Abstract 7418: LongFuse: Detecting gene fusion transcripts from high throughput long-read single cell RNA sequencing data

Abstract

Abstract Gene fusion resulted from genomic translocation plays important oncogenic roles including but not limited to gene activation and changing protein coding sequences. Historically, fusion gene analysis has been limited to bulk levels resulting in mixed oncogene activation models. Single cell Nanopore RNA sequencing (scNanoRNAseq) has the unique strength in detecting full-length chimeric transcripts in thousands of single cells in parallel. However, robust computational tools for detecting fusion genes in single cells from scNanoRNAseq data are missing. Here, we developed LongFuse, a fast and accurate tool, to detect fusion genes and their splicing isoforms in single cells from scNanoRNAseq data. LongFuse implements an XOR logic gate algorithm to speed up fusion candidate searching and stringent criteria to eliminate false discoveries due to trans-splicing events and randomly ligated chimeras. We applied LongFUSE onto scNanoRNAseq data of both human prostate cancer cell line, VCaP, and tumor tissues with known TMPRSS2-ERG fusion. Our results showed that both cancer cell line and prostate tumors harbored a mixture of fusion positive and fusion negative tumor cells. Gene expression analysis revealed transcriptional differences between fusion positive and negative cells including genes involved in cancer cell proliferation and invasion programs such as ELL2, ZBTB16, IGF1R, ANK3, MYPOP and PLPP1 genes. LongFUSE also calculates the splicing isoforms of fusion gene transcripts, allowing prediction of protein sequences of fusion gene transcripts. Taken together, our data demonstrated that LongFUSE is a robust computational tool for detecting gene fusions and their splicing isoforms at single cell level from scNanoRNAseq data. Citation Format: Cheng-Kai Shiau, Yueying He, Hsiao-Yun Lin, Lina Lu, Xiao-Dong Lu, Jindan Yu, Ruli Gao. LongFuse: Detecting gene fusion transcripts from high throughput long-read single cell RNA sequencing data [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 7418.