FUNGI: FUsioN Gene Integration toolset

Abstract Motivation Fusion genes are both useful cancer biomarkers and important drug targets. Finding relevant fusion genes is challenging due to genomic instability resulting in a high number of passenger events. To reveal and prioritize relevant gene fusion events we have developed FUsionN Gene Identification toolset (FUNGI) that uses an ensemble of fusion detection algorithms with prioritization and visualization modules. Results We applied FUNGI to an ovarian cancer dataset of 107 tumor samples from 36 patients. Ten out of 11 detected and prioritized fusion genes were validated. Many of detected fusion genes affect the PI3K-AKT pathway with potential role in treatment resistance. Availabilityand implementation FUNGI and its documentation are available at https://bitbucket.org/alejandra_cervera/fungi as standalone or from Anduril at https://www.anduril.org. Supplementary information Supplementary data are available at Bioinformatics online.

[1]  Li Ding,et al.  Driver Fusions and Their Implications in the Development and Treatment of Human Cancers , 2018, Cell reports.

[2]  A. Santiago-Walker,et al.  Oncogenic Characterization and Pharmacologic Sensitivity of Activating Fibroblast Growth Factor Receptor (FGFR) Genetic Alterations to the Selective FGFR Inhibitor Erdafitinib , 2017, Molecular Cancer Therapeutics.

[3]  B. Haas,et al.  Accuracy assessment of fusion transcript detection via read-mapping and de novo fusion transcript assembly-based methods , 2019, Genome Biology.

[4]  Benjamin E. Gross,et al.  The cBio cancer genomics portal: an open platform for exploring multidimensional cancer genomics data. , 2012, Cancer discovery.

[5]  T. Taxter,et al.  FGFR3-TACC3 fusion in solid tumors: mini review , 2016, Oncotarget.

[6]  Alicia Oshlack,et al.  Clinker: visualizing fusion genes detected in RNA-seq data , 2017, bioRxiv.

[7]  Helga Thorvaldsdóttir,et al.  Integrative Genomics Viewer (IGV): high-performance genomics data visualization and exploration , 2012, Briefings Bioinform..

[8]  Jin Zhang,et al.  INTEGRATE-Vis: a tool for comprehensive gene fusion visualization , 2017, Scientific Reports.

[9]  Elisa Ficarra,et al.  DEEPrior: a deep learning tool for the prioritization of gene fusions , 2020, Bioinform..

[10]  S. Mabuchi,et al.  The PI3K/AKT/mTOR pathway as a therapeutic target in ovarian cancer. , 2015, Gynecologic oncology.

[11]  Jun Wang,et al.  SOAPfuse: an algorithm for identifying fusion transcripts from paired-end RNA-Seq data , 2013, Genome Biology.

[12]  B. Johansson,et al.  The emerging complexity of gene fusions in cancer , 2015, Nature Reviews Cancer.

[13]  Chris Wiggins,et al.  Pegasus: a comprehensive annotation and prediction tool for detection of driver gene fusions in cancer , 2014, BMC Systems Biology.

[14]  Mikhail Shugay,et al.  Oncofuse: a computational framework for the prediction of the oncogenic potential of gene fusions , 2013, Bioinform..

[15]  Julia Casado,et al.  Anduril 2: upgraded large-scale data integration framework , 2019, Bioinform..

[16]  O. Kallioniemi,et al.  FusionCatcher – a tool for finding somatic fusion genes in paired-end RNA-sequencing data , 2014, bioRxiv.

[17]  Christopher A. Maher,et al.  ChimeraScan: a tool for identifying chimeric transcription in sequencing data , 2011, Bioinform..

[18]  Astrid Gall,et al.  Ensembl 2019 , 2018, Nucleic Acids Res..

[19]  Alberto Magi,et al.  Discovering chimeric transcripts in paired-end RNA-seq data by using EricScript , 2012, Bioinform..

[20]  Ming Tang,et al.  TumorFusions: an integrative resource for cancer-associated transcript fusions , 2017, Nucleic Acids Res..

[21]  Krishanpal Anamika,et al.  FusionHub: A unified web platform for annotation and visualization of gene fusion events in human cancer , 2018, PloS one.

[22]  Benjamin J. Raphael,et al.  Integrated Genomic Analyses of Ovarian Carcinoma , 2011, Nature.

[23]  A. Drilon,et al.  Fusions in solid tumours: diagnostic strategies, targeted therapy, and acquired resistance , 2017, Nature Reviews Clinical Oncology.

[24]  Timothy L. Tickle,et al.  STAR-Fusion: Fast and Accurate Fusion Transcript Detection from RNA-Seq , 2017, bioRxiv.