Evidence map›Paper›PMID 37596623›Full record

ArticleJournal of experimental & clinical cancer research : CR2023

Breast cancer patient-derived microtumors resemble tumor heterogeneity and enable protein-based stratification and functional validation of individualized drug treatment.

Nicole Anderle, Felix Schäfer-Ruoff, Annette Staebler, Nicolas Kersten, André Koch, Cansu Önder, Anna-Lena Keller, Simone Liebscher, Andreas Hartkopf, Markus Hahn and 4 more

Abstract read
In one paragraph

Article in Journal of experimental & clinical cancer research : CR, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing 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

6 citing papers in PubMed.

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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

14 authors.

Nicole AnderleNMI Natural and Medical Sciences Institute at the University of Tuebingen, 72770, Reutlingen, Germany. nicole.anderle@nmi.de.
Felix Schäfer-RuoffNMI Natural and Medical Sciences Institute at the University of Tuebingen, 72770, Reutlingen, Germany.
Annette StaeblerInstitute of Pathology and Neuropathology, Eberhard Karls University Tuebingen, 72076, Tuebingen, Germany.
Nicolas KerstenInterfaculty Institute for Bioinformatics and Medical Informatics (IBMI), Eberhard Karls University Tuebingen, Tuebingen, 72076, Germany.
André KochDepartment of Women's Health, University Women's Hospital, Eberhard Karls University Tuebingen, 72076, Tuebingen, Germany.
Cansu ÖnderDepartment of Women's Health, University Women's Hospital, Eberhard Karls University Tuebingen, 72076, Tuebingen, Germany.
Anna-Lena KellerNMI Natural and Medical Sciences Institute at the University of Tuebingen, 72770, Reutlingen, Germany.
Simone LiebscherInstitute of Biomedical Engineering, Department for Medical Technologies and Regenerative Medicine, Eberhard Karls University Tuebingen, 72076, Tuebingen, Germany.
Andreas HartkopfDepartment of Women's Health, University Women's Hospital, Eberhard Karls University Tuebingen, 72076, Tuebingen, Germany.
Markus HahnDepartment of Women's Health, University Women's Hospital, Eberhard Karls University Tuebingen, 72076, Tuebingen, Germany.
Markus TemplinNMI Natural and Medical Sciences Institute at the University of Tuebingen, 72770, Reutlingen, Germany.
Sara Y BruckerDepartment of Women's Health, University Women's Hospital, Eberhard Karls University Tuebingen, 72076, Tuebingen, Germany.
Katja Schenke-LaylandNMI Natural and Medical Sciences Institute at the University of Tuebingen, 72770, Reutlingen, Germany.
Christian SchmeesNMI Natural and Medical Sciences Institute at the University of Tuebingen, 72770, Reutlingen, Germany. christian.schmees@nmi.de.ORCID http://orcid.org/0000-0003-4883-2642

Funding

Ministerium für Wirtschaft, Arbeit und Wohnungsbau Baden-Württemberg 3-4332.62-HSG/84
6 · The paper itself

Abstract

Despite tremendous progress in deciphering breast cancer at the genomic level, the pronounced intra- and intertumoral heterogeneity remains a major obstacle to the advancement of novel and more effective treatment approaches. Frequent treatment failure and the development of treatment resistance highlight the need for patient-derived tumor models that reflect the individual tumors of breast cancer patients and allow a comprehensive analyses and parallel functional validation of individualized and therapeutically targetable vulnerabilities in protein signal transduction pathways. Here, we introduce the generation and application of breast cancer patient-derived 3D microtumors (BC-PDMs). Residual fresh tumor tissue specimens were collected from n = 102 patients diagnosed with breast cancer and subjected to BC-PDM isolation. BC-PDMs retained histopathological characteristics, and extracellular matrix (ECM) components together with key protein signaling pathway signatures of the corresponding primary tumor tissue. Accordingly, BC-PDMs reflect the inter- and intratumoral heterogeneity of breast cancer and its key signal transduction properties. DigiWest®-based protein expression profiling of identified treatment responder and non-responder BC-PDMs enabled the identification of potential resistance and sensitivity markers of individual drug treatments, including markers previously associated with treatment response and yet undescribed proteins. The combination of individualized drug testing with comprehensive protein profiling analyses of BC-PDMs may provide a valuable complement for personalized treatment stratification and response prediction for breast cancer.

Indexed as

Breast NeoplasmsBreastFemaleGenomicsHumansSignal TransductionAnti-cancer drug efficacyBreast cancerPreclinical tumor modelProtein profilingTherapy resistanceTherapy sensitivityTumor heterogeneity

Identifiers

PMID37596623
PMCPMC10436441

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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.