A phase Ib/II study of HER3-targeting lumretuzumab in combination with carboplatin and paclitaxel as first-line treatment in patients with advanced or metastatic squamous non-small cell lung cancer
Bibliographic record
Abstract
PURPOSE: This study investigated the safety and clinical activity of lumretuzumab, a humanised antihuman epidermal growth factor receptor 3 (HER3) monoclonal antibody, in combination with carboplatin and paclitaxel in first-line treatment of patients with squamous non-small cell lung cancer (sqNSCLC). HER3 ligand heregulin and HER3 protein expression were evaluated as potential biomarkers of clinical activity. PATIENTS AND METHODS: ) administered intravenously on a every 3-week schedule. Adverse event (AE) rates and tumour responses were determined. Heregulin messenger RNA (mRNA) and HER3 protein expression were investigated in archival tumour biopsies. RESULTS: Altogether, 12 patients received lumretuzumab in combination with carboplatin and paclitaxel. The most frequent AEs were gastrointestinal, haematological and nervous system toxicities, which were generally mild and manageable. Partial responses were observed in 3 of 12 patients lasting 81, 177 and 207 days. All responses were achieved in tumours expressing higher heregulin mRNA levels. CONCLUSION: Lumretuzumab in combination with carboplatin and paclitaxel was well tolerated. Objective responses were enriched in tumours expressing higher heregulin mRNA levels.
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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) | 0.002 | 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 | 0.001 |
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".