter for Integrated Oncology Aachen Bonn Cologne Duesseldorf, Aachen, Germany.ORCID http://orcid.org/0000-0002-8527-7353
Peter BoorCenter for Integrated Oncology Aachen Bonn Cologne Duesseldorf, Aachen, Germany.ORCID http://orcid.org/0000-0001-9921-4284
Ruth KnuechelCenter for Integrated Oncology Aachen Bonn Cologne Duesseldorf, Aachen, Germany.
Saskia von StillfriedCenter for Integrated Oncology Aachen Bonn Cologne Duesseldorf, Aachen, Germany.ORCID http://orcid.org/0000-0002-5260-8494
Gert StormDepartment of Pharmaceutics, Utrecht University, Utrecht, the Netherlands.
Sanyogitta PuriAdvanced Drug Delivery, Pharmaceutical Sciences, R&D, AstraZeneca, Macclesfield, UK.
Simon T BarryEarly TDE Discovery, Oncology R&D, AstraZeneca, Cambridge, UK.ORCID http://orcid.org/0000-0002-8511-0588
Volkmar SchulzInstitute for Experimental Molecular Imaging, University Hospital RWTH Aachen, Aachen, Germany.
Fabian KiesslingInstitute for Experimental Molecular Imaging, University Hospital RWTH Aachen, Aachen, Germany.ORCID http://orcid.org/0000-0002-7341-0399
Marianne B AshfordAdvanced Drug Delivery, Pharmaceutical Sciences, R&D, AstraZeneca, Macclesfield, UK.ORCID http://orcid.org/0000-0001-5301-4607
Twan LammersInstitute for Experimental Molecular Imaging, University Hospital RWTH Aachen, Aachen, Germany. tlammers@ukaachen.de.ORCID http://orcid.org/0000-0002-1090-6805

Funding

Deutsche Forschungsgemeinschaft (German Research Foundation) 331065168EC | EU Framework Programme for Research and Innovation H2020 | H2020 Priority Excellent Science | H2020 European Research Council (H2020 Excellent Science - European Research Council) 864121European Research Council 864121
6 · The paper itself

Abstract

The clinical prospects of cancer nanomedicines depend on effective patient stratification. Here we report the identification of predictive biomarkers of the accumulation of nanomedicines in tumour tissue. By using supervised machine learning on data of the accumulation of nanomedicines in tumour models in mice, we identified the densities of blood vessels and of tumour-associated macrophages as key predictive features. On the basis of these two features, we derived a biomarker score correlating with the concentration of liposomal doxorubicin in tumours and validated it in three syngeneic tumour models in immunocompetent mice and in four cell-line-derived and six patient-derived tumour xenografts in mice. The score effectively discriminated tumours according to the accumulation of nanomedicines (high versus low), with an area under the receiver operating characteristic curve of 0.91. Histopathological assessment of 30 tumour specimens from patients and of 28 corresponding primary tumour biopsies confirmed the score's effectiveness in predicting the tumour accumulation of liposomal doxorubicin. Biomarkers of the tumour accumulation of nanomedicines may aid the stratification of patients in clinical trials of cancer nanomedicines.

Indexed as

DoxorubicinNanomedicineNeoplasmsAnimalsBiomarkersBiomarkers, TumorCell Line, TumorFemaleHumansMachine LearningMicePolyethylene GlycolsTumor-Associated MacrophagesXenograft Model Antitumor AssaysBiomarkersBiomarkers, TumorDoxorubicinliposomal doxorubicinPolyethylene Glycols

Identifiers

PMID38589466
PMCPMC7616664

What OpenQuestion holds

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Registered trials

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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.