The Clinically Actionable Molecular Profile of Early versus Late-Stage Non-Small Cell Lung Cancer, an Individual Age and Sex Propensity-Matched Pair Analysis
Bibliographic record
Abstract
Background: Despite meticulous surgery for non-small cell lung cancer (NSCLC), relapse is as high as 70% at 5 years. Many institutions do not conduct reflexive molecular testing on early stage specimens, although targeted gene therapy may extend life by years in the event of recurrence. This ultimately delays definitive treatment with additional biopsy risking suboptimal tissue acquisition and quality for molecular testing. Objective: To compare molecular profiles of genetic alterations in early and late NSCLC to provide evidence that reflexive molecular testing provides clinically valuable information. Methods: A single-center propensity matched retrospective analysis was conducted using prospectively collected data. Adults with early and late-stage NSCLC had tissue subject to targeted panel-based NGS. Frequencies of putative drivers were compared, with 1:3 matching on the propensity score; p < 0.05 deemed statistically significant. Results: In total, 635 NSCLC patients underwent NGS (59 early, 576 late); 276 (43.5%) females; age 70.9 (±10.2) years; never smokers 140 (22.0%); 527 (83.0%) adenocarcinomas. Unadjusted frequencies of EGFR mutations were higher in the early cohort (30% vs. 18%). Following adjustment for sex and smoking status, similar frequencies for both early and late NSCLC were observed for variants in EGFR, KRAS, ALK, MET, and ROS1. Conclusion: The frequency of clinically actionable variants in early and late-stage NSCLC was found to be similar, providing evidence that molecular profiling should be performed on surgical specimens. This pre-determined profile is essential to avoid treatment delay for patients who will derive clinical benefit from targeted systemic therapy, in the high likelihood of subsequent relapse.
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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 | 0.005 |
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".