| Literature DB >> 33441414 |
Sebastian Uhrig1,2,3,4, Julia Ellermann4,5, Tatjana Walther5, Pauline Burkhardt1,3,4, Martina Fröhlich1,2,3, Barbara Hutter1,2,3, Umut H Toprak3,6, Olaf Neumann7, Albrecht Stenzinger3,7,8, Claudia Scholl3,9, Stefan Fröhling3,5,10, Benedikt Brors1,3,10.
Abstract
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.Entities:
Mesh：
Substances：
Year: 2021 PMID： 33441414 PMCID： PMC7919457 DOI： 10.1101/gr.257246.119
Source DB: PubMed Journal: Genome Res ISSN： 1088-9051 Impact factor: 9.043