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The landscape and therapeutic relevance of cancer-associated transcript fusions

Oncogene volume 34, pages 4845–4854 (2015)Cite this article

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Abstract

Transcript fusions as a result of chromosomal rearrangements have been a focus of attention in cancer as they provide attractive therapeutic targets. To identify novel fusion transcripts with the potential to be exploited therapeutically, we analyzed RNA sequencing, DNA copy number and gene mutation data from 4366 primary tumor samples. To avoid false positives, we implemented stringent quality criteria that included filtering of fusions detected in RNAseq data from 364 normal tissue samples. Our analysis identified 7887 high confidence fusion transcripts across 13 tumor types. Our fusion prediction was validated by evidence of a genomic rearrangement for 78 of 79 fusions in 48 glioma samples where whole-genome sequencing data were available. Cancers with higher levels of genomic instability showed a corresponding increase in fusion transcript frequency, whereas tumor samples harboring fusions contained statistically significantly fewer driver gene mutations, suggesting an important role for tumorigenesis. We identified at least one in-frame protein kinase fusion in 324 of 4366 samples (7.4%). Potentially druggable kinase fusions involving ALK, ROS, RET, NTRK and FGFR gene families were detected in bladder carcinoma (3.3%), glioblastoma (4.4%), head and neck cancer (1.0%), low-grade glioma (1.5%), lung adenocarcinoma (1.6%), lung squamous cell carcinoma (2.3%) and thyroid carcinoma (8.7%), suggesting a potential for application of kinase inhibitors across tumor types. In-frame fusion transcripts involving histone methyltransferase or histone demethylase genes were detected in 111 samples (2.5%) and may additionally be considered as therapeutic targets. In summary, we described the landscape of transcript fusions detected across a large number of tumor samples and revealed fusion events with clinical relevance that have not been previously recognized. Our results support the concept of basket clinical trials where patients are matched with experimental therapies based on their genomic profile rather than the tissue where the tumor originated.

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Acknowledgements

The results published here are in whole or part based upon data generated by The Cancer Genome Atlas project established by the NCI and NHGRI. Information about TCGA and the investigators and institutions who constitute the TCGA research network can be found at ‘http://cancergenome.nih.gov’. This work was supported in part by the Mary K Chapman Foundation, the Michael & Susan Dell Foundation (honoring Lorraine Dell), US National Cancer Institute (MD Anderson TCGA Genome Data Analysis Center) grant numbers CA143883 and CA083639, and MD Anderson Cancer Center Support Grant P30 CA016672 (the Bioinformatics Shared Resource).

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Authors and Affiliations

  1. Department of Bioinformatics and Computational Biology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA

    K Yoshihara, Q Wang, W Torres-Garcia, S Zheng, R Vegesna, H Kim & R G W Verhaak

  2. Department of Obstetrics and Gynecology, Niigata University Graduate School of Medical and Dental Sciences, Niigata, Japan

    K Yoshihara

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

    R G W Verhaak

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  1. K Yoshihara
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Correspondence to R G W Verhaak.

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Yoshihara, K., Wang, Q., Torres-Garcia, W. et al. The landscape and therapeutic relevance of cancer-associated transcript fusions. Oncogene 34, 4845–4854 (2015). https://doi.org/10.1038/onc.2014.406

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