Randomized Phase II Trial of Sunitinib or Cediranib in Alveolar Soft Part Sarcoma
Bibliographic record
Abstract
PURPOSE: Alveolar soft part sarcoma (ASPS) is a rare, highly vascular tumor with few treatment options. We designed a phase II randomized trial to determine the activity and tolerability of single-agent cediranib or sunitinib in patients with advanced metastatic ASPS. PATIENTS AND METHODS: Patients 16 years of age and older were randomized to receive cediranib (30 mg) or sunitinib (37.5 mg) in 28-day cycles. Patients could cross over to the other treatment arm at disease progression. The primary endpoint was to measure the objective response rate (ORR) for each agent. Median progression-free survival (mPFS) for the two arms was also determined. RESULTS: Twenty-nine of 34 enrolled patients were evaluable for response. One patient on each of the initial two treatment arms had a partial response (ORR: 6.7% and 7.1% for cediranib and sunitinib, respectively). Twenty-four patients had a best response of stable disease (86.7% and 78.6% for cediranib and sunitinib, respectively). There were no significant differences in mPFS for the two treatment arms. Clinical benefit (i.e., objective response or stable disease for a minimum of four or six cycles of therapy) on the first-line tyrosine kinase inhibitor (TKI) therapy did not predict benefit on the second-line TKI. Both drugs were well tolerated. As of August 2021, 1 patient (unevaluable for ORR) remains on study. CONCLUSIONS: The study did not meet its endpoints for ORR. Although both TKIs provided clinical benefit, the outcomes may have been attenuated in patients who had progressed ≤6 months before enrollment, potentially accounting for the low response rates. See related commentary by Wilky and Maleddu, p. 1163.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".