Evolution of BRAF gene mutation testing and treatment options in BRAF mutation carriers in Canada.
Bibliographic record
Abstract
e20094 Background: Since 2012, four drugs approval in Canada have changed the treatment practice in melanoma – immunotherapy ipilimumab and targeted therapies vemurafenib, dabrafenib and trametinib, all three being indicated for patients who harbor a BRAF mutation. The objective of this study is to show the evolution in BRAF mutation testing usage in Canada since 2012, and to evaluate drug choices in BRAF mutation carriers. Methods: This retrospective observational study utilized IMS Brogan Enhanced Tumor Studies, an anonymised patient database collected through quarterly physician panel survey, which provides comprehensive insight into total cancer care. The inclusion criteria consisted of Canadian patients diagnosed with stage IV melanoma between 2012 and 2014 and entered into the database from October 2013 to September 2014. The carriers of BRAF mutation were then evaluated based on the type of drug treatment: targeted therapy (TT – vemurafenib, dabrafenib and trametinib), immunotherapy (IT – ipilimumab and aldesleukin) or chemotherapy (CT – all other antineoplastic drugs excluding TT and IT). Results: Out of 29, 151 and 125 patients diagnosed in 2012, 2013 and 2014 respectively, BRAF testing was performed in 16 (55%), 124 (82%) and 82 (65%) patients. The number of patients who harbored a BRAF mutation was 13, 76 and 34, with the following anticancer drug treatment: CT (with no exposure to TT or IT) was used in 10 (77%), 32 (41%) and 9 (26%) patients; TT was used in 3 (23%), 32 (41%) and 23 (68%) patients; IT was the option in 14 (18%) and 8 (24%) patients in 2013 and 2014, respectively. The average number of days between the BRAF testing and the first TT were 384, 64 and 25 days in 2012, 2013 and 2014, respectively. Conclusions: The trend for BRAF mutation testing has grown since 2012, but a significant percentage of patients remain untested prior to their first treatment. In BRAF mutation carriers, TT appears to be the treatment of choice, although a surprisingly high percentage of patients continue to be treated with CT. Further studies with longer follow up are needed in order to better understand the uptake of BRAF testing as well as drug utilization in those who carry a BRAF mutation.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| 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) | 0.002 | 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".