Fusion genes are well-known cancer drivers. However, very few known oncogenic fusions involve non-coding sequences. We develop SFyNCS with superior performance to detect fusions of both protein-coding genes and non-coding sequences from transcriptomic sequencing data. We validate fusions using somatic structural variations detected from the genomes. This allows us to comprehensively evaluate various fusion detection and filtering strategies and parameters. We detect 165,139 fusions in 9,565 tumor samples across 33 tumor types in the Cancer Genome Atlas cohort. Among them, 72% of the fusions involve non-coding sequences and many are recurrent. We discover two long non-coding RNAs recurrently fused with various partner genes in 32% of dedifferentiated liposarcomas and experimentally validated the oncogenic functions in mouse model.

Download full-text PDF

Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC10104044PMC
http://dx.doi.org/10.1101/2023.04.03.535462DOI Listing

Publication Analysis

Top Keywords

non-coding sequences
16
oncogenic fusions
8
fusions involve
8
involve non-coding
8
fusions
6
non-coding
5
sfyncs detects
4
detects oncogenic
4
fusions involving
4
involving non-coding
4

Similar Publications

Want AI Summaries of new PubMed Abstracts delivered to your In-box?

Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!