Combining panel-based and whole-transcriptome-based gene fusion detection by long-read sequencing

Area of Science:

• Genomics
• Cancer Research
• Bioinformatics

Background:

• Gene fusions (GFs) are critical drivers in various cancers.
• Accurate detection of GFs is essential for targeted therapy.
• Current methods may have limitations in sensitivity and scope.

Purpose of the Study:

• To develop and validate a comprehensive gene fusion detection and analysis workflow.
• To combine targeted panel-based and whole-transcriptome long-read sequencing for enhanced GF discovery.
• To improve turnaround times and identify novel GFs in challenging cancer cases.

Main Methods:

• Adaptation of a short-read cancer fusion panel for long-read sequencing (Oxford Nanopore Technologies).
• Application of panel-based long-read sequencing for known GF detection.
• Whole-transcriptome long-read sequencing analysis of panel-negative glioma samples.
• Development of tailored computational pipelines for GF analysis.

Main Results:

• Successful detection of known GFs using the adapted panel and long-read sequencing, with reduced turnaround times.
• Identification of 20 novel candidate GFs in 24 panel-negative glioma samples.
• Experimental validation of all identified candidate GFs.
• Demonstrated compatibility of long-read sequencing for GF detection.

Conclusions:

• The presented workflow integrates panel-based and whole-transcriptome long-read sequencing for comprehensive GF detection.
• This approach enables fast and accurate identification of both known and novel gene fusions in cancer.
• The workflow is effective for clinically challenging cases, including panel-negative samples.