Lorlatinib Versus Crizotinib in Patients With Advanced <i>ALK</i> -Positive Non–Small Cell Lung Cancer: 5-Year Outcomes From the Phase III CROWN Study
Bibliographic record
Abstract
PURPOSE Lorlatinib improved progression-free survival (PFS) and intracranial activity versus crizotinib in patients with previously untreated, advanced, ALK -positive non–small cell lung cancer (NSCLC) in the phase III CROWN study. Here, we report long-term outcomes from CROWN after 5 years of follow-up. METHODS Two hundred ninety-six patients with ALK -positive NSCLC were randomly assigned 1:1 to receive lorlatinib 100 mg once daily (n = 149) or crizotinib 250 mg twice daily (n = 147). This post hoc analysis presents updated investigator-assessed efficacy outcomes, safety, and biomarker analyses. RESULTS With a median follow-up for PFS of 60.2 and 55.1 months, respectively, median PFS was not reached (NR [95% CI, 64.3 to NR]) with lorlatinib and 9.1 months (95% CI, 7.4 to 10.9) with crizotinib (hazard ratio [HR], 0.19 [95% CI, 0.13 to 0.27]); 5-year PFS was 60% (95% CI, 51 to 68) and 8% (95% CI, 3 to 14), respectively. Median time to intracranial progression was NR (95% CI, NR to NR) with lorlatinib and 16.4 months (95% CI, 12.7 to 21.9) with crizotinib (HR, 0.06 [95% CI, 0.03 to 0.12]). Safety profile was consistent with that in prior analyses. Emerging new ALK resistance mutations were not detected in circulating tumor DNA collected at the end of lorlatinib treatment. CONCLUSION After 5 years of follow-up, median PFS has yet to be reached in the lorlatinib group, corresponding to the longest PFS ever reported with any single-agent molecular targeted treatment in advanced NSCLC and across all metastatic solid tumors. These results coupled with prolonged intracranial efficacy and absence of new safety signals represent an unprecedented outcome for patients with advanced ALK -positive NSCLC and set a new benchmark for targeted therapies in cancer.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| 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 | 0.000 | 0.001 |
| 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".