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An integrative approach to reveal driver gene fusions from paired-end sequencing data in cancer

Nature Biotechnology volume 27, pages 1005–1011 (2009)Cite this article

Abstract

Cancer genomes contain many aberrant gene fusions—a few that drive disease and many more that are nonspecific passengers. We developed an algorithm (the concept signature or 'ConSig' score) that nominates biologically important fusions from high-throughput data by assessing their association with 'molecular concepts' characteristic of cancer genes, including molecular interactions, pathways and functional annotations. Copy number data supported candidate fusions and suggested a breakpoint principle for intragenic copy number aberrations in fusion partners. By analyzing lung cancer transcriptome sequencing and genomic data, we identified a novel R3HDM2-NFE2 fusion in the H1792 cell line. Lung tissue microarrays revealed 2 of 76 lung cancer patients with genomic rearrangement at the NFE2 locus, suggesting recurrence. Knockdown of NFE2 decreased proliferation and invasion of H1792 cells. Together, these results present a systematic analysis of gene fusions in cancer and describe key characteristics that assist in new fusion discovery.

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Figure 1: Exploring cancer-related gene fusions in the context of known molecular interaction networks.
Figure 2: Distinguishing biological features of gene fusions and point mutations in cancer.
Figure 3: Characterizing the genomic imbalances of recurrent gene fusions in acute lymphocytic leukemia.
Figure 4: Discovery and validation of the R3HDM2-NFE2 fusion using the ConSig algorithm and the fusion breakpoint principle.

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Acknowledgements

We thank F. Mitelman for offering the fusion genes list from the Mitelman database; T.W. Glover for the important comments for improving the manuscript; J. Granger for help with editing the manuscript; S. Qin for useful discussions about biostatistics; NCIBI colleagues L. Ke and A. Ade for helping in the implementation of tools and technologies; C. Yang for the guidance in drug informatics; Z. Hu from Boston University for help with network visualization. This work was supported by the National Institutes of Health (NIH; U54 DA021519) and a National Institutes of Health Cancer Biology Training Grant (CA009676-18 to J.R.P). J.R.P. is a Fellow of the University of Michigan Medical Scientist Training Program. A.M.C. is supported by NIH early detection network U01 CA111275, DOD W81XWH-09-2-0014, the Doris Duke Foundation and the American Cancer Society.

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Author notes

  1. John R Prensner and Guoan Chen: These authors contributed equally to this work.

Authors and Affiliations

  1. Michigan Center for Translational Pathology, Ann Arbor, Michigan, USA

    Xiao-Song Wang, John R Prensner, Qi Cao, Bo Han, Saravana M Dhanasekaran, Rakesh Ponnala, Xuhong Cao, Sooryanarayana Varambally, Nallasivam Palanisamy & Arul M Chinnaiyan

  2. National Center for Integrative Biomedical Informatics, CCMB, Ann Arbor, Michigan, USA.,

    Xiao-Song Wang, Maureen A Sartor, Gilbert S Omenn & Arul M Chinnaiyan

  3. Department of Pathology, University of Michigan, Ann Arbor, Michigan, USA

    Xiao-Song Wang, John R Prensner, Qi Cao, Bo Han, Saravana M Dhanasekaran, Xuhong Cao, Sooryanarayana Varambally, Dafydd G Thomas, Thomas J Giordano, Nallasivam Palanisamy & Arul M Chinnaiyan

  4. Department of Surgery, University of Michigan, Ann Arbor, Michigan, USA

    Guoan Chen & David G Beer

  5. Comprehensive Cancer Center, University of Michigan Medical School, Ann Arbor, Michigan, USA

    Sooryanarayana Varambally & Arul M Chinnaiyan

  6. Howard Hughes Medical Institute, University of Michigan Medical School, Ann Arbor, Michigan, USA

    Arul M Chinnaiyan

  7. Department of Urology, University of Michigan, Ann Arbor, Michigan, USA

    Arul M Chinnaiyan

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  1. Xiao-Song Wang
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Contributions

X-S.W., G.S.O. and A.M.C. designed the study. X-S.W., J.R.P. and M.A.S. performed bioinformatics analyses. X-S.W., J.R.P., G.C., Q.C., S.M.D., R.P., X.C. and S.V. performed experimental studies. B.H. and N.P. performed FISH analysis. D.G.T., T.J.G. and D.G.B. coordinated the clinical and pathology components. X-S.W., J.R.P., G.S.O. and A.M.C. wrote the manuscript.

Corresponding authors

Correspondence to Gilbert S Omenn or Arul M Chinnaiyan.

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Wang, XS., Prensner, J., Chen, G. et al. An integrative approach to reveal driver gene fusions from paired-end sequencing data in cancer. Nat Biotechnol 27, 1005–1011 (2009). https://doi.org/10.1038/nbt.1584

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