| Literature DB >> 12707378 |
Robyn Gaffney1, Artemis Chakerian, John X O'Connell, Joan Mathers, Kelly Garner, Nancy Joste, David S Viswanatha.
Abstract
Synovial sarcomas (SS) are characterized by the t(X;18)(p11;q11) translocation and its resultant fusion gene, SYT-SSX. Two homologues of the SSX gene (ie, SSX1 and SSX2) are involved in the vast majority of SS and the SYT-SSX1 type of fusion has been associated with inferior clinical outcome. Thus, detection of the presence and type of SYT-SSX fusion is critical for diagnosis and prognosis in SS. Identification of SYT-SSX fusion type is typically accomplished by reverse-transcription polymerase chain reaction (RT-PCR) followed by a post-PCR analytic method. As mRNA nucleotide sequences of the SSX1 and SSX2 segments involved in the SYT-SSX fusion are nearly identical, post-PCR methods must be highly discriminatory. We describe a novel method to identify and differentiate these two chimeric transcripts using RT-PCR followed by fluorescent thermostable ligase detection reaction (f-LDR), microparticle bead capture and flow cytometric detection. Evaluation of this unique approach in 11 cases of SS without prior knowledge of SYT-SSX status, six cases of control sarcomas (CS) and three hematopoietic cell lines, revealed that the f-LDR technique was rapid, unambiguous, and highly specific. The f-LDR results were compared to XmnI enzyme digestion patterns and sequencing of PCR products, revealing a 100% concordance for all cases of SS with regards to SYT-SSX transcript type. In addition, there was a strong association of transcript type detected by f-LDR and morphological subclassification of SS, as previously reported. We conclude that this f-LDR method with flow-based detection is a robust approach to post-PCR detection of specific nucleotide sequences in SS and may be more broadly applicable in molecular oncology.Entities:
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Year: 2003 PMID: 12707378 PMCID: PMC1907321 DOI: 10.1016/S1525-1578(10)60462-X
Source DB: PubMed Journal: J Mol Diagn ISSN: 1525-1578 Impact factor: 5.568