Immunotherapy of Ipilimumab and Nivolumab in Patients with Advanced Neuroendocrine Tumors: A Subgroup Analysis of the CA209-538 Clinical Trial for Rare Cancers
Bibliographic record
Abstract
PURPOSE: Combination immunotherapy with anti-CTLA-4 and anti-PD-1 blockade has demonstrated significant clinical activity across several tumor types. Neuroendocrine tumors (NET) are a heterogeneous group of rare tumors with limited treatment options. CA209-538 is a clinical trial of combination immunotherapy with ipilimumab and nivolumab in rare cancers, including advanced NETs. PATIENTS AND METHODS: CA209-538 is a prospective multicenter clinical trial in patients with advanced rare cancers. Patients received treatment with nivolumab at a dose of 3 mg/kg and ipilimumab at 1 mg/kg every three weeks for four doses, followed by nivolumab 3 mg/kg every two weeks and continued for up to 96 weeks, until disease progression or the development of unacceptable toxicity. Response was assessed every 12 weeks by RECIST 1.1. The primary endpoint was clinical benefit rate (CBR; complete remission + partial remission + stable disease). RESULTS: Twenty-nine patients with advanced NETs received treatment. Three (10%) patients had low-, 13 (45%) had intermediate-, and 13 (45%) had high-grade tumors; lung was the most common primary site (39%). The objective response rate was 24% with a CBR of 72%; 43% of patients with pancreatic neuroendocrine neoplasms (NEN), and 33% of patients with atypical bronchial carcinoid achieved an objective response. The median progression-free survival was 4.8 months [95% confidence interval (CI): 2.7-10.5] and overall survival was 14.8 months (95% CI: 4.1-21.3). Immune-related toxicity was reported in 66% of patients with 34% experiencing grade 3/4 events. CONCLUSIONS: Combination immunotherapy with ipilimumab and nivolumab demonstrated significant clinical activity in subgroups of patients with advanced NETs including patients with atypical bronchial carcinoid and high-grade pancreatic NENs.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.006 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".