Efficacy of immune checkpoint inhibitors in alveolar soft-part sarcoma: results from a retrospective worldwide registry
Bibliographic record
Abstract
BACKGROUND: Conventional cytotoxic drugs are not effective in alveolar soft-part sarcoma (ASPS). Immune checkpoint (programmed cell death protein 1/programmed death-ligand 1) inhibitors (ICIs) are promising drugs in ASPS. A worldwide registry explored the efficacy of ICI in ASPS. MATERIALS AND METHODS: Data from adult patients diagnosed with ASPS and treated with ICI for advanced disease in expert sarcoma centers from Europe, Australia and North America were retrospectively collected, including demographics and data related to treatments and outcome. RESULTS: Seventy-six ASPS patients, with a median age at diagnosis of 25 years (range 3-61 years), were registered. All patients received ICI for metastatic disease. Immunotherapy regimens consisted of monotherapy in 38 patients (50%) and combination in 38 (50%) (23 with a tyrosine kinase inhibitor). Among the 68 assessable patients, there were 3 complete responses and 34 partial responses, translating into an overall response rate of 54.4%. After a median follow-up of 36 months [95% confidence interval (CI) 32-40 months] since the start of immunotherapy, 45 (59%) patients have progressed on ICI, with a median progression-free survival (PFS) of 16.3 months (95% CI 8-25 months). Receiving ICI in first line (P = 0.042) and achieving an objective response (P = 0.043) correlated with a better PFS. Median estimated overall survival (OS) from ICI initiation has not been reached. The 12-month and 24-month OS rates were 94% and 81%, respectively. CONCLUSIONS: This registry constitutes the largest available series of ASPS treated with ICI. Our results suggest that the ICI treatment provides long-lasting disease control and prolonged OS in patients with advanced ASPS, an ultra-rare entity with limited active therapeutic options.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".