Osteosarcomas are characterized by highly disrupted genomes. Although osteosarcomas lack common fusions, we find evidence of many tumour specific gene-gene fusion transcripts, likely due to chromosomal rearrangements and expression of transcription-induced chimeras. Most of the fusions result in out-of-frame transcripts, potentially capable of producing long novel protein sequences and a plethora of neoantigens. To identify fusions, we explored RNA-sequencing data to obtain detailed knowledge of transcribed fusions, by creating a novel program to compare fusions identified by deFuse to de novo transcripts generated by Trinity. This allowed us to confirm the deFuse results and identify unusual splicing patterns associated with fusion events. Using various existing tools combined with this custom program, we developed a pipeline for the identification of fusion transcripts applicable as targets for immunotherapy. In addition to identifying candidate neoantigens associated with fusions, we were able to use the pipeline to establish a method for measuring the frequency of fusion events, which correlated to patient outcome, as well as highlight some similarities between canine and human osteosarcomas. The results of this study of osteosarcomas underscores the numerous benefits associated with conducting a thorough analysis of fusion events within cancer samples.
Bibliographical noteFunding Information:
Our colleague and friend, Dr. John R. Ohlfest, now deceased, was instrumental in the planning of the initial mouse experiments and establishing our long-term goals for this project. We extend our thanks to the University of Minnesota resources involved in our project. The University of Minnesota Genomics Center provided services for RNA sequencing, oligo preparation, and Sanger sequencing. The Minnesota Supercomputing Institute provides programming services, maintains the Galaxy Software and related software tools, and provides data management services and training. Funding for this project was provided by the Children’s Cancer Research Fund, the American Cancer Society Research Professor Award (#123939), and National Cancer Institute (R01CA113636).
PubMed: MeSH publication types
- Journal Article
- Research Support, N.I.H., Extramural
- Research Support, Non-U.S. Gov't