ReviewCancers2026
Importance of Patient-Derived Xenograft Models in Battling Cancer Therapy Resistance.
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.
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.
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.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
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
Identifiers
What Socratic holds
Registered trials
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.