Junction Location Identifier (JuLI): Accurate Detection of DNA Fusions in Clinical Sequencing for Precision Oncology

Area of Science:

• Genomics
• Bioinformatics
• Oncology

Background:

• Accurate genomic fusion detection is crucial for precise oncology.
• Clinical samples often present challenges like low tumor purity and formalin-fixed, paraffin-embedded (FFPE) tissue.
• High-throughput sequencing (HTS) is a key technology for this detection.

Purpose of the Study:

• To develop and validate a novel algorithm, Junction Location Identifier (JuLI), for optimizing genomic fusion detection in high-depth clinical sequencing.
• To enhance the accuracy and reliability of fusion detection in challenging clinical samples.
• To minimize false positives in clinical settings.

Main Methods:

• Development of the JuLI algorithm with novel filtering steps.
• Validation using high-depth sequencing data from cancer cell lines and clinical samples.
• Testing with genome sequencing data from NA12878.
• Application in plasma cell-free DNA (cfDNA) analysis using joint calling.

Main Results:

• JuLI demonstrated improved performance, particularly in positive predictive value, compared to existing fusion callers on high-depth clinical sequencing data.
• The algorithm successfully identified a driver fusion that was missed (false negative) in plasma cfDNA using joint calling.
• Novel filtering steps effectively minimized false positives in the clinical context.

Conclusions:

• JuLI is an effective algorithm for accurate genomic fusion detection in high-depth clinical sequencing.
• The algorithm shows promise for improving diagnostic yield in precise oncology, especially with challenging sample types.
• JuLI's capabilities extend to liquid biopsies, enhancing the detection of clinically significant genomic alterations.