Early Circulating Tumor DNA Dynamics and Efficacy of Lorlatinib in Patients With Treatment-Naive, Advanced, ALK-Positive NSCLC
Bibliographic record
Abstract
Introduction Circulating tumor DNA (ctDNA) has been used as a biomarker for prognostication and response to treatment. Here, we evaluate ctDNA as a potential biomarker for response to lorlatinib, a third-generation ALK tyrosine kinase inhibitor in patients with treatment-naive, advanced, ALK -positive NSCLC in the ongoing phase 3 CROWN study (NCT03052608). Methods Molecular responses were calculated using mean variant allele frequency (VAF), longitudinal mean change in VAF (dVAF), and ratio to baseline. Efficacy assessments (progression-free survival [PFS] and objective response rate) were paired with individual patient ctDNA and analyzed for association. Results Compared with baseline, mean VAF at week 4 was decreased in both treatment arms. Considering all detected somatic variants, a reduction in dVAF (≤0) was associated with a longer PFS in the lorlatinib arm. The hazard ratio (HR) for a dVAF less than or equal to 0 versus more than 0 was 0.50 (95% confidence interval [CI]: 0.23–1.12) in the lorlatinib arm. A similar association was not observed for crizotinib (HR = 1.00, 95% CI: 0.49–2.03). Comparing molecular responders with nonresponders, patients treated with lorlatinib who had a molecular response had longer PFS (HR = 0.37, 95% CI: 0.16–0.85); patients treated with crizotinib who had a molecular response had similar PFS as those without a molecular response (HR = 1.48, 95% CI: 0.67–3.30). Conclusions In patients with treatment-naive, advanced, ALK -positive NSCLC, early ctDNA dynamics predicted better outcome with lorlatinib but not with crizotinib. These results suggest that ctDNA may be used to monitor and potentially predict efficacy of lorlatinib 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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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 | 0.000 |
| 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".