Dataset Information

Identification of SRF-E2F1 fusion transcript in EWSR-negative myoepithelioma of the soft tissue.

ABSTRACT: Myoepithelial neoplasms (MN) are rare and not well-circumstanced entities displaying a heterogeneous spectrum of genetic abnormalities, including EWSR1, FUS and PLAG1 rearrangements. However, in the remaining MN no other fusion gene has been described and knowledge concerning secondary acquired molecular alterations is still poor. Therefore, we screened 5 cases of MN of the soft tissue by RNA sequencing with the aim of identifying novel fusion transcripts. A novel SRF-E2F1 fusion was detected in two cases: one was negative for other fusions while the other showed also the presence of FUS-KLF17. The fusion was validated through independent techniques and, in both cases, SRF-E2F1 was detected only in a subclone of the tumoral mass. SRF-E2F1 maintained the coding frame, thus leading to the translation of a chimeric protein containing the DNA-binding domain of SRF and the trans-activation domain of E2F1. Moreover, ectopical expression of SRF-E2F1 demonstrated that the chimeric transcript is functionally active and could affect tumor growth. Occurrence in two cases and biological relevance of the two genes involved suggest that the SRF-E2F1 fusion might become a helpful diagnostic tool. Further biologic studies are needed to better assess its role in MN biology.

SUBMITTER: Urbini M

PROVIDER: S-EPMC5601120 | biostudies-literature | 2017 Sep

REPOSITORIES: biostudies-literature

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Publications

Identification of SRF-E2F1 fusion transcript in EWSR-negative myoepithelioma of the soft tissue.

Urbini Milena M Astolfi Annalisa A Indio Valentina V Tarantino Giuseppe G Serravalle Salvatore S Saponara Maristella M Nannini Margherita M Gronchi Alessandro A Fiore Marco M Maestro Roberta R Brenca Monica M Dei Tos Angelo Paolo AP Dagrada Gian Paolo GP Negri Tiziana T Pilotti Silvana S Casali Paolo Giovanni PG Biasco Guido G Pession Andrea A Stacchiotti Silvia S Pantaleo Maria Abbondanza MA

Oncotarget 20170517 36

Myoepithelial neoplasms (MN) are rare and not well-circumstanced entities displaying a heterogeneous spectrum of genetic abnormalities, including EWSR1, FUS and PLAG1 rearrangements. However, in the remaining MN no other fusion gene has been described and knowledge concerning secondary acquired molecular alterations is still poor. Therefore, we screened 5 cases of MN of the soft tissue by RNA sequencing with the aim of identifying novel fusion transcripts. A novel SRF-E2F1 fusion was detected in ...[more]

PMID: 28947952

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Project description:BackgroundRNA sequencing has been proposed as a means of increasing diagnostic rates in studies of undiagnosed rare inherited disease. Recent studies have reported diagnostic improvements in the range of 7.5-35% by profiling splicing, gene expression quantification and allele specific expression. To-date however, no study has systematically assessed the presence of gene-fusion transcripts in cases of germline disease. Fusion transcripts are routinely identified in cancer studies and are increasingly recognized as having diagnostic, prognostic or therapeutic relevance. Isolated reports exist of fusion transcripts being detected in cases of developmental and neurological phenotypes, and thus, systematic application of fusion detection to germline conditions may further increase diagnostic rates. However, current fusion detection methods are unsuited to the investigation of germline disease due to performance biases arising from their development using tumor, cell-line or in-silico data.MethodsWe describe a tailored approach to fusion candidate identification and prioritization in a cohort of 47 undiagnosed, suspected inherited disease patients. We modify an existing fusion transcript detection algorithm by eliminating its cell line-derived filtering steps, and instead, prioritize candidates using a custom workflow that integrates genomic and transcriptomic sequence alignment, biological and technical annotations, customized categorization logic, and phenotypic prioritization.ResultsWe demonstrate that our approach to fusion transcript identification and prioritization detects genuine fusion events excluded by standard analyses and efficiently removes phenotypically unimportant candidates and false positive events, resulting in a reduced candidate list enriched for events with potential phenotypic relevance. We describe the successful genetic resolution of two previously undiagnosed disease cases through the detection of pathogenic fusion transcripts. Furthermore, we report the experimental validation of five additional cases of fusion transcripts with potential phenotypic relevance.ConclusionsThe approach we describe can be implemented to enable the detection of phenotypically relevant fusion transcripts in studies of rare inherited disease. Fusion transcript detection has the potential to increase diagnostic rates in rare inherited disease and should be included in RNA-based analytical pipelines aimed at genetic diagnosis.

Dataset Information

Identification of SRF-E2F1 fusion transcript in EWSR-negative myoepithelioma of the soft tissue.

Publications

Identification of SRF-E2F1 fusion transcript in EWSR-negative myoepithelioma of the soft tissue.

Similar Datasets

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