Diagnostic and Therapeutic Implications of a <scp><i>FUS</i></scp>::<scp><i>TFCP2</i></scp> Fusion and <scp><i>ALK</i></scp> Activation in a Metastatic Rhabdomyosarcoma
Bibliographic record
Abstract
The identification of gene fusions in rare sarcoma subtypes can have diagnostic, prognostic, and therapeutic impacts for advanced cancer patients. Here, we present a case of a 31-year-old male with a lytic lesion of the left mandible initially diagnosed as an osteosarcoma but found to have a TFCP2 fusion and ALK alteration, redefining the diagnosis and providing rationale for a novel treatment strategy. Histologically, the tumor displayed hypercellular, spindled to epithelioid neoplasm and nuclear pleomorphism, while immunohistochemistry showed diffuse SATB2 and focal desmin staining. Whole genome and transcriptome analysis revealed a FUS::TFCP2 fusion, the defining alteration of a rare molecularly characterized subtype of soft tissue sarcoma termed intraosseous rhabdomyosarcoma. An internal ALK deletion and extremely high ALK RNA expression were also identified, suggesting potential benefit of an ALK inhibitor. This patient displayed a rapid and dramatic clinical and radiographic response to an ALK inhibitor, alectinib. Unfortunately, the response was short-lived, likely due to the advanced stage and aggressiveness of the disease. This report describes genome and transcriptome characterization of an intraosseous rhabdomyosarcoma, few of which exist in the literature, as well as providing evidence that inhibition of ALK may be a rational treatment strategy for patients with this exceedingly rare soft tissue sarcoma subtype characterized by TFCP2 fusions and ALK activation.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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 | 0.000 |
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".