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BioMed Research International
Volume 2017, Article ID 9829175, 8 pages
Research Article

RNA-seq Based Transcription Characterization of Fusion Breakpoints as a Potential Estimator for Its Oncogenic Potential

Jian-lei Gu,1,2,3 Morris Chukhman,4 Yao Lu,1,2 Cong Liu,1,2,4 Shi-yi Liu,2 and Hui Lu1,2,3,4

1Shanghai Institute of Medical Genetics, Shanghai Children’s Hospital, Shanghai Jiao Tong University, Shanghai 200040, China
2Department of Bioinformatics, SJTU-Yale Joint Center for Biostatistics, Shanghai Jiao Tong University, Shanghai 200240, China
3Key Laboratory of Molecular Embryology, Ministry of Health and Shanghai Key Laboratory of Embryo and Reproduction Engineering, Shanghai 200040, China
4Department of Bioengineering, Bioinformatics Program, University of Illinois at Chicago, Chicago, IL 60607, USA

Correspondence should be addressed to Hui Lu; moc.liamg@ofnioib.uliuh

Received 21 December 2016; Accepted 23 August 2017; Published 17 October 2017

Academic Editor: Ansgar Poetsch

Copyright © 2017 Jian-lei Gu et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


Based on high-throughput sequencing technology, the detection of gene fusions is no longer a big challenge but estimating the oncogenic potential of fusion genes remains challenging. Recent studies successfully applied machine learning methods and gene structural and functional features of fusion mutation to predict their oncogenic potentials. However, the transcription characterizations features of fusion genes have not yet been studied. In this study, based on the clonal evolution theory, we hypothesized that a fusion gene is more likely to be an oncogenic genomic alteration, if the neoplastic cells harboring this fusion mutation have larger clonal size than other neoplastic cells in a tumor. We proposed a novel method, called iFCR (internal Fusion Clone Ratio), given an estimation of oncogenic potential for fusion mutations. We have evaluated the iFCR method in three public cancer transcriptome sequencing datasets; the results demonstrated that the fusion mutations occurring in tumor samples have higher internal fusion clone ratio than normal samples. And the most frequent prostate cancer fusion mutation, TMPRSS2-ERG, appears to have a remarkably higher iFCR value in all three independent patients. The preliminary results suggest that the internal fusion clone ratio might potentially advantage current fusion mutation oncogenic potential prediction methods.