Fluorescence In Situ Hybridization for the Detection of t(X;18)(p11.2;q11.2) in a Synovial Sarcoma Tissue Microarray Using a Breakapart-Style Probe
Bibliographic record
Abstract
Synovial sarcomas (SSs) account for 5% of soft tissue tumors and carry a balanced translocation t(X;18)(p11.2;q11.2), detectable in over 90% of cases. This translocation brings together portions of two genes: SYT and SSX. Detecting interruption of the SYT gene on chromosome 18 would be useful as a diagnostic tool. We describe a scoring method to detect disruption of SYT with breakapart probe fluorescence in situ hybridization (FISH) and the application of this method for identification of SS within a sarcoma tissue microarray. After optimization, SYT disruption was identified in 22 of 23 (96%) of known SS tumor samples but was not in 23 of 23 (100%) of non-SS sarcoma samples. Ten of 11 (91%) blinded test SS tumor samples were also correctly identified. For comparison, commercially available FISH and chromogenic in situ hybridization (CISH) probes were tested. The commercial FISH probes identified SYT disruption in 81% of the SS tumor samples but in none of the non-SS samples. The CISH probes produced signals too weak to interpret. The use of breakapart FISH probes is a relatively quick procedure for detection of synovial sarcoma translocations and can be applied to archival specimens in tissue microarrays.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".