Real-World Treatment Patterns and Effectiveness of Targeted and Immune Checkpoint Inhibitor-Based Systemic Therapy in BRAF Mutation-Positive NSCLC
Bibliographic record
Abstract
Introduction BRAF mutations (present in 2%–3% of NSCLC) are a known oncogenic driver and emerging therapeutic target. There is a scarcity of real-world data describing the clinical characteristics, treatment patterns, and effectiveness of targeted BRAF -inhibiting and immune checkpoint inhibitor (ICI)-based systemic therapies, yet this is required for appropriate treatment decisions that optimize patient outcome. Methods Demographic, clinical, treatment, and outcome data of patients with BRAF mutation-positive NSCLC diagnosed between 2018 and 2022 were identified from the Glans-Look Lung Cancer Research database and included in this analysis. Results A total of 53 BRAF mutation-positive patients were identified (V600E, n = 35; non-V600E, n = 18). Furthermore, 46 patients (87%) were diagnosed with metastatic disease, of whom 61% were treated with systemic anticancer therapy, which significantly improved overall survival (34.1 versus 2.2 mo, p = 0.01). ICI-based regimens were found to have effectiveness in the first-line setting for both V600E and non-V600E cohorts (objective response rate: 38%–43%; real-world calculations of median progression-free survival: 10.5–10.8 mo, respectively). Dual-targeted BRAF / MEK inhibition was also found to have effectiveness in the first-line setting for V600E patients (objective response rate: 33%, real-world calculations of median progression-free survival: 15.2 mo). Conclusions This study of real-world patients with BRAF mutations confirms the importance of effective systemic therapies. Both dual-targeted BRAF / MEK inhibition and ICI-based regimens have evidence of benefit in this population revealing that real-world populations can experience similar clinical response and outcome to clinical trial cohorts on these treatment regimens. Future studies to clarify the role of co-mutations on response to both dual-targeted BRAF / MEK inhibition and ICI-based regimens may be important to treatment selection and optimization of patient outcome.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".