Evidence mapPaperPMID 42512253Full record

ReviewCancers2026

Importance of Patient-Derived Xenograft Models in Battling Cancer Therapy Resistance.

Ákos Juhász, Sára Eszter Surguta, Laura Svajda, Ivan Ranđelović, Andrea Ladányi, József Tóvári, Mihály Cserepes

Abstract readReview
In one paragraph

Review in Cancers, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

7 authors.

Ákos JuhászPharmacy, National Institute of Oncology, 1122 Budapest, Hungary.
Sára Eszter SurgutaDepartment of Experimental Pharmacology and the National Tumor Biology Laboratory, National Institute of Oncology, 1122 Budapest, Hungary.
Laura SvajdaDepartment of Experimental Pharmacology and the National Tumor Biology Laboratory, National Institute of Oncology, 1122 Budapest, Hungary.
Ivan RanđelovićDepartment of Experimental Pharmacology and the National Tumor Biology Laboratory, National Institute of Oncology, 1122 Budapest, Hungary.
Andrea LadányiDepartment of Surgical and Molecular Pathology and the National Tumor Biology Laboratory, National Institute of Oncology, 1122 Budapest, Hungary.ORCID 0000-0001-9304-8473
József TóváriDepartment of Experimental Pharmacology and the National Tumor Biology Laboratory, National Institute of Oncology, 1122 Budapest, Hungary.
Mihály CserepesDepartment of Experimental Pharmacology and the National Tumor Biology Laboratory, National Institute of Oncology, 1122 Budapest, Hungary.ORCID 0000-0003-4816-2618

Funding

Ministry for Culture and Innovation 2025-2.1.2-EKÖP-KDPNational Research Development and Innovation Fund 2022-2.1.1-NL-2022-00010National Research Development and Innovation Fund NKFIH-OTKA-K147410National Research Development and Innovation Fund NKFIH-OTKA-PD142272National Research Development and Innovation Fund TKP-2021-EGA-44
6 · The paper itself

Abstract

Cancer accounts for approximately ten million deaths annually. The majority of these are attributable to resistance-driven tumor progression and metastasis. Although increasingly effective, precise, and selective therapeutic strategies are being developed, cancer cells retain the capacity to dynamically alter their phenotype and evade treatment. Traditional in vitro approaches rely heavily on cell line monocultures; however, their limited clinical translatability has driven the development of more advanced model systems. Three-dimensional in vitro models, including spheroids, organoids, and bioprinted tissues, provide more physiologically relevant and rapid insights, but fail to capture systemic pharmacodynamics and anatomical complexity. Emerging in vivo models, such as genetically engineered mouse models (GEMMs) of carcinogenesis and patient-derived xenografts (PDXs), as well as their derived organoids, provide a more comprehensive understanding of tumor biology. The preservation of tumor heterogeneity, microenvironment, and drug sensitivity profiles has positioned PDX models as widely used platforms in both drug development and therapy response prediction. Despite limitations-including variable engraftment rates, genetic drift, lack of fully functional immune systems, ethical concerns, and high costs-PDX models, when integrated with complementary techniques, contribute significantly to identifying novel therapeutic targets and combinations. Moreover, they support clinical decision-making by enabling drug response prediction based on genetic landscapes and co-clinical response data.

Indexed as

acquired resistancecancer therapy resistancepatient-derived xenograftPDXtargeted therapeutic strategiestumor heterogeneity

Identifiers

PMID42512253
PMCPMC13406331

What Socratic holds

Textmetadata
LicenceCC BY
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 Socratic graph.