Dual MAPK Inhibition Is an Effective Therapeutic Strategy for a Subset of Class II BRAF Mutant Melanomas
Bibliographic record
Abstract
Abstract Purpose: Dual MAPK pathway inhibition (dMAPKi) with BRAF and MEK inhibitors improves survival in BRAF V600E/K mutant melanoma, but the efficacy of dMAPKi in non-V600 BRAF mutant tumors is poorly understood. We sought to characterize the responsiveness of class II (enhanced kinase activity, dimerization dependent) BRAF mutant melanoma to dMAPKi. Experimental Design: Tumors from patients with BRAF wild-type (WT), V600E (class I), and L597S (class II) metastatic melanoma were used to generate patient-derived xenografts (PDX). We assembled a panel of melanoma cell lines with class IIa (activation segment) or IIb (p-loop) mutations and compared these with WT or V600E/K BRAF mutant cells. Cell lines and PDXs were treated with BRAFi (vemurafenib, dabrafenib, encorafenib, and LY3009120), MEKi (cobimetinib, trametinib, and binimetinib), or the combination. We identified 2 patients with BRAF L597S metastatic melanoma who were treated with dMAPKi. Results: BRAFi impaired MAPK signaling and cell growth in class I and II BRAF mutant cells. dMAPKi was more effective than either single MAPKi at inhibiting cell growth in all class II BRAF mutant cells tested. dMAPKi caused tumor regression in two melanoma PDXs with class II BRAF mutations and prolonged survival of mice with class II BRAF mutant melanoma brain metastases. Two patients with BRAF L597S mutant melanoma clinically responded to dMAPKi. Conclusions: Class II BRAF mutant melanoma is growth inhibited by dMAPKi. Responses to dMAPKi have been observed in 2 patients with class II BRAF mutant melanoma. These data provide rationale for clinical investigation of dMAPKi in patients with class II BRAF mutant metastatic melanoma. See related commentary by Johnson and Dahlman, p. 6107.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".