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 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 | 0.002 | 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) | 0.002 | 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".