Molecular characteristics of BRAF mutated non-small cell lung cancer and therapeutic outcomes: Multi-institution study.
Bibliographic record
Abstract
e21029 Background: BRAF-mutations are uncommon, present in only 2-4% of all new non-small cell lung cancer (NSCLC) diagnoses. BRAF-mutation type has treatment implications, where the most common BRAFV600E shows sensitivity to tyrosine kinase inhibitors (Dabrafenib or Vemurafenib) with additional benefit seen in duel therapy adding MEK-inhibitors (Trametinib or Cobimetinib). Clinical responses have also been observed with immune checkpoint inhibitors in both V600E and non-V600E mutant patients. The optimal management strategy in this patient population is still unknown. Methods: Patients from the province of Alberta, Canada, with a BRAF-mutation and initiating systemic therapy between 2018 and 2020 were identified. Demographic, clinical, treatment and outcome data were extracted from the institutional Glans-Look Lung Cancer Database. Results: 31 patients with a BRAF-mutation were identified: 52% alive, 58% female, 87% ‘ever’ smokers (average: 40 pack-years). 70% ECOG > 2, 58% Stage IV at diagnosis, with the M1b (one extrathoracic metastatic site) being the most common. 87% had an adenocarcinoma histology and 64.5% carried the BRAFV600E mutation. 19% had other concurrent mutations (KRAS, PIK3CA or EGFR-L858R), 52% showed high PD-L1 expression ( > 50%). In addition, concurrent mutations also were associated with high PD-L1 positivity. 55% of the cohort received systemic treatment, with 71% still on treatment at the time of analysis. Conclusions: BRAF mutant NSCLC is associated with high PD-L1 expression and responses to both checkpoint inhibitors and BRAF inhibitor combinations. Treatment with immunotherapy appears to have a superior toxicity profile and prolonged disease control in both BRAF V600E and non-V600E mutant NSCLC and is an effective first-line strategy. Higher overall response rates are observed with BRAF inhibitor combinations in BRAF V600E patients. Further investigation is warranted to further elucidate sequencing strategies among specific subgroups.[Table: see text]
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".