Identification of fusion genes in breast cancer by paired-end RNA-sequencing

Abstract

BackgroundUntil recently, chromosomal translocations and fusion genes have been an underappreciated class of mutations in solid tumors. Next-generation sequencing technologies provide an opportunity for systematic characterization of cancer cell transcriptomes, including the discovery of expressed fusion genes resulting from underlying genomic rearrangements.ResultsWe applied paired-end RNA-seq to identify 24 novel and 3 previously known fusion genes in breast cancer cells. Supported by an improved bioinformatic approach, we had a 95% success rate of validating gene fusions initially detected by RNA-seq. Fusion partner genes were found to contribute promoters (5' UTR), coding sequences and 3' UTRs. Most fusion genes were associated with copy number transitions and were particularly common in high-level DNA amplifications. This suggests that fusion events may contribute to the selective advantage provided by DNA amplifications and deletions. Some of the fusion partner genes, such as GSDMB in the TATDN1-GSDMB fusion and IKZF3 in the VAPB-IKZF3 fusion, were only detected as a fusion transcript, indicating activation of a dormant gene by the fusion event. A number of fusion gene partners have either been previously observed in oncogenic gene fusions, mostly in leukemias, or otherwise reported to be oncogenic. RNA interference-mediated knock-down of the VAPB-IKZF3 fusion gene indicated that it may be necessary for cancer cell growth and survival.ConclusionsIn summary, using RNA-sequencing and improved bioinformatic stratification, we have discovered a number of novel fusion genes in breast cancer, and identified VAPB-IKZF3 as a potential fusion gene with importance for the growth and survival of breast cancer cells.

RNA-Seq and human complex diseases: recent accomplishments and future perspectives

“…66 Novel fusion transcripts have been also discovered, especially in breast cancer (Table 1). 51,61 RNA-Seq revealed that the occurrence of chimeric transcripts in melanoma is a frequent event, also highlighting novel genes and pathways previously not associated to its pathogenesis. 55 Precisely defining the specificity and occurrence of some rearrangements may help clinicians to discern the molecular subtypes of the same cancer, such as in B-cell lymphomas and breast cancer.…”

“…Many RNA-Seq studies have suggested that detrimental fusion transcripts and alternative splicing may be involved in the carcinogenesis of different tissues and organs such as breast, 51 prostate, 52,53 soft tissue, 54 melanocytes 55 and lymphoid tissue and organs (Table 1). [56][57][58] Most of them have discovered a considerable fraction of fusion transcripts -that is chimeric mRNAs that may alter cell's functionality -commonly produced by genomes rearrangement and critically involved in the pathogenesis of several types of malignancies.…”

Identification of factors required for m6A mRNA methylation in Arabidopsis reveals a role for the conserved E3 ubiquitin ligase HAKAI

“…Differential gene expression was assessed with the DE seq package (Anders & Huber, 2010). The full procedure is described in (Edgren et al ., 2011). The GO analysis of complete gene list expressed in the vir‐1 root tip was done using the A gri GO tools (Du et al ., 2010) with FDR‐corrected P‐ value ( Q value) as a ranking criterion and Hypergeometric test (Hochberg FDR) with 0.05 significance cut‐off.…”

Massive parallel sequencing uncovers actionable FGFR2–PPHLN1 fusion and ARAF mutations in intrahepatic cholangiocarcinoma

“…In brief, tophat-fusion breaks up individual reads into 25-bp segments, which are mapped independently to the reference build hg19 via bowtie 41 . Following Edgren et al 42 , we used the following filtration scheme to identify fusion events: (1) BLAST sequence around putative breakpoint to hg19 build to identify and remove paralogous sequences; (2) filter fusions based on the number of reads that support the putative fusion breakpoints, setting the minimum number of such spanning reads conservatively; (3) compute scores of distributions of coverage of reads around the putative breakpoints, rejecting non uniformly covered reads; (4) fusions between adjacent genes were rejected as read-through transcript events, with a minimum distance of 100 kb; (5) finally, we considered a read to support a fusion if it mapped to both sides of breakpoint by at least a minimum fusion anchor length of 25 bp, or generally about a quarter of a read length. All these parameters were essentially tuned with the constraint that the matched normal sample had no positive fusion detections.…”