Efficacy and Safety of Dose-Escalated Alectinib in Patients With Metastatic ALK-Positive NSCLC and Central Nervous System Relapse on Standard-Dose Alectinib
Bibliographic record
Abstract
Introduction: Central nervous system (CNS) metastases remain a common challenge in patients with ALK-positive NSCLC. We previously reported reinduction of CNS responses using dose-intensified alectinib in two patients with CNS progression on standard-dose alectinib. Nevertheless, this strategy has not been assessed in larger cohorts. Methods: Patients were eligible for this retrospective study if they had metastatic ALK-positive NSCLC with CNS relapse on alectinib 600 mg twice daily dosing and subsequently received escalated dosing (900 mg twice daily) of alectinib. CNS efficacy was assessed per the modified Response Evaluation Criteria in Solid Tumors version 1.1. Results: Among 27 patients, median duration of dose-escalated alectinib was 7.7 months (95% confidence interval [CI]: 4.8-10.9), with median overall time-to-progression (TTP) of 7.1 months (95% CI: 4.4-9.6). Among 25 CNS response-assessable patients, CNS objective response rate was 12.0% (95% CI: 2.5-31.2) and CNS disease control rate was 92.0% (95% CI: 74.0-99.0), with median CNS duration of disease control of 5.3 months (95% CI: 3.4-8.3) and median CNS TTP of 7.1 months (95% CI: 4.4-9.6). Among four patients with measurable CNS disease at baseline, three experienced a best intracranial response of stable disease and one experienced intracranial partial response with CNS TTP ranging from 4.1 to 7.7 months. No patient required drug discontinuation due to treatment-related adverse event or experienced grade 3 or higher treatment-related adverse events. Conclusions: Dose-intensified alectinib was found to have tolerability and activity in patients with ALK-positive NSCLC who experienced CNS relapse on standard-dose alectinib and represents one clinically viable strategy for this population.
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 | 0.001 | 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) | 0.000 | 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".