CRESTONE: A Phase II Study of the Efficacy and Safety of the HER3 Monoclonal Antibody, Seribantumab, in Solid Tumors With Neuregulin-1 ( <i>NRG1</i> ) Fusions
Bibliographic record
Abstract
PURPOSE Neuregulin-1 ( NRG1 ) fusions are rare but actionable oncogenic drivers in solid tumors. Seribantumab is a fully human anti-HER3 IgG2 monoclonal antibody. In this report, we present the antitumor activity and safety data of seribantumab from the CRESTONE study (ClinicalTrials.gov identifier: NCT04383210 ). PATIENTS AND METHODS This was a prospective phase II clinical study in which patients with advanced solid tumors harboring an NRG1 fusion received seribantumab at a dose of 3,000 mg intravenously once weekly. The primary end point was objective response rate (ORR) by RECIST v1.1. Secondary end points included safety, duration of response, progression-free survival (PFS), overall survival (OS), and disease control rate (DCR). The study was terminated before full enrollment because of sponsor decision, unrelated to safety or efficacy. RESULTS A total of 54 patients with nine different tumor types featuring 19 different NRG1 fusion partners were enrolled. For the 29 patients included in the primary efficacy analysis, the investigator-assessed ORR was 34.5% (95% CI, 17.9 to 54.3), with a DCR of 79% (95% CI, 60 to 92). The median PFS was 5.4 (95% CI, 3.9 to 10.8) months; the median OS was 20.3 (95% CI, 10.2 to not reached) months. In patients with non–small cell lung cancer, eight of 22 achieved response (ORR, 36.4%). Adverse events (AEs) were mostly grade 1 or 2. The most common treatment-related AEs were diarrhea (39%), fatigue (32%), and nausea (22%). CONCLUSION These results support the antitumor activity and safety of seribantumab in patients with advanced solid tumors harboring NRG1 fusions.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".