Real-world treatment sequencing and effectiveness of second- and third-generation ALK tyrosine kinase inhibitors for ALK-positive advanced non-small cell lung cancer
Bibliographic record
Abstract
INTRODUCTION: With multiple targeted therapies approved for anaplastic lymphoma kinase (ALK)-positive metastatic non-small cell lung cancer (NSCLC), it is increasingly important to understand outcomes with various sequences of next-generation ALK tyrosine kinase inhibitors (TKIs). We describe contemporary sequencing patterns and treatment effectiveness of first-line (1L) and second-line (2L) treatments in patients who received second-generation ALK TKIs in the 1L treatment of ALK-positive NSCLC in the United States. METHODS: A cohort of adults with ALK-positive advanced NSCLC who initiated treatment with 1L alectinib or brigatinib between June 2017 and April 2021 in the Flatiron Health electronic health record-derived de-identified database were followed through April 2023. Time to treatment discontinuation (TTD) in 1L and 2L, TTD on 1L plus 2L sequential therapy (TTD2), and total time on sequential ALK TKI therapy (including beyond 2L) were evaluated. RESULTS: Patients (N=273) were followed up for a median duration of 28.9 months. Among patients who discontinued 1L therapy, 22% died after 1L discontinuation (median time from discontinuation to death, 4.0 months) without receiving 2L therapy. Median (95% confidence interval [CI]) TTD was 21.9 (15.2-25.8) and 7.3 (5.3-10.2) months in 1L and 2L, respectively. Median (95% CI) TTD2 was 29.4 (25.1-36.1) months and total time on sequential ALK TKI treatment was 28.0 (23.6-32.9) months. CONCLUSIONS: In this large real-world study, TTD2 and the total time on sequential ALK TKIs was approximately 2.5 years. The high attrition rate from 1L to 2L and the longest clinical benefit observed with 1L therapy support using the drug with the longest 1L effectiveness up front in patients with ALK-positive advanced NSCLC.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".