597 A phase 1 trial of the oncolytic virus SVV-001 in combination with nivolumab and ipilimumab in patients with high grade neuroendocrine neoplasms
Bibliographic record
Abstract
Background High-grade neuroendocrine neoplasms (NENs), including poorly differentiated neuroendocrine carcinomas (NECs) and well-differentiated grade 3 neuroendocrine tumors (NETs), are aggressive malignancies with limited effective treatment options. Immune checkpoint inhibitors (ICIs) have demonstrated limited clinical activity in these tumors. Seneca Valley Virus (SVV-001) is a novel oncolytic RNA virus that has shown synergistic activity with ICIs in preclinical models. Additionally, SVV-001 has been observed to reverse CPI resistance in vivo, supporting its evaluation in combination with nivolumab and ipilimumab.Methods This is an investigator-initiated, phase 1, dose-escalation and cohort-expansion study evaluating intratumoral SVV-001 in combination with nivolumab and ipilimumab in patients with histologically confirmed poorly differentiated NEC or well-differentiated grade 3 NET. The trial was activated in March 2025, with patient enrollment currently ongoing and a target of up to 36 patients. A standard 3+3 dose-escalation design is being employed to determine the recommended phase 2 dose (RP2D). Following dose escalation, an expansion cohort will further evaluate safety and preliminary signals of activity. Tumor endothelial marker 8 (TEM8), a potential biomarker of SVV-001 sensitivity, will be assessed as part of correlative studies. The Clinical Trial Identifier is NCT06889493Trial Registration Clinical Trial Identifier: NCT06889493Ethics Approval The study was approved by the institutional review boards and conducted in accordance with International Conference on Harmonization Good Clinical Practice Guidelines (ICH-GCP) and the Declaration of Helsinki. Study participation is voluntary and all enrolled patients have signed a consent form before taking part in the trial.
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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 | 0.001 |
| 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 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".