Non-small-cell lung cancer: how to manage BRAF-mutated disease
Bibliographic record
Abstract
BRAF mutations are reported in about 3-5% of non-small-cell lung cancer (NSCLC), almost exclusively in adenocarcinoma histology, and are classified into three different classes.The segmentation of BRAF mutations into V600 (class 1) and non-V600 (classes 2 and 3) relies on their biological characteristics and is of interest for predicting the therapeutic benefit of targeted therapies and immunotherapy.Given the relative rarity of this molecular subset of disease, evidence supporting treatment choices is limited.This review aims to offer a comprehensive update about available therapeutic options for patients with NSCLC harbouring BRAF mutations to guide the physician in the choice of treatment strategies.We collected the most relevant available data, from singlearm phase II studies and retrospective analyses conducted in advanced NSCLC, regarding the efficacy of BRAF and MEK inhibitors in both V600 and non-V600 BRAF mutations.We included case reports and smaller experiences that could provide information on specific alterations.With respect to immunotherapy, we reviewed retrospective evidence on immune-checkpoint inhibitors in this molecular subset, whereas data about chemo-immunotherapy in this molecular subgroup are lacking.Moreover, we included the available, though limited, retrospective evidence of immunotherapy as consolidation after chemo-radiation for unresectable stage III BRAF-mutant NSCLC, and an overview of ongoing clinical trials in the peri-operative setting that could open new perspectives in the future.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".