Early Clearance of Plasma <i>Epidermal Growth Factor Receptor</i> Mutations as a Predictor of Outcome on Osimertinib in Advanced Non–Small Cell Lung Cancer; Exploratory Analysis from AURA3 and FLAURA
Bibliographic record
Abstract
PURPOSE: Plasma circulating tumor DNA (ctDNA) analysis is used for genotyping advanced non-small cell lung cancer (NSCLC); monitoring dynamic ctDNA changes may be used to predict outcomes. PATIENTS AND METHODS: This was a retrospective, exploratory analysis of two phase III trials [AURA3 (NCT02151981), FLAURA (NCT02296125)]. All patients had EGFR mutation-positive (EGFRm; ex19del or L858R) advanced NSCLC; AURA3 also included T790M-positive NSCLC. Osimertinib (FLAURA, AURA3), or comparator EGFR-tyrosine kinase inhibitor (EGFR-TKI; gefitinib/erlotinib; FLAURA), or platinum-based doublet chemotherapy (AURA3) was given. Plasma EGFRm was analyzed at baseline and Weeks 3/6 by droplet digital PCR. Outcomes were assessed by detectable/non-detectable baseline plasma EGFRm and plasma EGFRm clearance (non-detection) at Weeks 3/6. RESULTS: In AURA3 (n = 291), non-detectable versus detectable baseline plasma EGFRm had longer median progression-free survival [mPFS; HR, 0.48; 95% confidence interval (CI), 0.33-0.68; P < 0.0001]. In patients with Week 3 clearance versus non-clearance (n = 184), respectively, mPFS (months; 95% CI) was 10.9 (8.3-12.6) versus 5.7 (4.1-9.7) with osimertinib and 6.2 (4.0-9.7) versus 4.2 (4.0-5.1) with platinum-pemetrexed. In FLAURA (n = 499), mPFS was longer with non-detectable versus detectable baseline plasma EGFRm (HR, 0.54; 95% CI, 0.41-0.70; P < 0.0001). For Week 3 clearance versus non-clearance (n = 334), respectively, mPFS was 19.8 (15.1 to not calculable) versus 11.3 (9.5-16.5) with osimertinib and 10.8 (9.7-11.1) versus 7.0 (5.6-8.3) with comparator EGFR-TKI. Similar outcomes were observed by Week 6 clearance/non-clearance. CONCLUSIONS: Plasma EGFRm analysis as early as 3 weeks on-treatment has the potential to predict outcomes in EGFRm 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 | 0.002 | 0.002 |
| 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 | 0.001 | 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".