RT Journal
A1 Uhrig, Sebastian
A1 Ellermann, Julia
A1 Walther, Tatjana
A1 Burkhardt, Pauline
A1 Fröhlich, Martina
A1 Hutter, Barbara
A1 Toprak, Umut H.
A1 Neumann, Olaf
A1 Stenzinger, Albrecht
A1 Scholl, Claudia
A1 Fröhling, Stefan
A1 Brors, Benedikt
T1 Accurate and efficient detection of gene fusions from RNA sequencing data
JF Genome Research 
JO Genome Research 
YR 2021 
FD March 01 
VO 31 
IS 3 
SP 448 
OP 460 
DO 10.1101/gr.257246.119 
UL http://genome.cshlp.org/content/31/3/448.abstract 
AB The identification of gene fusions from RNA sequencing data is a routine task in cancer research and precision oncology. However, despite the availability of many computational tools, fusion detection remains challenging. Existing methods suffer from poor prediction accuracy and are computationally demanding. We developed Arriba, a novel fusion detection algorithm with high sensitivity and short runtime. When applied to a large collection of published pancreatic cancer samples (n = 803), Arriba identified a variety of driver fusions, many of which affected druggable proteins, including ALK, BRAF, FGFR2, NRG1, NTRK1, NTRK3, RET, and ROS1. The fusions were significantly associated with KRAS wild-type tumors and involved proteins stimulating the MAPK signaling pathway, suggesting that they substitute for activating mutations in KRAS. In addition, we confirmed the transforming potential of two novel fusions, RRBP1-RAF1 and RASGRP1-ATP1A1, in cellular assays. These results show Arriba's utility in both basic cancer research and clinical translation.