BM-32 * CERITINIB (LDK378) FOR TREATMENT OF PATIENTS WITH ALK-REARRANGED (ALK+) NON-SMALL CELL LUNG CANCER (NSCLC) AND BRAIN METASTASES (BM) IN THE ASCEND-1 TRIAL
Bibliographic record
Abstract
BACKGROUND: Ceritinib is an ALK inhibitor (ALKi) recently approved for patients with ALK+ advanced NSCLC. Efficacy and safety were evaluated in a subset of patients with BM in the phase I ASCEND-1 study. In 246 patients with ALK+ NSCLC who received ceritinib 750 mg/day, overall response rate (ORR) was 58.5% (95% CI: 52.1, 64.8) and was 66.3% (55.1, 76.3) and 54.6% (46.6, 62.4) in ALKi-naïve and ALKi-treated patients, respectively. METHODS: Patients with ALK+ advanced NSCLC and clinically/neurologically stable BM at baseline who received ceritinib 750 mg/day were analyzed for response based on investigator assessment. RESULTS: Among 246 patients, 124 had BM at baseline, including 98 with prior ALKi treatment and 26 who were ALKi-naïve. The BM subset had a median age of 51.0 years; 85.5% with an ECOG PS ≤1; 58.1% Caucasian, 39.5% Asian; median time from NSCLC diagnosis to first ceritinib dose was 20.5 months. Median duration of exposure was 27 weeks. ORR was 54.0% (44.9, 63.0) in the full cohort [50.0% (39.7, 60.3) in ALKi-treated and 69.2% (48.2, 85.7) in ALKi-naïve]. Median DOR was 7.0 mo (5.5, 9.7) for the full subset [6.9 mo (4.8, 8.5) in ALKi-treated and not estimable (NE) in ALKi-naïve]. Median PFS was 6.9 mo (5.4, 8.4) [6.7 mo (4.9, 8.4) and 8.3 mo (4.6, NE) in ALKi-treated and ALKi-naïve]. Measurable target lesions were identified at baseline in 14 patients (10 ALKi-treated, 4 ALKi-naïve). Seven of these patients achieved a response in the brain (4 ALKi-treated, 3 ALKi-naïve) and 3 had stable disease (all ALKi-treated). The most common adverse events (all grades) in all patients and the BM subset were diarrhea (86% vs 79%), nausea (80% vs 82%), and vomiting (60% vs 63%). CONCLUSIONS: Ceritinib has clinically significant durable efficacy in patients with ALK+ NSCLC, including patients with BM, regardless of prior ALKi treatment.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| 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".