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Breakpoint analysis of transcriptional and genomic profiles uncovers novel gene fusions spanning multiple human cancer types (RNA-seq)

ABSTRACT: We report the design and implementation of a "breakpoint analysis" pipeline to discover novel gene fusions by tell-tale transcript level or genomic DNA copy number transitions occurring within genes. We use this method to prioritize candidate rearrangements from high density array CGH datasets as well as exon-resolution expression microarrays. We mine both publicly available data as well as datasets generated in our laboratory. Several gene fusion candidates were chosen for further characterization, and corresponding samples were profiled using paired end RNA sequencing to discover the identity of the gene fusion. Using this approach, we report the discovery and characterization of novel gene fusions spanning multiple cancer subtypes including angiosarcoma, pancreatic cancer, anaplastic astrocytoma, melanoma, breast cancer, and T-cell acute lymphoblastic leukemia. Taken together, this study provides a robust approach for gene fusion discovery, and our results highlight a more widespread role of fusion genes in cancer pathogenesis.

ORGANISM(S): Homo sapiens

PROVIDER: GSE45133 | GEO | 2013/05/31

SECONDARY ACCESSION(S): PRJNA192951

REPOSITORIES: GEO

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Breakpoint analysis of transcriptional and genomic profiles uncovers novel gene fusions spanning multiple human cancer types (RNA-seq)

Project description:We report the design and implementation of a "breakpoint analysis" pipeline to discover novel gene fusions by tell-tale transcript level or genomic DNA copy number transitions occurring within genes. We use this method to prioritize candidate rearrangements from high density array CGH datasets as well as exon-resolution expression microarrays. We mine both publicly available data as well as datasets generated in our laboratory. Several gene fusion candidates were chosen for further characterization, and corresponding samples were profiled using paired end RNA sequencing to discover the identity of the gene fusion. Using this approach, we report the discovery and characterization of novel gene fusions spanning multiple cancer subtypes including angiosarcoma, pancreatic cancer, anaplastic astrocytoma, melanoma, breast cancer, and T-cell acute lymphoblastic leukemia. Taken together, this study provides a robust approach for gene fusion discovery, and our results highlight a more widespread role of fusion genes in cancer pathogenesis. Breakpoint analysis for the discovery of novel gene fusions across human cancers

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Project description:Gene fusions and chimeric transcripts occur frequently in cancers and in some cases drive the development of the disease. An accurate detection of these events is crucial for cancer research and in a long-term perspective could be applied for personalized therapy. RNA-seq technology has been established as an efficient approach to investigate transcriptomes and search for gene fusions and chimeric transcripts on a genome-wide scale. A number of computational methods for the detection of gene fusions from RNA-seq data have been developed. However, recent studies demonstrate differences between commonly used approaches in terms of specificity and sensitivity. Moreover their ability to detect gene fusions on the isoform level has not been studied carefully so far. Here we propose a novel computational approach called InFusion for fusion gene detection from deep RNA sequencing data. Validation of InFusion on simulated and on several public RNA-seq datasets demonstrated better detection accuracy compared to other tools. We also performed deep RNA sequencing of two well-established prostate cancer cell lines. Using these data we showed that InFusion is capable of discovering alternatively spliced gene fusion isoforms as well as chimeric transcripts that include non-exonic regions. In addition our method can detect anti-sense transcription in the fusions by incorporating strand specificity of the sequencing library.

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