Tumorigenesis driven by the BRAF <sup>V600E</sup> oncoprotein requires secondary mutations that overcome its feedback inhibition of migration and invasion
Bibliographic record
Abstract
Abstract BRAF V600E mutation occurs in 46% of melanomas and drives high levels of ERK activity and ERK-dependent proliferation. However, BRAF V600E is insufficient to drive melanoma in GEMM models, and 82% of human benign nevi harbor BRAF V600E mutations. We show here that BRAF V600E inhibits mesenchymal migration by causing feedback inhibition of RAC1 activity. ERK pathway inhibition induces RAC1 activation and restores migration and invasion. In cells with BRAF V600E , mutant RAC1, overexpression of PREX1, PREX2, or PTEN inactivation restore RAC1 activity and cell motility. Together, these lesions occur in 48% of BRAF V600E melanomas. Thus, although BRAF V600E activation of ERK deregulates cell proliferation, it prevents full malignant transformation by causing feedback inhibition of cell migration. Secondary mutations are, therefore, required for tumorigenesis. One mechanism underlying tumor evolution may be the selection of lesions that rescue the deleterious effects of oncogenic drivers. Statement of significance BRAF V600E activation of ERK causes feedback inhibition of cell migration and invasion and thus blocks tumorigenesis. Secondary genetic lesions are required to rescue these processes and enable tumor development. Thus, oncogenic feedback can shape the details of tumor progression and, in doing so, selects for new mutations that may be therapeutic targets.
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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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".