Efficacy of Lorlatinib in Treatment-Naive Patients With ALK-Positive Advanced NSCLC in Relation to EML4::ALK Variant Type and ALK With or Without TP53 Mutations
Bibliographic record
Abstract
INTRODUCTION: Lorlatinib, a third-generation ALK tyrosine kinase inhibitor, improved outcomes compared with crizotinib in patients with previously untreated ALK-positive advanced NSCLC in the phase 3 CROWN study. Here, we investigated response correlates using plasma circulating tumor DNA (ctDNA) and tumor tissue profiling. METHODS: ALK fusions and ALK with or without TP53 mutations were assessed by next-generation sequencing. End points included objective response rate (ORR), duration of response, and progression-free survival (PFS) by blinded independent central review on the basis of EML4::ALK variants and ALK with or without TP53 or other mutation status. RESULTS: ALK fusions were detected in the ctDNA of 62 patients in the lorlatinib arm and 64 patients in the crizotinib arm. ORRs were numerically higher with lorlatinib versus crizotinib for EML4::ALK variant 1 (v1; 80.0% versus 50.0%) and variant 2 (v2; 85.7% versus 50.0%) but were similar between the arms for variant 3 (v3; 72.2% versus 73.9%). Median PFS in the lorlatinib arm was not reached for EML4::ALK v1 and v2 and was 33.3 months for v3; in the crizotinib arm, median PFS was 7.4 months, not reached, and 5.5 months, respectively. ORRs and PFS were improved with lorlatinib versus crizotinib regardless of TP53 mutation status and in patients harboring preexisting bypass pathway resistance alterations. In the lorlatinib arm, PFS was lower in patients who had a co-occurring TP53 mutation. Results from ctDNA analysis were similar to those observed with tumor tissue samples. CONCLUSIONS: Patients with untreated ALK-positive advanced NSCLC derived greater clinical benefits, with higher ORRs and potentially longer PFS, when treated with lorlatinib compared with crizotinib, independent of EML4::ALK variant or ALK mutations, TP53 mutations, or bypass resistance alterations.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 teacher head, 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".