Randomized, Open-Label, Phase III Study of Tilsotolimod in Combination With Ipilimumab Versus Ipilimumab Alone in Patients With Advanced Refractory Melanoma (ILLUMINATE-301)
Bibliographic record
Abstract
PURPOSE There are limited treatment options for advanced melanoma that have progressed during or after immune checkpoint inhibitor therapy. Intratumoral (IT) immunotherapy may improve tumor-specific immune activation by promoting local tumor antigen presentation while avoiding systemic toxicities. The phase 3 ILLUMINATE-301 study (ClinicalTrials.gov identifier: NCT03445533 ) evaluated tilsotolimod, a Toll-like receptor-9 agonist, with or without ipilimumab in patients with anti–PD-1 advanced refractory melanoma. METHODS Patients with unresectable stage III-IV melanoma that progressed during or after anti–PD-1 therapy were randomly assigned 1:1 to receive 24 weeks of tilsotolimod plus ipilimumab or 10 weeks of ipilimumab alone. Nine IT injections of tilsotolimod were administered to a single designated lesion over 24 weeks. Intravenous ipilimumab 3 mg/kg was administered once every 3 weeks from week 2 in the tilsotolimod arm and week 1 in the ipilimumab arm. The primary end point was efficacy measured using objective response rate (ORR; independent review) and overall survival (OS). RESULTS A total of 481 patients received tilsotolimod plus ipilimumab (n = 238) or ipilimumab alone (n = 243). ORRs were 8.8% in the tilsotolimod arm and 8.6% in the ipilimumab arm, with disease control rates of 34.5% and 27.2%, respectively. Median OS was 11.6 months in the tilsotolimod arm and 10 months in the ipilimumab arm (hazard ratio, 0.96 [95% CI, 0.77 to 1.19]; P = .7). Grade ≥3 treatment-emergent adverse events occurred in 61.1% and 55.5% of patients in the tilsotolimod and ipilimumab arms, respectively. CONCLUSION Combining IT tilsotolimod with ipilimumab did not significantly improve the ORR or OS compared with ipilimumab alone in patients with anti–PD-1 advanced refractory melanoma.
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) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".