Efficacy of vemurafenib in patients (pts) with non-small cell lung cancer (NSCLC) with <i>BRAF</i><sup>V600</sup> mutation.
Bibliographic record
Abstract
9074 Background: BRAFV600 mutations occur in 1–2% of pts with NSCLC. We previously reported the efficacy of vemurafenib, a selective BRAFV600 inhibitor, in BRAF mutation-positive non-melanoma tumors (VE-BASKET study). We now present final data for the expanded NSCLC cohort. Methods: This open-label, histology-independent, phase 2 study included 6 prespecified cohorts (including NSCLC) plus one ‘all-others’ cohort. Pts received vemurafenib (960 mg bid) until disease progression or unacceptable toxicity. The primary endpoint was objective response rate (RECIST v1.1). Secondary endpoints included best overall response rate, duration of response (DoR), progression-free survival (PFS), and overall survival (OS). Because the pre-specified clinical benefit endpoint was met in the initial NSCLC cohort, the cohort was expanded. ClinicalTrials.gov identifier NCT01524978. Results: Database lock was 12 Jan 2017. Of 208 pts enrolled at 25 centers worldwide, 62 pts had NSCLC: median age 65 years; 56% male; 13% had no prior systemic therapy; 50% had ≥2 prior therapies. Responses were seen in previously treated and untreated pts (Table). The most common all-grade adverse event (AE) was nausea (40%); grade 3–5 AEs included keratoacanthoma (15%) and squamous cell carcinoma of the skin (15%). Six pts discontinued vemurafenib due to AEs; two had non-treatment-related fatal AEs. Conclusions: Vemurafenib showed evidence of encouraging efficacy in pts with NSCLC with BRAFV600 mutation, with prolonged PFS in previously untreated pts; median OS was not estimable due to ongoing responses. The safety profile of vemurafenib was similar to that seen in melanoma studies. Our results suggest a role for BRAF inhibition in NSCLC with BRAF mutations. Clinical trial information: NCT01524978. [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) | 0.001 | 0.001 |
| 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".