@article{68b00e03efb543c4b4912acc886af2f3,
title = "Pan-Cancer Analysis Reveals the Diverse Landscape of Novel Sense and Antisense Fusion Transcripts",
abstract = "Gene fusions that contribute to oncogenicity can be explored for identifying cancer biomarkers and potential drug targets. To investigate the nature and distribution of fusion transcripts in cancer, we examined the transcriptome data of about 9,000 primary tumors from 33 different cancers in TCGA (The Cancer Genome Atlas) along with cell line data from CCLE (Cancer Cell Line Encyclopedia) using ChimeRScope, a novel fusion detection algorithm. We identified several fusions with sense (canonical, 39%) or antisense (non-canonical, 61%) transcripts recurrent across cancers. The majority of the recurrent non-canonical fusions found in our study are novel, unexplored, and exhibited highly variable profiles across cancers, with breast cancer and glioblastoma having the highest and lowest rates, respectively. Overall, 4,344 recurrent fusions were identified from TCGA in this study, of which 70% were novel. Additional analysis of 802 tumor-derived cell line transcriptome data across 20 cancers revealed significant variability in recurrent fusion profiles between primary tumors and corresponding cell lines. A subset of canonical and non-canonical fusions was validated by examining the structural variation evidence in whole-genome sequencing (WGS) data or by Sanger sequencing of fusion junctions. Several recurrent fusion genes identified in our study show promise for drug repurposing in basket trials and present opportunities for mechanistic studies.",
keywords = "CCLE, ChimeRScope, TCGA, antisense fusion, cancer, fusion transcripts, pan-cancer analysis, primary tumor-cell line comparison, recurrent fusions, therapeutic targets",
author = "Vellichirammal, {Neetha Nanoth} and Abrar Albahrani and Banwait, {Jasjit K.} and Mishra, {Nitish K.} and You Li and Shrabasti Roychoudhury and Kling, {Mathew J.} and Sameer Mirza and Bhakat, {Kishor K.} and Vimla Band and Joshi, {Shantaram S.} and Chittibabu Guda",
note = "Funding Information: The authors are grateful to Sanjit Pandey for providing systems administrative support to Linux and Windows servers, and to the Bioinformatics and Systems Biology core at the University of Nebraska Medical Center (UNMC). The authors also acknowledge the Holland Computing Center of the University of Nebraska-Lincoln for computational resources, which receives support from the Nebraska Research Initiative. This work was supported by the National Institutes of Health, United States (grants 5P20GM103427, 1P30GM127200, 2P01AG029531, 5P30CA036727, and 5P30GM110768) and by the National Science Federation, United States EPSCoR Award (grant OIA-1557417). Funding Information: The authors are grateful to Sanjit Pandey for providing systems administrative support to Linux and Windows servers, and to the Bioinformatics and Systems Biology core at the University of Nebraska Medical Center (UNMC). The authors also acknowledge the Holland Computing Center of the University of Nebraska-Lincoln for computational resources, which receives support from the Nebraska Research Initiative . This work was supported by the National Institutes of Health , United States (grants 5P20GM103427 , 1P30GM127200 , 2P01AG029531 , 5P30CA036727 , and 5P30GM110768 ) and by the National Science Federation , United States EPSCoR Award (grant OIA-1557417 ). Publisher Copyright: {\textcopyright} 2020 The Authors",
year = "2020",
month = mar,
day = "6",
doi = "10.1016/j.omtn.2020.01.023",
language = "English (US)",
volume = "19",
pages = "1379--1398",
journal = "Molecular Therapy - Nucleic Acids",
issn = "2162-2531",
publisher = "Nature Publishing Group",
}