Development and Evaluation of a Pan-Sarcoma Fusion Gene Detection Assay Using the NanoString nCounter Platform
Bibliographic record
Abstract
The NanoString nCounter assay is a high-throughput hybridization technique using target-specific probes that can be customized to test for numerous fusion transcripts in a single assay using RNA from formalin-fixed, paraffin-embedded material. We designed a NanoString assay targeting 174 unique fusion junctions in 25 sarcoma types. The study cohort comprised 212 cases, 96 of which showed fusion gene expression by the NanoString assay, including all 20 Ewing sarcomas, 11 synovial sarcomas, and 5 myxoid liposarcomas tested. Among these 96 cases, 15 showed fusion expression not identified by standard clinical assay, including EWSR1-FLI1, EWSR1-ERG, BCOR-CCNB3, ZC3H7B-BCOR, HEY1-NCOA2, CIC-DUX4, COL1A1-PDGFB, MYH9-USP6, YAP1-TFE3, and IRF2BP2-CDX1 fusions. There were no false-positive results; however, four cases were false negative when compared with clinically available fluorescence in situ hybridization or RT-PCR testing. When batched as six cases, the per-sample reagent cost was less than conventional techniques, such as fluorescence in situ hybridization, with technologist hands-on time of 1.2 hours per case and assay time of 36 hours. In summary, the NanoString nCounter Sarcoma Fusion CodeSet reliably and cost-effectively identifies fusion genes in sarcomas using formalin-fixed, paraffin-embedded material, including many fusions missed by standard clinical assays, and can serve as a first-line clinical diagnostic test for sarcoma fusion gene identification, replacing multiple individual clinical assays.
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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 | 0.005 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".