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Pegasus: a comprehensive annotation and prediction tool for detection of driver gene fusions in cancer
Francesco Abate1,2,3, Sakellarios Zairis4, Elisa Ficarra5
1Department of Biomedical Informatics, Columbia University, 1130 St. Nicholas Ave, New York, NY, 10032, USA. fa2306@columbia.edu.
Background:
The extraordinary success of imatinib in the treatment of BCR-ABL1 associated cancers underscores the need to identify novel functional gene fusions in cancer. RNA sequencing offers a genome-wide view of expressed transcripts, uncovering biologically functional gene fusions. Although several bioinformatics tools are already available for the detection of putative fusion transcripts, candidate event lists are plagued with non-functional read-through events, reverse transcriptase template switching events, incorrect mapping, and other systematic errors. Such lists lack any indication of oncogenic relevance, and they are too large for exhaustive experimental validation.
Results:
We have designed and implemented a pipeline, Pegasus, for the annotation and prediction of biologically functional gene fusion candidates. Pegasus provides a common interface for various gene fusion detection tools, reconstruction of novel fusion proteins, reading-frame-aware annotation of preserved/lost functional domains, and data-driven classification of oncogenic potential. Pegasus dramatically streamlines the search for oncogenic gene fusions, bridging the gap between raw RNA-Seq data and a final, tractable list of candidates for experimental validation.
Conclusion:
We show the effectiveness of Pegasus in predicting new driver fusions in 176 RNA-Seq samples of glioblastoma multiforme (GBM) and 23 cases of anaplastic large cell lymphoma (ALCL).
Insights
Pegasus is a new bioinformatics pipeline that identifies biologically functional gene fusions from RNA sequencing data. It filters out errors and predicts oncogenic potential, streamlining cancer research.
Area of Science:
- Genomics
- Bioinformatics
- Cancer Biology
Background:
- The success of targeted therapies like imatinib highlights the need for identifying novel cancer-driving gene fusions.
- RNA sequencing (RNA-Seq) enables genome-wide detection of expressed transcripts, including functional gene fusions.
- Existing tools for fusion transcript detection generate numerous false positives, hindering experimental validation.
Purpose of the Study:
- To develop a robust bioinformatics pipeline for accurate annotation and prediction of biologically functional gene fusion candidates.
- To improve the identification of oncogenic gene fusions by filtering out non-functional events and errors.
Main Methods:
- Implementation of the Pegasus pipeline, integrating multiple gene fusion detection tools.
- Reconstruction of novel fusion proteins and reading-frame-aware annotation of functional domains.
- Development of a data-driven classification system for predicting oncogenic potential.
Main Results:
- Pegasus effectively annotates and predicts biologically functional gene fusion candidates.
- The pipeline reconstructs fusion proteins and assesses the impact on functional domains.
- Pegasus streamlines the process from raw RNA-Seq data to a validated list of candidate oncogenic fusions.
Conclusions:
- Pegasus demonstrates effectiveness in identifying novel driver fusions.
- The pipeline was validated using 176 RNA-Seq samples from glioblastoma multiforme (GBM) and 23 from anaplastic large cell lymphoma (ALCL).
- Pegasus facilitates the discovery of clinically relevant gene fusions for cancer treatment.
