Multicenter phase II trial of temsirolimus (TEM) and bevacizumab (BEV) in pancreatic neuroendocrine tumor (PNET).
Bibliographic record
Abstract
4032 Background: PNET has long had few effective therapies other than chemotherapy. Placebo-controlled phase III trials of the mTOR inhibitor everolimus and the VEGF/PDGF receptor inhibitor sunitinib noted improved progression-free survival (PFS). However, objective response rates (RR) with these agents are still <10%. Preclinical studies suggest enhanced anti-tumor effects with combined mTOR and VEGF targeted therapy. Methods: We conducted a phase II trial of the mTOR inhibitor TEM (25 mg IV q week) and the VEGF-A monoclonal antibody BEV (10 mg/kg IV q 2 weeks) in patients (pts) with well or moderately differentiated PNET and progressive disease by RECIST within 7 months of study entry. Co primary endpoints were RR and 6-month PFS. Planned enrollment was 50 patients, with interim analysis for futility after the first 25 evaluable pts. Pts had no prior mTOR or VEGF targeted agents, ECOG PS 0-1, and adequate hematologic and organ function. Continued octreotide was allowed, but not required. Prior interferon, embolization, and ≤ 2 chemotherapy regimens were allowed. Results: 55 pts were eligible for response assessment. Confirmed PR was documented in 20 of 55 patients (37%). 44 of 55 (80%) patients were progression-free at 6 months. Of 49 pts evaluable for this endpoint, 12 month PFS is 49%. 15 patients remain on therapy. For evaluable patients, the most common grade 3-4 adverse events attributed to therapy were hypertension (18%), hyperglycemia (13%), fatigue (11%). leukopenia (9%), headache (9%), proteinuria (7%), and hypokalemia (7%). Conclusions: The combination of TEM/BEV has substantial activity in a multi-center phase II trial with RR of 37%, well in excess of single targeted agents in PNET. 6-month PFS was a notable 80% in a population of patients with RECIST criteria progression within 7 months of study entry. Phase III trials of combined VEGF/mTOR inhibition in PNET should be pursued. Clinical trial information: NCT01010126.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".