ArticleAmerican journal of cancer research2016
Successful establishment of patient-derived tumor xenografts from gastrointestinal stromal tumor-a single center experience.
Article in American journal of cancer research, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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
2 citing papers in PubMed, 2 citations in OpenAlex.
- Patient-derived xenograft models of gastrointestinal stromal tumors provide a ready-to-use platform for translational research.Disease models & mechanisms · 2025Article
- Myosin Light-Chain Kinase Inhibition Potentiates the Antitumor Effects of Avapritinib in PDGFRA D842V-Mutant Gastrointestinal Stromal Tumor.Clinical cancer research : an official journal of the American Association for Cancer Research · 2023Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Patient-derived tumor xenografts (PDTX) generally represent a kind of more reliable model of human disease, by which a potential drugs' preclinical efficacy could be evaluated. To date, no stable gastrointestinal stromal tumor (GIST) PDTX models have been reported. In this study, we aimed to establish stable GIST PDTX models and to evaluate whether these models accurately reflected the histological feature of the corresponding patient tumors and create a reliable GIST PDTX models for our future experiment. By engrafting fresh patient GIST tissues into immune-compromised mice (BALB/c athymic mice), 4 PDTX models were established. Histological features were assessed by a qualified pathologist based on H&E staining, CD117 and DOG-1. We also conduct whole exome sequencing(WES) for the 4 established GIST PDTX models to test if the model still harbored the same mutation detected in corresponding patient tumors and get a more intensive vision for the genetic profile of the models we have established, which will help a lot for our future experiment. To explore the tumorigenesis mechanism for GIST, we also have a statistical analysis for the genes detected as nonsynchronous-mutated simultaneously in 4 samples. All 4 GIST PDTX models retained the histological features of the corresponding human tumors, with original morphology type and positive stains for CD117 and DOG-1. Between the GIST PDTX models and their parental tumors, a same mutation site was detected, which confirmed the genetic consistency. The stability of molecular profiles observed within the GIST PDTX models provides confidence in the utility and translational significance of these models for in vivo testing of personalized therapies. To date, we conducted the first study to successfully establish a GIST PDTX model whose genetic profiles were revealed by whole exome sequencing. Our experience could be of great use.
Indexed as
Identifiers
27186422PMC4858539W2416618462What OpenQuestion 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 OpenQuestion graph.