Abstract B056: Non-V600 BRAF mutations in melanoma: actionable targets for rational drug combinations
Bibliographic record
Abstract
Abstract Background: We identified a patient who presented with brain metastatic melanoma. Her tumor expressed a rare BRAF mutation (mt) (L597S). L597S is a non-V600 BRAF mt with intermediate BRAF kinase activity. Little is known about whether melanomas bearing these non-V600 mt are amenable to treatment with the same targeted therapies currently used for BRAF V600 mutant melanoma. Therefore, we employed a translational approach to characterize non-V600 mts in melanoma and to investigate their responsiveness to targeted therapies. Methods: We performed systemic review to quantify the incidence of non-V600 mts. Tumor fragments from patients BRAF WT, V600E and L597S melanoma brain metastases were used to generate 3 patient-derived xenograft and cell lines. We obtained several other cell lines bearing these mts, and mts within the p-loop of BRAF. Cells were treated with clinical inhibitors of BRAF (BRAFi; vemurafenib (V), dabrafenib (D), encorafenib (E)), and MEK (MEKi; cobimetinib (C), trametinib (T), binimetinib (B)). BRAFi, MEKi, or the combination thereof were tested in the following assays: immunoblots to analyze cell signalling, long-term in vitro growth assays, and subcutaneous and intracranial in vivo tumor growth experiments. Results: MEKi inhibited Erk in all cell lines whereas BRAFi induced paradoxical Erk activation in WT cells, but impaired Erk phosphorylation in V600 and L597 mutant cells. In long-term growth assays, all MEKi were capable of inhibiting growth in all 12 cell lines tested to varying degrees. BRAFi inhibited the growth of V600 and L597S mt cells but not BRAF WT or p-loop mutant cells. The addition of BRAFi consistently inhibited the growth of MEKi-treated L597S mt cells. E was more effective than either D or V at impairing the growth of MEKi-treated non-V600 cells. Remarkably, V potentiated the growth of MEKi-treated cells with BRAF p-loop mts, whereas E further inhibited their growth. In the L597S subcutaneous PDX model, all vehicle-treated tumors grew progressively, whereas T led to overall stability of tumor growth (25% ORR) and D + T caused 100% of tumors to shrink significantly. Moreover, D + T improved the survival of mice with intracranial L597S metastases, compared to vehicle or T treated mice. Conclusions: Non-V600 BRAF mts comprise 15% of all BRAF mts in melanoma. Like V600 mutant melanoma, L597S BRAF mutant melanoma are synergistically growth inhibited by the combination of BRAFi +MEKi in vitro and in vivo. MEKi-treated cells with BRAF p-loop mutations are further inhibited by E, whereas V potentiates growth, suggesting unique mechanisms of BRAFi between inhibitors. Taken together, our data provide a rationale for investigating the efficacy of dual MAPK inhibition with E or D + MEKi in patients with non-V600 mutant melanoma. Citation Format: April A.N. Rose, Matthew Dankner, Shivshankari Rajkumar, Ian R. Watson, Kevin Petrecca, Catalin Mihalcioiu, Peter M. Siegel. Non-V600 BRAF mutations in melanoma: actionable targets for rational drug combinations [abstract]. In: Proceedings of the AACR-NCI-EORTC International Conference: Molecular Targets and Cancer Therapeutics; 2017 Oct 26-30; Philadelphia, PA. Philadelphia (PA): AACR; Mol Cancer Ther 2018;17(1 Suppl):Abstract nr B056.
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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 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".