RNA-seq Fusion Detection in Clinical Oncology

Area of Science:

• Oncology
• Genomics
• Bioinformatics

Background:

• Gene fusions are key drivers in cancer development and serve as critical biomarkers.
• Traditional fusion detection methods were locus-specific, limiting comprehensive analysis.
• Next-generation sequencing (NGS) technologies like whole genome sequencing (WGS) and RNA-sequencing (RNA-seq) enable genome-wide fusion screening.

Purpose of the Study:

• To review current strategies and software tools for detecting gene fusions using RNA-sequencing data.
• To highlight the potential and challenges of RNA-seq for genome-wide fusion detection in clinical oncology.
• To assess RNA-seq as a multi-purpose tool for tumor classification and molecular analysis.

Main Methods:

• Survey of existing software tools for RNA-seq-based gene fusion detection.
• Discussion of general strategies employed by current fusion detection software.
• Analysis of technical artifacts affecting RNA-seq fusion detection sensitivity and specificity.

Main Results:

• RNA-seq allows for simultaneous detection of known and novel gene fusions.
• RNA-seq offers faster turnaround times and requires less sequencing depth compared to WGS.
• Existing software tools for RNA-seq fusion detection face challenges with sensitivity and specificity due to technical artifacts.

Conclusions:

• RNA-seq based fusion detection provides a more comprehensive and efficient approach than multiple targeted assays.
• Despite limitations, RNA-seq is a valuable tool for precision oncology when used within a broader diagnostic framework.
• Further development of RNA-seq analysis tools is needed to overcome current challenges in genome-wide fusion screening.