DEVELOPMENT OF A GENE FUSION DETECTION VALIDATION FRAMEWORK FOR LONG-READ RNA SEQUENCING USING ALIGNMENT EVIDENCE AND MACHINE LEARNING

Abstract

Gene fusions are critical drivers of cancer and serve as diagnostic and therapeutic biomarkers. Detecting them reliably from long-read RNA sequencing (RNA-Seq) data remains challenging due to high error rates and complex transcript structures. Current methods often depend on matched whole-genome sequencing (WGS) data, which may be unavailable or uninformative when fusions are expressed without clear genomic breakpoints. To address this, a long-read fusion validation pipeline was developed, optimized for transcript-level evidence by removing reliance on genomic data and focusing on functionally expressed fusions. The pipeline integrates alignment support from realigned soft-clipped reads, supplementary alignments, and full-length chimeric reads to validate transcripts. A Random Forest model was further trained using features derived from validated events to refine classification. Applied to five cancer cell line datasets, with emphasis on breast cancer, the pipeline achieved a 68.1% overall validation rate and 77.8% in MCF7. It distinguished true fusions, deprioritized database-reported false positives, and highlighted high-confidence novel candidates. Known fusions such as BCAS4–BCAS3 were confirmed, while MOV10–RHOC emerged as a biologically relevant novel fusion supported by multiple evidence types and recurrent in MCF7 and K562. Another candidate, CLUL1–TYMS, detected across four lines, likely represents a transcriptional read-through.Benchmarking against experimentally validated fusion transcripts, rather than DNA-based tools, established a transcript-focused alternative for fusion discovery. This dataset will be made publicly available to support benchmarking and machine learning research. The framework enables high-confidence detection of transcript-level fusions in cancer and shows strong potential for biomarker discovery and precision oncology.