Accurate RET Fusion Detection in Solid Tumors Using RNA Sequencing Coverage Imbalance Analysis

Area of Science:

• Oncology
• Genomics
• Bioinformatics

Background:

• Accurate detection of oncogenic gene fusions is crucial for targeted cancer therapies.
• Current methods like IHC, FISH, and standard RNA-seq have limitations in sensitivity, specificity, and multiplexing capacity for fusion detection.
• RET fusions are clinically actionable targets in various cancers, but their identification is challenging.

Purpose of the Study:

• To develop and validate a novel, highly sensitive, and specific method for detecting clinically actionable REarranged during Transfection (RET) gene fusions.
• To assess the utility of RNA-sequencing (RNA-seq) exon coverage imbalance analysis for identifying RET fusions in solid tumors.
• To discover novel RET fusion partners and rare RET fusion events.

Main Methods:

• A novel approach measuring the imbalance in RNA-sequencing (RNA-seq) read coverage of 3' and 5' exons of potential fusion oncogenes was developed.
• 1327 solid tumor RNA-seq profiles were screened, including non-small cell lung cancer and thyroid cancer samples.
• RET fusion status was validated using targeted next-generation sequencing (NGS) and Sanger sequencing in selected cases.

Main Results:

• The exon coverage imbalance analysis accurately discriminated between true and false positive RET fusions, achieving 100% sensitivity and specificity with optimized thresholds.
• Validation in an independent cohort confirmed the reliability of the method.
• Among 18 RET fusion-positive samples, one rare fusion (RUFY3::RET) and two novel fusions (FN1::RET, PPP1R21::RET) were identified.

Conclusions:

• Exon coverage imbalance analysis is a robust and highly accurate method for detecting clinically relevant RET fusions.
• This approach complements existing computational RNA-seq analysis pipelines, enhancing the identification of actionable gene fusions.
• The findings support the clinical utility of this method for guiding targeted therapy decisions in oncology.