Identification of Driver Mutations and Risk Stratification in Lung Adenocarcinoma via Liquid Biopsy
Bibliographic record
Abstract
Background: Liquid biopsy using plasma cfDNA has been established as a tool for informing the management of advanced-stage NSCLC. However, its effectiveness in early lung cancer detection, including the identification of high-risk cases, remains to be determined. Methods: We analyzed plasma cfDNA and matched tumors from 117 stage I–IV lung adenocarcinoma cases and compared the variants identified across all stages using the Oncomine Precision Assay on the GenexusTM next-generation sequencing platform. Results: Cancer-specific mutations were detected in plasma from approximately 72% (84/117) of cases (all stages), with detection rates increasing by stage. Concordance between cfDNA and tumor tissue also increased with stage 0% (stage I), 19% (stage II), 45% (stage III), and 75% (stage IV). KRAS mutations were concordant in approximately 22% (6/27) of stage II and 46% (11/24) of stage III cases. Clinically important EGFR variants showed concordance in 11% (1/9) of stage II and 80% (8/10) in stage III/IV cases. Actionable mutations, targetable with FDA-approved drugs, were detected in 11% (4/37) of stage II, 27% (12/45) of stage III, and 55% (4/9) of stage IV cases, underscoring the potential of liquid biopsy for early detection of therapeutic targets. Moreover, co-occurring mutations with varying actionability were identified more frequently in plasma than in tumor tissues. Plasma detection of clinically important KRAS and EGFR variants was mostly associated with advanced-stage disease, suggesting the presence of these variants in plasma as a potential indication of disease progression. Conclusions: Liquid biopsy holds promise for identifying high-risk lung adenocarcinoma cases and serves as a complementary diagnostic tool in advanced stages, enhancing disease management strategies.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 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) | 0.001 | 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".