A phase 2 trial of sunitinib in patients with progressive paraganglioma or pheochromocytoma: the SNIPP trial
Bibliographic record
Abstract
BACKGROUND: Pheochromocytoma (PCC) and paraganglioma (PGL) are uncommon neoplasms with high morbidity in advanced stages. Effective systemic treatments are limited. METHODS: A multisite phase 2 trial evaluated sunitinib in patients with progressive PCC/PGL. Patients received 50 mg orally for 4-6 weeks. RESULTS: Between May 2009 and May 2016, 25 patients were enroled. The median age was 50 years and 56% were male. Three patients (12%) received prior chemotherapy and 16 (64%) prior surgery. The DCR was 83% (95% CI: 61-95%) and median PFS 13.4 (95% CI: 5.3-24.6) months. Of 23 evaluable patients, 3 (13%) with germline mutations (SDHA, SDHB, RET) achieved a PR. The patient with mutated RET and MEN2A remains on treatment after 64 cycles. The median time on treatment was 12.4 (1-88.0) months. Grade 3 or 4 toxicities were as expected and manageable; fatigue (16%) and thrombocytopenia (16%) were most common. One patient with grade 3 hypertension and 2 with grade 3 cardiac events discontinued treatment. CONCLUSION: Although the primary endpoint of disease control was met, the overall response rate of sunitinib was low in unselected patients with progressive PCC/PGL. Patients with germline variants in RET or in the subunits of SDH may derive greatest benefit.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".