Prospective real-world evidence for the use of immune-checkpoint inhibitors and BRAF-targeted therapy in advanced melanoma from a large Canadian cohort.
Bibliographic record
Abstract
e21533 Background: Clinical trial evidence showed that anti-PD1 ± anti-CTLA4 as well as BRAF ± MEK-inhibitors for BRAF-mutated tumours dramatically improved outcomes for patients with advanced melanoma. Large prospective data sets provide real-world insight into the management of patients with advanced melanoma in routine practice. Methods: Patients ≥ 18 years with unresectable or metastatic melanoma receiving therapy with first- (1L) or second-line (2L) anti-PD1 alone (PD1), with anti-CTLA4 (C-IO) or combination BRAF- and MEK-inhibitors (C-TT) were enrolled in a multi-centre prospective observational study across Canada. Data was collected from May 1, 2016 to November 30, 2021 and entered into the Canadian Melanoma Research Network Registry to include demographics and clinical details. Each patient was followed until death, up to 3 years, or date of data extraction, whichever occurred first. Results: Data for 401 (1L) and 128 (2L) patients was analyzed. There was a significant difference in the age at diagnosis for PD1 (69.2y), C-IO (57.8y), and C-TT (58.8y) in the 1L cohort (p < 0.0001) and 2L cohort (59.5y, 50.8y, and 50.9y respectively; p = 0.036). Patients treated with either C-IO or C-TT had a significantly higher baseline LDH (p = 0.0003) and proportion with brain metastases (p = 0.021) compared to PD1. The probability of survival for PD1, C-IO, and C-TT at 1 year was 0.85, 0.78, and 0.66; and at 3 years was 0.63, 0.54, and 0.36 respectively. The probability of survival for 2L PD1, C-IO, and C-TT at 1 year was 0.81, 0.53, and 0.56; and at 3 years was 0.55, 0.42, and 0.20 respectively. When comparing treatment regimens, the overall survival (OS) in 1L and 2L showed a superior survival for PD1 when compared with C-TT using a pairwise log-rank comparison (p < 0.0001 and p = 0.0009). There was no difference between C-IO and C-TT, or C-IO and PD1 in 1L or 2L. Using a Cox proportional hazard model for OS, the presence of brain metastases was significant only in 1L (HR 1.663, p = 0.01). There was no difference in OS based on age in either 1L or 2L for all treatments. When comparing regimens, the progression-free survival (PFS) in 1L showed no difference using a pairwise log-rank comparison; however, there was a significant improvement in PFS in 2L for PD1 compared with C-TT (p = 0.0014). There was no difference in PFS between C-IO and C-TT, or C-IO and PD1 in 2L. Conclusions: This real-world data suggests that patient selection is key when deciding on the most appropriate treatment in 1L or 2L as PD1 therapy appeared to have superior OS and PFS across comparisons. Patients with high-risk features such as high LDH and the presence of brain metastases received C-IO or C-TT more often than PD1. Age was not associated with OS in 1L or 2L for each treatment.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: yes | Observational | low |
| gpt | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: yes | Observational | high |
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 | 0.005 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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, unvalidatedLabeled directly by 2 models reading the full record.
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".