Fusion-Negative Rhabdomyosarcoma: Clinical Application of Targeted RNA Sequencing
Bibliographic record
Abstract
Background: Rhabdomyosarcoma (RMS) is the most common soft tissue sarcoma of childhood. For stratification purposes, rhabdomyosarcoma is classified into fusion-positive RMS (alveolar rhabdomyosarcoma) and fusion-negative RMS (embryonal or spindle cell/sclerosing, FN-RMS) subtypes according to its PAX::FOXO1 fusion status. This study aims to highlight the pathologic and molecular characteristics of a cohort of FN-RMS using a targeted NGS RNA-Seq assay. Methods: Twelve tumors were analyzed through targeted RNA-Seq using the Trusight Pancancer panel from Illumina. Molecular alterations were then correlated with the clinicopathological features. Results: Of the 12 tumors analyzed, we identified 6 embryonal rhabdomyosarcomas (ERMSs) harboring mutations in key signaling molecules ( KRAS, HRAS, NRAS , and FGFR4 ), oncogenic DICER1 mutations in 2 ERMS, pathogenic TP53 and NF1 mutations in an ERMS with features of anaplasia, a TEAD1::NCOA2 gene fusion in a congenital spindle cell and sclerosing rhabdomyosarcoma (SSRMS), and a FUS::TFCP2 gene fusion in a skull base SSRMS. Only 1 ERMS in the bladder showed no reportable molecular alterations. Conclusion: We illustrate case examples demonstrating how a combined morphological and molecular approach with targeted RNA-Seq can aid in diagnosis and identify clinically actionable alterations in pediatric FN-RMS.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".