Sarcoma Subgrouping by Detection of Fusion Transcripts Using NanoString nCounter Technology
Bibliographic record
Abstract
BACKGROUND: NanoString technology is an innovative barcode-based system that requires less tissue than traditional techniques and can test for multiple fusion transcripts in a single reaction. The objective of this study was to determine the utility of NanoString technology in the detection of sarcoma-specific fusion transcripts in pediatric sarcomas. DESIGN: Probe pairs for the most common pediatric sarcoma fusion transcripts were designed for the assay. The NanoString assay was used to test 22 specific fusion transcripts in 45 sarcoma samples that had exhibited one of these fusion genes previously by reverse transcription polymerase chain reaction (RT-PCR). A mixture of frozen (n = 18), formalin-fixed, paraffin-embedded (FFPE) tissue (n = 23), and rapid extract template (n = 4) were used for testing. RESULTS: Each of the 22 transcripts tested was detected in at least one of the 45 tumor samples. The results of the NanoString assay were 100% concordant with the previous RT-PCR results for the tumor samples, and the technique was successful using both FFPE and rapid extract method. CONCLUSION: Multiplexed interrogation for sarcoma-specific fusion transcripts using NanoString technology is a reliable approach for molecular diagnosis of pediatric sarcomas and works well with FFPE tissues. Future work will involve validating additional sarcoma fusion transcripts as well as determining the optimal workflow for diagnostic purposes.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".