Pharmacodynamic Effects and Mechanisms of Resistance to Vemurafenib in Patients With Metastatic Melanoma
Bibliographic record
Abstract
PURPOSE To assess pharmacodynamic effects and intrinsic and acquired resistance mechanisms of the BRAF inhibitor vemurafenib in BRAF(V600)-mutant melanoma, leading to an understanding of the mechanism of action of vemurafenib and ultimately to optimization of metastatic melanoma therapy. METHODS In the phase II clinical study NP22657 (BRIM-2), patients received oral doses of vemurafenib (960 mg twice per day). Serial biopsies were collected to study changes in mitogen-activated protein kinase (MAPK) signaling, cell-cycle progression, and factors causing intrinsic or acquired resistance by immunohistochemistry, DNA sequencing, or somatic mutation profiling. Results Vemurafenib inhibited MAPK signaling and cell-cycle progression. An association between the decrease in extracellular signal-related kinase (ERK) phosphorylation and objective response was observed in paired biopsies (n = 22; P = .013). Low expression of phosphatase and tensin homolog showed a modest association with lower response. Baseline mutations in MEK1(P124) coexisting with BRAF(V600) were noted in seven of 92 samples; their presence did not preclude objective tumor responses. Acquired resistance to vemurafenib associated with reactivation of MAPK signaling as observed by elevated ERK1/2 phosphorylation levels in progressive lesions and the appearance of secondary NRAS(Q61) mutations or MEK1(Q56P) or MEK1(E203K) mutations. These two activating MEK1 mutations had not previously been observed in vivo in biopsies of progressive melanoma tumors. CONCLUSION Vemurafenib inhibits tumor proliferation and oncogenic BRAF signaling through the MAPK pathway. Acquired resistance results primarily from MAPK reactivation driven by the appearance of secondary mutations in NRAS and MEK1 in subsets of patients. The data suggest that inhibition downstream of BRAF should help to overcome acquired resistance.
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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) | 0.001 | 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 | 0.000 |
| 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".