The identification of gene fusions promises to play an important role in personalized cancer treatment decisions. Many rare gene fusion events have been identified in fresh frozen solid tumors from common cancers employing next-generation sequencing technology. However the ability to detect transcripts from gene fusions in RNA isolated from formalin-fixed paraffin-embedded (FFPE) tumor tissues, which exist in very large sample repositories for which disease outcome is known, is still limited due to the low complexity of FFPE libraries and the lack of appropriate bioinformatics methods. We sought to develop a bioinformatics method, named gFuse, to detect fusion transcripts in FFPE tumor tissues. An integrated, cohort based strategy has been used in gFuse to examine single-end 50 base pair (bp) reads generated from FFPE RNA-Sequencing (RNA-Seq) datasets employing two breast cancer cohorts of 136 and 76 patients. In total, 118 fusion events were detected transcriptome-wide at base-pair resolution across the 212 samples. We selected 77 candidate fusions based on their biological relevance to cancer and supported 61% of these using TaqMan assays. Direct sequencing of 19 of the fusion sequences identified by TaqMan confirmed them. Three unique fused gene pairs were recurrent across the 212 patients with 6, 3, 2 individuals harboring these fusions respectively. We show here that a high frequency of fusion transcripts detected at the whole transcriptome level correlates with poor outcome (P<0.0005) in human breast cancer patients. This study demonstrates the ability to detect fusion transcripts as biomarkers from archival FFPE tissues, and the potential prognostic value of the fusion transcripts detected.

11018 Background: While several recently discovered gene fusions already play an important role in personalized cancer treatment, many cancer gene fusions remain to be discovered. Next generation sequencing has enabled identification of many rare gene fusion events in fresh or frozen solid tumors. There is a need to detect gene fusions in transcriptomes of formalin-fixed paraffin-embedded (FFPE) tumor tissue, for which there is long-term clinical outcome data. We therefore sought to develop bioinformatics methods to detect fusion transcripts in FFPE tissue and to characterize their association with clinical outcomes. Methods: RNA sequencing libraries were created and sequenced from tumor biopsy tissues (Plos One 2012 7(7): e40092) of two ER+ breast cancer cohorts consisting of 136 and 77 patients, for which clinical outcomes were available. The fusion junctions were nominated by the RNA-seq aligner GSNAP and further filtered to consider discontinuous expression patterns at exon/intron levels. Results: A total of 108 candidate fusion transcripts were detected and RT-PCR assays confirmed 89% of the top ranking fusion transcript candidates. The majority (82%) of identified fusion gene partners are listed in the COSMIC database of known cancer sequence variations. Of note, several patients expressed multiple fusion transcripts that are significantly associated with tumor progression (P<0.001), including genes associated with cell proliferation and cellular metabolism. Furthermore, these patients also harbored inter-chromosomal gene fusions. It is noteworthy that several gene fusions were present in multiple patients. In one of these recurrent fusions the estrogen receptor gene acts as the fusion pair donor. Conclusions: Novel bioinformatics approaches developed here demonstrate the ability to detect fusion transcripts as biomarkers from archival FFPE tissues that associate with breast cancer progression. Some gene fusions were common in multiple patients and deserved further study.