Identification of candidate neoantigens produced by fusion transcripts in human osteosarcomas

Area of Science:

• Oncology
• Genomics
• Bioinformatics

Background:

• Osteosarcomas exhibit highly disrupted genomes with numerous chromosomal rearrangements.
• While common gene fusions are rare, tumor-specific gene-gene fusion transcripts are prevalent, likely arising from these rearrangements.
• These fusions can produce novel protein sequences and neoantigens, offering potential therapeutic targets.

Purpose of the Study:

• To identify and characterize tumor-specific gene fusion transcripts in osteosarcomas.
• To develop a computational pipeline for detecting fusion transcripts and associated neoantigens.
• To investigate the correlation between fusion event frequency and patient outcomes.

Main Methods:

• Utilized RNA-sequencing data to analyze transcribed fusions.
• Developed a novel computational program to compare existing fusion-detection tools (deFuse) with de novo transcript assembly (Trinity).
• Integrated multiple bioinformatics tools into a pipeline for fusion transcript identification and analysis.

Main Results:

• Confirmed gene fusion events in osteosarcoma RNA-sequencing data and identified unusual splicing patterns.
• Developed a pipeline for identifying fusion transcripts as potential immunotherapy targets.
• Identified candidate neoantigens arising from fusion transcripts and established a method to quantify fusion event frequency.
• Observed a correlation between fusion event frequency and patient outcomes, and noted similarities between canine and human osteosarcomas.

Conclusions:

• Thorough analysis of fusion events in cancer samples provides significant benefits.
• Fusion transcripts represent a valuable source of neoantigens for targeted immunotherapies in osteosarcoma.
• The developed pipeline aids in identifying fusion transcripts, assessing their frequency, and correlating them with clinical outcomes.