Clinical Activity of Mitogen-Activated Protein Kinase–Targeted Therapies in Patients With Non–V600 BRAF-Mutant Tumors
Bibliographic record
Abstract
PURPOSE: Non-V600 mutations comprise approximately 35% of all BRAF mutations in cancer. Many of these mutations have been identified as oncogenic drivers and can be classified into three classes according to molecular characteristics. Consensus treatment strategies for class 2 and 3 BRAF mutations have not yet been established. METHODS: We performed a systematic review and meta-analysis with published reports of individual patients with cancer harboring class 2 or 3 BRAF mutations from 2010 to 2021, to assess treatment outcomes with US Food and Drug Administration-approved mitogen-activated protein kinase (MAPK) pathway targeted therapy (MAPK TT) according to BRAF class, cancer type, and MAPK TT type. Coprimary outcomes were response rate and progression-free survival. RESULTS: A total of 18,167 studies were screened, identifying 80 studies with 238 patients who met inclusion criteria. This included 167 patients with class 2 and 71 patients with class 3 BRAF mutations. Overall, 77 patients achieved a treatment response. In both univariate and multivariable analyses, response rate and progression-free survival were higher among patients with class 2 compared with class 3 mutations, findings that remain when analyses are restricted to patients with melanoma or lung primary cancers. MEK ± BRAF inhibitors demonstrated greater clinical activity in class 2 compared with class 3 BRAF-mutant tumors than BRAF or EGFR inhibitors. CONCLUSION: This meta-analysis suggests that MAPK TTs have clinical activity in some class 2 and 3 BRAF-mutant cancers. BRAF class may dictate responsiveness to current and emerging treatment strategies, particularly in melanoma and lung cancers. Together, this analysis provides clinical validation of predictions made on the basis of a mutation classification system established in the preclinical literature. Further evaluation with prospective clinical trials is needed for this population.
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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 | 0.007 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.015 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".