Spartalizumab in metastatic, well/poorly differentiated neuroendocrine neoplasms
Bibliographic record
Abstract
Spartalizumab, a humanized anti-programmed death protein 1 (PD-1) MAB, was evaluated in patients with well-differentiated metastatic grade 1/2 neuroendocrine tumors (NET) and poorly differentiated gastroenteropancreatic neuroendocrine carcinomas (GEP-NEC). In this phase II, multicenter, single-arm study, patients received spartalizumab 400 mg every 4 weeks until confirmed disease progression or unacceptable toxicity. The primary endpoint was confirmed overall response rate (ORR) according to blinded independent review committee using response evaluation criteria in solid tumors 1.1. The study enrolled 95 patients in the NET group (30, 32 and 33 in the thoracic, gastrointestinal, and pancreatic cohorts, respectively), and 21 patients in the GEP-NEC group. The ORR was 7.4% (95% CI: 3.0, 14.6) in the NET group (thoracic, 16.7%; gastrointestinal, 3.1%; pancreatic, 3.0%), which was below the predefined success criterion of ≥10%, and 4.8% (95% CI: 0.1, 23.8) in the GEP-NEC group. In the NET and GEP-NEC groups, the 12-month progression-free survival was 19.5 and 0%, respectively, and the 12-month overall survival was 73.5 and 19.1%, respectively. The ORR was higher in patients with ≥1% PD-L1 expression in immune/tumor cells or ≥1% CD8+ cells at baseline. The most common adverse events considered as spartalizumab-related included fatigue (29.5%) and nausea (10.5%) in the NET group, and increased aspartate and alanine aminotransferases (each 14.3%) in the GEP-NEC group. The efficacy of spartalizumab was limited in this heterogeneous and heavily pre-treated population; however, the results in the thoracic cohort are encouraging and warrants further investigation. Adverse events were manageable and consistent with previous experience.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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 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".