Vaccinia-based oncolytic immunotherapy Pexastimogene Devacirepvec in patients with advanced hepatocellular carcinoma after sorafenib failure: a randomized multicenter Phase IIb trial (TRAVERSE)
Bibliographic record
Abstract
Pexastimogene devacirepvec (Pexa-Vec) is a vaccinia virus-based oncolytic immunotherapy designed to preferentially replicate in and destroy tumor cells while stimulating anti-tumor immunity by expressing GM-CSF. An earlier randomized Phase IIa trial in predominantly sorafenib-naïve hepatocellular carcinoma (HCC) demonstrated an overall survival (OS) benefit. This randomized, open-label Phase IIb trial investigated whether Pexa-Vec plus Best Supportive Care (BSC) improved OS over BSC alone in HCC patients who failed sorafenib therapy (TRAVERSE).129 patients were randomly assigned 2:1 to Pexa-Vec plus BSC vs. BSC alone. Pexa-Vec was given as a single intravenous (IV) infusion followed by up to 5 IT injections. The primary endpoint was OS. Secondary endpoints included overall response rate (RR), time to progression (TTP) and safety.A high drop-out rate in the control arm (63%) confounded assessment of response-based endpoints. Median OS (ITT) for Pexa-Vec plus BSC vs. BSC alone was 4.2 and 4.4 months, respectively (HR, 1.19, 95% CI: 0.78–1.80; p = .428). There was no difference between the two treatment arms in RR or TTP. Pexa-Vec was generally well-tolerated. The most frequent Grade 3 included pyrexia (8%) and hypotension (8%). Induction of immune responses to vaccinia antigens and HCC associated antigens were observed.Despite a tolerable safety profile and induction of T cell responses, Pexa-Vec did not improve OS as second-line therapy after sorafenib failure. The true potential of oncolytic viruses may lie in the treatment of patients with earlier disease stages which should be addressed in future studies.ClinicalTrials.gov: NCT01387555
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".