Accurate and Efficient Detection of Gene Fusions from RNA Sequencing Data

Overview

Journal Genome Res

Specialty Genetics

Date 2021 Jan 14

PMID 33441414

Citations 174

Authors

Sebastian Uhrig

Julia Ellermann

Tatjana Walther

Pauline Burkhardt

Martina Frohlich

Barbara Hutter

Umut H Toprak

Olaf Neumann

Albrecht Stenzinger

Claudia Scholl

Stefan Frohling

Benedikt Brors

Affiliations

Soon will be listed here.

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 ( = 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 wild-type tumors and involved proteins stimulating the MAPK signaling pathway, suggesting that they substitute for activating mutations in In addition, we confirmed the transforming potential of two novel fusions, - and -, in cellular assays. These results show Arriba's utility in both basic cancer research and clinical translation.

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