PD-L1 blockade in combination with inhibition of MAPK oncogenic signaling in patients with advanced melanoma
Bibliographic record
Abstract
Combining PD-L1 blockade with inhibition of oncogenic mitogen-activated protein kinase (MAPK) signaling may result in long-lasting responses in patients with advanced melanoma. This phase 1, open-label, dose-escalation and -expansion study (NCT02027961) investigated safety, tolerability and preliminary efficacy of durvalumab (anti-PD-L1) combined with dabrafenib (BRAF inhibitor) and trametinib (MEK inhibitor) for patients with BRAF-mutated melanoma (cohort A, n = 26), or durvalumab and trametinib given concomitantly (cohort B, n = 20) or sequentially (cohort C, n = 22) for patients with BRAF-wild type melanoma. Adverse events and treatment discontinuation rates were more common than previously reported for these agents given as monotherapy. Objective responses were observed in 69.2% (cohort A), 20.0% (cohort B) and 31.8% (cohort C) of patients, with evidence of improved tumor immune infiltration and durable responses in a subset of patients with available biopsy samples. In conclusion, combined MAPK inhibition and anti-PD-L1 therapy may provide treatment options for patients with advanced melanoma.
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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) | 0.000 | 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) | 0.001 | 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".