ETV transcriptional upregulation is more reliable than RNA sequencing algorithms and FISH in diagnosing round cell sarcomas with <i>CIC</i> gene rearrangements
Bibliographic record
Abstract
CIC rearrangements have been reported in two-thirds of EWSR1-negative small blue round cell tumors (SBRCTs). However, a number of SBRCTs remain unclassified despite exhaustive analysis. Fourteen SBRCTs lacking driver genetic events by RNA sequencing (RNAseq) analysis were collected. Unsupervised hierarchical clustering was performed using samples from our RNAseq database, including 13 SBRCTs with non-CIC genetic abnormalities and 2 CIC-rearranged angiosarcomas among others. Remarkably, all 14 study cases showed high mRNA levels of ETV1/4/5, and by unsupervised clustering most grouped into a distinct cluster, separate from other tumors. Based on these results indicating a close relationship with CIC-rearranged tumors, we manually inspected CIC reads in RNAseq data. FISH for CIC and DUX4 abnormalities and immunohistochemical stains for ETV4 were also performed. In the control group, only 2 CIC-rearranged angiosarcomas had high ETV1/4/5 expression. Upon manual inspection of CIC traces, 7 of 14 cases showed CIC-DUX4 fusion reads, 2 cases had DUX4-CIC reads, while the remaining 5 were negative. FISH showed CIC break-apart in 7 cases, including 5 cases lacking CIC-DUX4 or DUX4-CIC fusion reads on RNAseq manual inspection. However, no CIC abnormalities were detected by FISH in 6 cases with CIC-DUX4 or DUX4-CIC reads. ETV4 immunoreactivity was positive in 7 of 11 cases. Our results highlight the underperformance of FISH and RNAseq methods in diagnosing SBRCTs with CIC gene abnormalities. The downstream ETV1/4/5 transcriptional up-regulation appears highly sensitive and specific and can be used as a reliable molecular signature and diagnostic method for CIC fusion positive SBRCTs.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".