A systematic review of the prevalence of pathogenic or likely pathogenic germline variants in individuals with FOXO1 fusion‐positive rhabdomyosarcoma
Bibliographic record
Abstract
Several cancer predisposition syndromes (CPS) are reported to predispose to rhabdomyosarcoma, most frequently in children with embryonal rhabdomyosarcoma. There are lingering questions over the role of CPS in individuals with alveolar rhabdomyosarcoma (ARMS), which are frequently driven by FOXO1 fusion oncoproteins. We conducted a systematic review to identify patients with FOXO1 fusion-positive ARMS (FP-ARMS) who underwent germline DNA sequencing. We estimated the prevalence of pathogenic/likely pathogenic (P/LP) variants in cancer predisposing genes (CPGs) and of CPSs. We included 19 publications reporting on 191 patients with FP-ARMS. P/LP variants in CPGs were identified in 26/191 (13.6%) patients, nine (4.9%) of which were associated with a CPS diagnosis. Evidence for causal associations between CPSs and FP-ARMS could not be assessed with available data from this review. Only one patient was affected with a CPS known to predispose to rhabdomyosarcoma, Li-Fraumeni syndrome. Typical CPS associations with rhabdomyosarcoma are rare, but not nonexistent, in patients with FP-ARMS. FOXO1 fusion status, alone, is insufficient for clinicians to rely on to distinguish between patients with/without CPS.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.009 | 0.011 |
| 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) | 0.005 | 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".