The Human tumor model-based databank shared resource assists University of Colorado Cancer Center investigators to establish their advanced disease specific models, and will develop a coordinated effort to create an –omics database that can be used for integrative analyses and queried remotely. Currently there are several-characterized disease-specific direct patient tumor models (DPTM), totaling close to 250 unique types. While it would be redundant to replicate this, a coordinated effort to give access to investigators to the multi-platform genomics data that is being generated is a unique opportunity.
The long-term goals are to:
- Assist investigators to establish new DPTMs.
- Standardize strategies and virtual databanks from all genomics platforms.
- Use an online querying tool to enable remote access to the physical and virtual datasets by investigators.
The Shared Resources plan to incorporate the following platforms:
- Dnaseq and Exome sequencing.
The Shared Resources plan to establish the following layers of biological data:
- Tumor data from DPTMs.
- Paired normal data.
- Paired intra-tumoral stroma from DPTMs.
- Normal tissue controls.
- Cell lines.
- Mouse normal and tumor controls.
The Shared Resources have developed an integrated informatics approach, which will store the biological data, and bioinformatics tools and software to integrate these biological data across platforms, and anticipate a web-based interactive query system will be deployed for end-users.
The Shared Resource will also collect and integrate public data sources (such as TCGA data or Cancer Cell Lines Encyclopedia data) into this integrated informatics platform. Creating the tools to standardize and “make talk” different platforms is an efficient way to use already acquired genomics data. This enables generation of multi-level and multi-layered strategies to connect existing datasets, and to plug-in new batches of data as it is generated by other Shared Resources.
This will facilitate the investigation of:
- Animal models of cancer.
- Adapt to the idiosyncrasy of each disease area.
- Find commonalities that enable true genomics-driven (as opposed to anatomically-driven) treatments and drug development.