Clinical Development of BRAF plus MEK Inhibitor Combinations
Bibliographic record
Abstract
Genomic profiling shows that many solid tumors are characterized by specific driver aberrations, and this has expanded the therapeutic options for many patients. The mitogen-activated protein kinase (MAPK) pathway is a key cell signaling pathway involved in regulating cellular growth, proliferation, and survival. Driver mutations in the BRAF gene, a key player in the MAPK pathway, are described in multiple tumor types, including subsets of melanoma, non-small cell lung cancer (NSCLC), and anaplastic thyroid cancer (ATC), making BRAF a desirable target for inhibition. BRAF inhibitors have shown efficacy in several cancers; however, most patients eventually develop resistance. To delay or prevent resistance, combination therapy targeting BRAF and MEK, a downstream signaling target of BRAF in the MAPK pathway, was evaluated and demonstrated synergistic benefit. BRAF and MEK inhibitor combinations have been approved for use in various cancers by the US FDA. We review the clinical data for various BRAF plus MEK combination regimens in three cancer types with underlying BRAF driver mutations: melanoma, NSCLC, and ATC. We also discuss practical treatment considerations and management of selected combination therapy toxicities.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".