Efficacy and Safety Analysis of Nelipepimut-S Vaccine to Prevent Breast Cancer Recurrence: A Randomized, Multicenter, Phase III Clinical Trial
Bibliographic record
Abstract
Abstract Purpose: In phase I/II studies, nelipepimut-S (NP-S) plus GM-CSF vaccine was well tolerated and effectively raised HER2-specific immunity in patients with breast cancer. Results from a prespecified interim analysis of a phase III trial assessing NP-S + GM-CSF are reported. Patients and Methods: This multicenter, randomized, double-blind phase III study enrolled females ≥18 years with T1–T3, HER2 low–expressing (IHC 1+/2+), node-positive breast cancer in the adjuvant setting. Patients received 1,000 μg NP-S + 250 μg GM-CSF or placebo + GM-CSF monthly for 6 months, then every 6 months through 36 months. The primary objective was disease-free survival (DFS). Protocol-specified imaging occurred annually. New abnormalities were categorized as recurrence events; biopsy confirmation was not mandated. The interim analysis was conducted as specified in the protocol after 73 DFS events. Results: A total of 758 patients (mean age 51.8 years) were randomized. Adverse events were similar between groups; most common were injection-associated: erythema (84.3%), induration (55.8%), and pruritus (54.9%). There was no significant between-arms difference in DFS events at interim analysis at median follow-up (16.8 months). In the NP-S arm, imaging detected 54.1% of recurrence events in asymptomatic patients versus 29.2% in the placebo arm (P = 0.069). Conclusions: NP-S was well tolerated. There was no significant difference in DFS events between NP-S and placebo. Use of mandated annual scans and image-detected recurrence events hastened the interim analysis contributing to early trial termination.
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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 | 0.000 |
| 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) | 0.003 | 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".