Real-World Evidence of Systemic Therapy Sequencing on Overall Survival for Patients with Metastatic BRAF-Mutated Cutaneous Melanoma
Bibliographic record
Abstract
Aim: To evaluate optimal systemic therapy sequencing (first-line targeted therapy (1L-TT) vs. first-line immunotherapy (1L-IO)) in patients with BRAF-mutated metastatic melanoma. Methods: Nation-wide prospective data of patients with newly diagnosed BRAF-mutated metastatic melanoma were retrieved from the Canadian Melanoma Research Network. Results: Our study included 79 and 107 patients in the 1L-IO and 1L-TT groups, respectively. There were more patients with ECOG 0−1 (91% vs. 72%, p = 0.023) in the 1L-IO group compared to the 1L-TT group. Multivariable Cox analysis suggested no OS differences between the two groups (HR 0.838, 95%CI 0.502−1.400, p = 0.500). However, patients who received 1L-TT then 2L-IO had the longest OS compared to 1L-IO without 2L therapy, 1L-IO then 2L-TT, and 1L-TT without 2L therapy (38.3 vs. 32.2 vs. 16.9 vs. 6.3 months, p < 0.001). For patients who received 2L therapy, those who received 2L-IO had a trend towards OS improvement compared with the 2L-TT group (21.7 vs. 8.9 months, p = 0.053). Conclusions: Our nation-wide prospective study failed to establish any optimal systemic therapy sequencing in advanced BRAF-mutant melanoma patients. Nevertheless, we provided evidence that immunotherapy has durable efficacy in advanced BRAF-mutant melanoma patients, regardless of treatment line, and that Canadian medical oncologists were selecting the appropriate treatment sequences in a real-world setting, based on patients’ clinical and tumour characteristics.
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.000 | 0.000 |
| 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".