SplitFusion enables ultrasensitive gene fusion detection and reveals fusion variant-associated tumor heterogeneity.
basic_science · Level V
Where this comes from
- Record sourced from PubMed, PMID 40041857.
- Also identified by DOI 10.1016/j.patter.2025.101174 and PMC identifier 11873004.
- Licence recorded as CC BY.
- The licence permits redistribution, so the abstract is shown in full and the full text is available from the publisher.
Abstract
Gene fusions are common cancer drivers and therapeutic targets, but clinical-grade open-source bioinformatic tools are lacking. Here, we introduce a fusion detection method named SplitFusion, which is fast by leveraging Burrows-Wheeler Aligner-maximal exact match (BWA-MEM) split alignments, can detect cryptic splice-site fusions (e.g., <i>EML4::ALK</i> v3b and <i>ARv7</i>), call fusions involving highly repetitive gene partners (e.g., <i>CIC::DUX4</i>), and infer frame-ness and exon-boundary alignments for functional prediction and minimizing false positives. Using 1,848 datasets of various sizes, SplitFusion demonstrated superior sensitivity and specificity compared to three other tools. In 1,076 formalin-fixed paraffin-embedded lung cancer samples, SplitFusion identified novel fusions and revealed that <i>EML4::ALK</i> variant 3 was associated with multiple fusion variants coexisting in the same tumor. Additionally, SplitFusion can call targeted splicing variants. Using data from 515 The Cancer Genome Atlas (TCGA) samples, SplitFusion showed the highest sensitivity and uncovered two cases of <i>SLC34A2::ROS1</i> that were missed in previous studies. These capabilities make SplitFusion highly suitable for clinical applications and the study of fusion-defined tumor heterogeneity.