The Role of BRAF Gene in Cancer: Literature Review and Future Directions
Bibliographic record
Abstract
The BRAF gene encodes a protein belonging to the RAF family of serine/threonine protein kinases. This protein plays an important role in regulating the MAP kinase signaling pathway, which is involved in cellular development, differentiation, division, proliferation, secretion, inflammatory responses and apoptosis in mammalian cells. Since 2002, the mutation of valine 600 to glutamic acid (V600E) is the most prevalent, and it is found to be recurrent in many cancer types. It is frequently identified cancer-causing mutation in melanoma, colorectal cancer, thyroid carcinoma, non-small cell lung carcinoma, hairy cell leukemia, non-Hodgkin lymphoma, glioneuronal tumors, hepatocellular carcinoma, adenocarcinoma of lung, ovarian cancer, and also others malignancies and some cancer metastasis. In the early 1990s, some researchers began studying MAP kinase signaling pathway involved in controlling cell growth and its role in cancer, and it helped identify targets for new classes of cancer therapy. Later BRAF mutation was found in over 50% of melanomas. The overactive BRAF protein expression looked like an attractive drug target. Elucidating the detailed molecular structure of the mutant protein helped pharmaceutical companies developed selective inhibitors of mutated BRAF, including Vemurafenib and Dabrafenib, which have been approved to treat melanoma by the Food and Drug Administration (FDA). In addition, there is a growing number of targeted agents that are being evaluated to treat various BRAF-mutant advanced cancer (especially melanoma, lung, thyroid and colorectal cancer), including other RAF kinase inhibitors and/or MEK inhibitors. The standard therapy of inhibition of BRAF mutation in advanced melanoma and/or others malignancies, improved clinical benefit compared to chemotherapy. In the meantime, intrinsic and acquired resistances are still key challenges by using these drugs. The future research is heading to understand the mechanisms of the resistance, therefore it will help us to understand diseases biology and continuously bringing new therapeutic strategies for melanoma and/or others malignancies, including other drugs combination and next-generation of BRAF inhibitors.
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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 | 0.000 | 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) | 0.000 | 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".