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SBCDDB: Sleeping Beauty Cancer Driver Database for gene discovery in mouse models of human cancers
Justin Y Newberg1,2, Karen M Mann1,2, Michael B Mann1,2
1Cancer Research Program, Houston Methodist Research Institute, Houston, Texas, USA.
Abstract:
Large-scale oncogenomic studies have identified few frequently mutated cancer drivers and hundreds of infrequently mutated drivers. Defining the biological context for rare driving events is fundamentally important to increasing our understanding of the druggable pathways in cancer. Sleeping Beauty (SB) insertional mutagenesis is a powerful gene discovery tool used to model human cancers in mice. Our lab and others have published a number of studies that identify cancer drivers from these models using various statistical and computational approaches. Here, we have integrated SB data from primary tumor models into an analysis and reporting framework, the Sleeping Beauty Cancer Driver DataBase (SBCDDB, http://sbcddb.moffitt.org), which identifies drivers in individual tumors or tumor populations. Unique to this effort, the SBCDDB utilizes a single, scalable, statistical analysis method that enables data to be grouped by different biological properties. This allows for SB drivers to be evaluated (and re-evaluated) under different contexts. The SBCDDB provides visual representations highlighting the spatial attributes of transposon mutagenesis and couples this functionality with analysis of gene sets, enabling users to interrogate relationships between drivers. The SBCDDB is a powerful resource for comparative oncogenomic analyses with human cancer genomics datasets for driver prioritization.
Insights
The Sleeping Beauty Cancer Driver DataBase (SBCDDB) identifies rare cancer drivers using insertional mutagenesis data. This resource aids in understanding druggable pathways by analyzing drivers in various biological contexts.
Area of Science:
- Oncogenomics
- Cancer Biology
- Bioinformatics
Background:
- Large-scale oncogenomic studies reveal numerous infrequently mutated cancer drivers.
- Understanding the biological context of rare drivers is crucial for identifying druggable cancer pathways.
- Sleeping Beauty (SB) insertional mutagenesis is a key tool for modeling human cancers and discovering gene drivers.
Purpose of the Study:
- To integrate Sleeping Beauty (SB) insertional mutagenesis data into a comprehensive analysis and reporting framework.
- To develop a database (SBCDDB) for identifying cancer drivers in individual tumors and populations.
- To enable evaluation and re-evaluation of SB drivers within different biological contexts.
Main Methods:
- Integrated SB data from primary tumor models into the Sleeping Beauty Cancer Driver DataBase (SBCDDB).
- Utilized a single, scalable, statistical analysis method for driver identification.
- Incorporated visual representations of transposon mutagenesis and gene set analysis functionalities.
Main Results:
- The SBCDDB identifies cancer drivers from SB insertional mutagenesis data.
- The database allows grouping of data by biological properties for contextual driver analysis.
- Visualizations highlight spatial attributes of mutagenesis and enable interrogation of driver relationships.
Conclusions:
- The SBCDDB is a valuable resource for comparative oncogenomic analyses.
- It facilitates driver prioritization by integrating SB data with human cancer genomics datasets.
- This framework enhances the understanding of rare driving events and their biological significance in cancer.
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