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Co-fuse: a new class discovery analysis tool to identify and prioritize recurrent fusion genes from RNA-sequencing data

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Abstract

Recurrent oncogenic fusion genes play a critical role in the development of various cancers and diseases and provide, in some cases, excellent therapeutic targets. To date, analysis tools that can identify and compare recurrent fusion genes across multiple samples have not been available to researchers. To address this deficiency, we developed Co-occurrence Fusion (Co-fuse), a new and easy to use software tool that enables biologists to merge RNA-seq information, allowing them to identify recurrent fusion genes, without the need for exhaustive data processing. Notably, Co-fuse is based on pattern mining and statistical analysis which enables the identification of hidden patterns of recurrent fusion genes. In this report, we show that Co-fuse can be used to identify 2 distinct groups within a set of 49 leukemic cell lines based on their recurrent fusion genes: a multiple myeloma (MM) samples-enriched cluster and an acute myeloid leukemia (AML) samples-enriched cluster. Our experimental results further demonstrate that Co-fuse can identify known driver fusion genes (e.g., IGH-MYC, IGH-WHSC1) in MM, when compared to AML samples, indicating the potential of Co-fuse to aid the discovery of yet unknown driver fusion genes through cohort comparisons. Additionally, using a 272 primary glioma sample RNA-seq dataset, Co-fuse was able to validate recurrent fusion genes, further demonstrating the power of this analysis tool to identify recurrent fusion genes. Taken together, Co-fuse is a powerful new analysis tool that can be readily applied to large RNA-seq datasets, and may lead to the discovery of new disease subgroups and potentially new driver genes, for which, targeted therapies could be developed. The Co-fuse R source code is publicly available at https://github.com/sakrapee/co-fuse.

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Acknowledgements

C. H. K is a recipient of Mary Overton research fellowship.

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Authors and Affiliations

  1. School of Computer Science, The University of Adelaide, Adelaide, 5005, Australia

    Sakrapee Paisitkriangkrai

  2. Department of Thoracic/Head and Neck Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, 77030, USA

    Kelly Quek

  3. Department of Genomic Medicine, The University of Texas MD Anderson Cancer Center, Houston, TX, 77030, USA

    Kelly Quek

  4. Division of Stem Cells and Cancer, German Cancer Research Center (DKFZ), 69120, Heidelberg, Germany

    Eva Nievergall

  5. Australian Centre for Blood Diseases, Monash University, Melbourne, 3800, Australia

    Anissa Jabbour

  6. Department of Clinical Haematology, The Alfred Hospital, Melbourne, 3004, Australia

    Anissa Jabbour

  7. Department of Medical Biology, The University of Melbourne, Melbourne, 3010, Australia

    Anissa Jabbour

  8. Faculty of Health and Medical Sciences, Adelaide Medical School, The University of Adelaide, Adelaide, 5005, Australia

    Andrew Zannettino & Chung Hoow Kok

  9. Cancer Theme, South Australian Health & Medical Research Institute, Adelaide, 5000, Australia

    Andrew Zannettino & Chung Hoow Kok

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  1. Sakrapee Paisitkriangkrai
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Correspondence to Chung Hoow Kok.

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Conflict of interest

Sakrapee Paisitkriangkrai declares that he has no conflict of interest. Kelly Quek declares that she has no conflict of interest. Eva Nievergall declares that she has no conflict of interest. Anissa Jabbour declares that she has no conflict of interest. Andrew Zannettino declares that he has no conflict of interest. Chung H Kok declares that he has no conflict of interest.

Ethical approval

This article does not contain any studies with human participants or animals performed by any of the authors.

Data availability

The RNA-seq leukemic cell lines dataset analysed during the current study is publicly available at Cancer Genomics Hub (http://cghub.ucsc.edu) and NCI Genomic Data Commons (https://gdc.nci.nih.gov/). The raw sequencing data for 272 gliomas clinical samples dataset analysed during the current study is publicly available at NCBI Gene Expression Omnibus (GEO; http://www.ncbi.nlm.nih.gov/geo/) under accession number GSE48865. The raw FusionCatcher analysis results generated during this study are included in this published article as supplementary information files.

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Communicated by S. Hohmann.

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Paisitkriangkrai, S., Quek, K., Nievergall, E. et al. Co-fuse: a new class discovery analysis tool to identify and prioritize recurrent fusion genes from RNA-sequencing data. Mol Genet Genomics 293, 1217–1229 (2018). https://doi.org/10.1007/s00438-018-1454-1

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