Find Your Next PDX Model
Using appropriate Patient-Derived Xenograft (PDX) models in a specific indication, or with a specific gene mutation or amplification is the first step in optimizing preclinical and translational oncology research.
CrownBio has developed a powerful yet easy to use online database, HuBase, for accessing, mining, and managing the wealth of available HuPrime® and HuKemia® PDX model data.
The Worlds Only Database Of Its Kind
Our unique and dynamic searchable database collates all of the:
- Genotypic
- Pharmacological
- Patient clinical data available for our PDX models
- Regularly updated with new model information.
Quickly Browse or Search PDX Models to Meet Your Research Needs
HuBase enables scientists around the world to quickly and easily search for models that meet their specific research needs. Models can be browsed (with PDX collated by tumor type) and selecting an individual model displays all data on:
- Tumor profile
- Patient medical history (including treatment history and tumor biopsy site) and demographics
- PDX molecular profile
- Growth characteristics
- Response to oncology agents available
- TMA availability
- Associated publications
Searching for models of interest can be as basic or in depth as individual clients require. Simple model searches are based on PDX with standard of care data available, or for models treated with a specific agent of choice. Models can also be stratified by known hotspot mutations.
Search Through In-Depth Genotypic Characterization Data, Easily Export Your Results
HuBase also contains more powerful search options, allowing clients to find models of interest based on:
- Gene expression
- Copy number
- miRNA expression
- Mutation
- Gene fusion, or combinations of these features.
Model Selection Made Easy by HuBase.
Gene Expression Microarray Searching for EGFR Expression,
with Models Stratified by Expression Levels ≥8 and ≤3
With all data easily exportable in both graphical and tabular form, HuBase provides an essential tool for the rapid discovery of appropriate and predictive preclinical models.
