DocumentCode :
3252290
Title :
Identification and characterization of gene fusions in breast cancer - A non-trivial pursuit
Author :
Varadan, Vinay ; Agrawal, Vishal ; Harris, Lyndsay ; Dimitrova, Nevenka
Author_Institution :
Philips Res. North America, Briarcliff Manor, NY, USA
fYear :
2013
fDate :
3-5 Dec. 2013
Firstpage :
109
Lastpage :
112
Abstract :
Gene fusions represent a very important class of genomic aberrations playing a significant role in certain types of cancer. In this paper we provide a method for gene fusion prioritization and assessment of their functional impact in cancer. Recent discoveries of recurrent and targetable gene fusions in breast cancer suggest the need to characterize the functional significance of such genomic aberrations within larger cohorts. Beyond existing gene fusion detection algorithms, our method identifies and prioritizes high confidence fusion calls and produces full sequence of the fused genes, annotates and visualizes protein domains included in the chimeric protein. We evaluate the sensitivity of downstream interpretive algorithms such as pathway enrichment to the statistical confidence thresholds that are parameters of the gene fusion calling algorithms in a breast cancer cohort. We show that while some pathways are strongly enriched in the results across multiple confidence cutoffs, the pathway enrichment analysis is indeed sensitive to the statistical cutoffs. This suggests that the gene fusion calling algorithms should not be considered plug-and-play tools and require great care in parameter selection before down-stream analysis and interpretation is performed.
Keywords :
RNA; cancer; data visualisation; genomics; medical computing; object detection; proteins; sensor fusion; RNAseq data; breast cancer; down-stream analysis; functional impact assessment; gene fusion calling algorithms; gene fusion detection algorithms; gene fusion prioritization; genomic aberrations; multiple confidence cutoffs; nontrivial pursuit; parameter selection; pathway enrichment analysis; plug-and-play tools; protein domain annotation; protein domain visualization; statistical confidence thresholds; statistical cutoffs; Bioinformatics; Breast cancer; Genomics; Proteins; RNA; Sequential analysis;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Global Conference on Signal and Information Processing (GlobalSIP), 2013 IEEE
Conference_Location :
Austin, TX
Type :
conf
DOI :
10.1109/GlobalSIP.2013.6736827
Filename :
6736827
Link To Document :
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