Sarcoma Subgrouping by Detection of Fusion Transcripts Using NanoString nCounter Technology
Javal Sheth1, Anthony Arnoldo1, Yunan Zhong1
11 Division of Pathology, Department of Paediatric Laboratory Medicine, Hospital for Sick Children, Toronto, Ontario, Canada.
Summary
NanoString technology reliably detects sarcoma-specific fusion transcripts in pediatric sarcomas. This innovative method, using formalin-fixed, paraffin-embedded tissues, shows 100% concordance with RT-PCR for molecular diagnosis.
Area of Science:
- Oncology
- Molecular Diagnostics
- Genetics
Background:
- NanoString technology offers a barcode-based system for detecting multiple fusion transcripts with minimal tissue.
- This method is advantageous over traditional techniques in terms of sample requirements and multiplexing capabilities.
Purpose of the Study:
- To evaluate the effectiveness of NanoString technology for identifying sarcoma-specific fusion transcripts in pediatric sarcoma samples.
- To determine the utility of this innovative technique in the molecular diagnosis of pediatric sarcomas.
Main Methods:
- Designed probe pairs for common pediatric sarcoma fusion transcripts.
- Utilized the NanoString assay to test 22 specific fusion transcripts in 45 sarcoma samples previously confirmed by RT-PCR.
- Employed a mix of frozen, formalin-fixed paraffin-embedded (FFPE), and rapid extract templates for analysis.
Main Results:
- All 22 targeted transcripts were successfully detected in at least one of the 45 tumor samples.
- NanoString assay results demonstrated 100% concordance with prior RT-PCR findings.
- The technique proved effective with both FFPE and rapid extract sample preparations.
Conclusions:
- Multiplexed analysis of sarcoma-specific fusion transcripts via NanoString technology is a dependable method for pediatric sarcoma molecular diagnosis.
- The NanoString assay performs effectively with FFPE tissues, facilitating its use in diagnostic workflows.
- Further validation of additional transcripts and optimization of diagnostic workflows are recommended.
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