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Updated: Aug 2, 2025
Identification of Sleeping Beauty Transposon Insertions in Solid Tumors using Linker-mediated PCR
Published on: February 1, 2013
SB Digestor: a tailored driver gene identification tool for dissecting heterogeneous Sleeping Beauty
Aiping Zhang1,2, Lijian Wang1,2, Josh Haipeng Lei1,2
1Cancer Center, Faculty of Health Sciences, University of Macau, Macau SAR, China.
Abstract:
Sleeping Beauty (SB) insertional mutagenesis has been widely used for genome-wide functional screening in mouse models of human cancers, however, intertumor heterogeneity can be a major obstacle in identifying common insertion sites (CISs). Although previous algorithms have been successful in defining some CISs, they also miss CISs in certain situations. A major common characteristic of these previous methods is that they do not take tumor heterogeneity into account. However, intertumoral heterogeneity directly influences the sequence read number for different tumor samples and then affects CIS identification. To precisely detect and define cancer driver genes, we developed SB Digestor, a computational algorithm that overcomes biological heterogeneity to identify more potential driver genes. Specifically, we define the relationship between the sequenced read number and putative gene number to deduce the depth cutoff for each tumor, which can reduce tumor complexity and precisely reflect intertumoral heterogeneity. Using this new tool, we re-analyzed our previously published SB-based screening dataset and identified many additional potent drivers involved in Brca1-related tumorigenesis, including Arhgap42, Tcf12, and Fgfr2. SB Digestor not only greatly enhances our ability to identify and prioritize cancer drivers from SB tumors but also substantially deepens our understanding of the intrinsic genetic basis of cancer.
Insights
A new algorithm, SB Digestor, identifies more cancer driver genes by accounting for tumor heterogeneity in Sleeping Beauty insertional mutagenesis screens. This improves understanding of cancer genetics.
Area of Science:
- Genomics
- Cancer Biology
- Bioinformatics
Background:
- Sleeping Beauty (SB) insertional mutagenesis is crucial for cancer gene discovery in mouse models.
- Intertumor heterogeneity complicates the identification of common insertion sites (CISs) and driver genes.
- Existing algorithms often overlook CISs due to their failure to account for tumor heterogeneity.
Purpose of the Study:
- To develop a computational algorithm, SB Digestor, to overcome biological heterogeneity in CIS identification.
- To enhance the precise detection and definition of cancer driver genes from insertional mutagenesis data.
- To improve the identification of potential driver genes by considering intertumoral heterogeneity.
Main Methods:
- Developed SB Digestor, a novel computational algorithm for analyzing SB insertional mutagenesis data.
- Defined a relationship between sequenced read number and putative gene number to deduce tumor-specific depth cutoffs.
- Reduced tumor complexity and precisely reflected intertumoral heterogeneity to improve CIS identification.
Main Results:
- Re-analyzed a previously published SB screening dataset using SB Digestor.
- Identified numerous additional potent driver genes in Brca1-related tumorigenesis, including Arhgap42, Tcf12, and Fgfr2.
- Demonstrated SB Digestor's enhanced ability to identify and prioritize cancer drivers.
Conclusions:
- SB Digestor significantly improves the identification of cancer driver genes by addressing intertumor heterogeneity.
- The algorithm deepens the understanding of the genetic basis of cancer.
- SB Digestor offers a more precise method for analyzing genome-wide functional screening data in cancer research.
