ReviewCancers2024
Murine Xenograft Models as Preclinical Tools in Endometrial Cancer Research.
Review in Cancers, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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
1 citing paper in PubMed.
- Harnessing PDX and PDX 2.0: the next-generation paradigm for precision oncology and translational breakthroughs.Molecular cancer · 2026Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
Abstract
Murine xenograft models are valuable and increasingly used preclinical tools in cancer research to understand disease pathogenesis and guide treatment options. The aim of this narrative review is to summarize the studies that employed mouse xenograft models, using cell lines, patient-derived tumors, or organoids, in endometrial cancer (EC) research, detailing their methodology and main findings. We identified 27 articles reporting on heterotopic EC xenografts, including subcutaneous, subrenal capsule, intraperitoneal, and retro-orbital models, and 18 articles using orthotopic xenografts. Subcutaneous xenografts generated using either cell lines or patient tumors have been widely used; however, their low engraftment rates and the inability to recapitulate main clinical features such as metastases limit their translational value. Subrenal capsule models showed improved engraftment rates compared to subcutaneous models, but tumors exhibited slower and constrained tumor growth. Orthotopic models are technically more challenging to generate and monitor, but tumor growth occurs in a relevant microenvironment and EC ortho-xenografts exhibit high engraftment rates and metastases to clinically relevant sites. Cell line-based xenograft (CDX) models are attractive tools because they are convenient, easy to use, and amenable to genetic modifications, making them suitable for proof-of-concept approaches and large-scale studies. EC xenografts developed from patient tumors (PDTXs) are more labor/cost-intensive for their establishment but can capture the genetic and molecular heterogeneity within and across histologic subtypes and can inform personalized patient treatment. EC organoid-based xenograft (PDOX) models combine the advantages of both CDXs and PDTXs since they are more time- and cost-effective, faithfully maintain tumor characteristics and therapeutic responses, and can be genetically modified. Despite substantial progress in EC management, there are still several unmet needs. Efficient targeted treatments are currently indicated only for a small subgroup of patients, while women with recurrent or advanced-stage EC have very few therapeutic options and their prognosis remains unfavorable. Novel (targeted) drugs, combinational regimens and tools to predict the real drug response in patients are urgently needed. Xenograft models are expected to inform about disease mechanisms and to help identify novel therapeutic options and suitable target patients.
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.