Abstract 1209: Patterns of treatment with immune check point inhibitors and targeted therapy in patients with metastatic melanoma presumed BRAF V600 positive
Bibliographic record
Abstract
Abstract Background: Immune check point inhibitors (I-O) and targeted therapies (TT) have changed the treatment landscape for patients with metastatic melanoma (MM), particularly for patients with BRAFV600 (BRAF) mutation who are eligible for both types of treatment after a diagnosis of MM. The aim of the current study was to describe patterns of treatment with I-O and TT in first line (1L) and subsequent lines of therapy for MM in a sample of patients presumed BRAF positive. Methods: Adults with MM initiated on I-O (ipilimumab, pembrolizumab, nivolumab) and TT (vemurafenib, dabrafenib, trametinib) therapies in 1L were identified in Symphony Health Solutions' Integrated Dataverse (Q1/2014 - Q1/2017; n = 4,196), the largest pharmacy database in the US. Lines of pharmacological therapy were investigated from the first I-O/TT (index date) until the end of the observation period using an algorithm that relies on prescription/administration dates, days of supply and periods without any therapies. Patients were presumed BRAF positive if they received TT in at least one line of MM therapy. All patients in this analysis were required to have ≥ 2 lines of therapy for MM. Results: Of 366 presumed BRAF patients in the study sample, 110 (30%) and 256 (70%) were initiated on I-O and TT in 1L, respectively. The table below presents treatment patterns in 1L, 2L, and 3L for MM. The distribution of I-O vs TT was 30% vs. 70% in 1L, 25% vs. 57% in 2L, and 41% vs. 39% in 3L (table). Conclusions: This real-world data study showed dabrafenib+trametinib was the most common treatment for patients with MM presumed BRAF positive, even in the era of I-O availability. During the study period (years 2014-2017), ipilimumab continued to be the most common I-O therapy used in 1L and 2L among presumed BRAF patients. Table.Years 2014-20171L N=3662L N=3663L N=111RegimensI-O, N (%)110 (30%)103 (28%)46 (41%)Up to third most common I-O regimen, N (%)Ipilimumab 72 (20%)Ipilimumab 32 (9%)Nivolumab 14 (13%)Nivolumab 13 (4%)Pembrolizumab 32 (9%)Pembrolizumab 12 (11%)Pembrolizumab 13 (4%)Nivolumab 22 (6%)Ipilimumab + Nivolumab 10 (9%)TT, N (%)256 (70%)207 (57%)43 (39%)Up to third most common TT regimen, N (%)Dabrafenib+Trametinib 134 (37%)Dabrafenib+Trametinib 117 (32%)Dabrafenib+Trametinib 18 (16%)Vemurafenib 69 (19%)Vemurafenib 22 (6%)Vemurafenib 5 (5%)Dabrafenib 31 (8%)Dabrafenib 18 (5%)Trametinib 5 (5%)Vemurafenib + Cobimetinib 18 (5%)Vemurafenib + Cobimetinib 5 (5%)Both I-O and TT, N (%)0 (by design)18 (5%)7 (2%)Other antineoplastic agents, N (%)0 (by design)38 (10%)15 (4%)Top 3 most frequent treatment sequencesPatients with ≥2 lines of therapy (n=366 )1L -> 2L (%)TT -> TT (34%)TT -> I-O (25%)I-O -> TT (23%)Patients with ≥3 lines of therapy (n=111)1L -> 2L -> 3L (%)TT -> I-O -> I-O (14%)I-O -> TT -> I-O (12%)TT -> TT -> I-O (11%)1L, 2L, 3L: first, second, third-line of therapy; I-O: immune check point inhibitors; TT, targeted therapy. Citation Format: Sameer Ghate, Antonio Nakasato, Raluca Ionescu-Ittu, Sherry Shi, Briana Ndife, Rebecca Burne, François Laliberté, Mei Sheng Duh. Patterns of treatment with immune check point inhibitors and targeted therapy in patients with metastatic melanoma presumed BRAF V600 positive [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2018; 2018 Apr 14-18; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2018;78(13 Suppl):Abstract nr 1209.
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".