Abstract 1206: Patterns of BRAF testing and treatment in patients with metastatic melanoma presumed BRAF positive
Bibliographic record
Abstract
Abstract Background: BRAF mutation affects approximately half of the patients with metastatic melanoma (MM). The NCCN guidelines recommend first-line (1L) therapy with both immune checkpoint inhibitors (I-O) and BRAF-targeted therapies (TT) for patients with BRAF mutated MM. Using two claims-based databases, the current study investigated (a) patterns of BRAF testing among patients with MM presumed to be BRAF positive and (b) characteristics of patients initiated on 1L treatment with I-O versus TT after a presumably positive BRAF test. Methods: Adults with MM initiated on I-O and TT in 1L were identified in Truven MarketScan (Q1/2014 - Q3/2016; n = 1,156) and Quintiles IMS Real-World Data Adjudicated Claims database (Q1/2014 - Q2/2016; n = 1,455) databases. Patients were presumed to be BRAF positive if they received TT in at least one line of MM therapy. All patients were required to have ≥ 2 lines of therapy for MM (n = 162 in Truven sample and n = 247 in QuintilesIMS sample). Results: In the Truven sample, 57% of presumed BRAF positive patients were tested for BRAF mutation between first melanoma diagnosis in the data and 1L initiation; in QuintilesIMS, 64% were tested. Among patients tested for BRAF mutation before 1L initiation, the distribution of I-O vs. TT regimens in 1L was 34% vs. 66% in Truven and 38% vs 62% in QuintilesIMS. Patients with a BRAF test before 1L who were initiated on TT appeared to have more brain metastases, and auto-immune, liver, and renal diseases than those initiated on I-O (Table 1). Of 70 patients in MarketScan and 89 patients in QuintilesIMS not tested before 1L, 11% and 10% respectively were tested between 1L initiation and 2L initiation. Conclusions: This real-world study in two databases showed that approximately two thirds of those tested for BRAF mutation received TT for BRAF in 1L. Among patients tested for BRAF before 1L initiaiton, TT appeared to be channeled towards sicker patients. Future research should ascertain the physician decision on treatment selection. Table 1.Patients tested for BRAF before 1L start, by 1L regimenTruven sample (n = 92)QuintilesIMS sample (n = 158)I-O (n= 31)Targeted (n= 61)I-O (n= 58)Targeted (n= 100)Age at 1L initiation, Median [Q1-Q3]57 [47-62]57 [47-62]51.5 [43-57]54 [46-60]Male, N (%)15 (48%)40 (66%)29 (50%)63 (63%)Comorbidity score (range 0-23, higher values indicate higher risk of death),* Median [Q1-Q3]6 [6-7]7 [6-8]6 [6-7]6 [6-8]Brain metastases, N (%)9 (29%)28 (46%)14 (24%)31 (31%)Comorbid conditions, N (%)Auto-immune disease**0 (0%)6 (10%)3 (5%)9 (9%)Anemia5 (16%)19 (31%)12 (21%)20 (20%)Liver disease4 (13%)16 (26%)9 (16%)25 (25%)Renal disease0 (0%)8 (13%)1 (2%)7 (7%)* Deyo et al. J Clin Epi 1992.** Ankylosing spondylitis, thyroiditis, celiac disease, Graves' disease, inflammatory bowel disease (including Chron''s disease and ulcerative colitis), multiple sclerosis, psoriasis, psoriatic arthritis, rheumatoid arthritis, systemic lupus erythematosus. Citation Format: Sameer Ghate, Antonio Nakasato, Raluca Ionescu-Ittu, Sherry Shi, Briana Ndife, Rebecca Burne, François Laliberté, Mei Sheng Duh. Patterns of BRAF testing and treatment in patients with metastatic melanoma presumed BRAF 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 1206.
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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 | 0.004 |
| 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 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".