Liquid Biopsy: A Multi-Parametric Analysis of Mutation Status, Circulating Tumor Cells and Inflammatory Markers in EGFR-Mutated NSCLC
Bibliographic record
Abstract
The liquid biopsy has the potential to improve patient care in the diagnostic and therapeutic setting in non-small cell lung cancer (NSCLC). Consented patients with epidermal growth factor receptor (EGFR) positive disease (n = 21) were stratified into two cohorts: those currently receiving EGFR tyrosine kinase inhibitor (TKI) therapy (n = 9) and newly diagnosed EGFR TKI treatment-naïve patients (n = 12). Plasma genotyping of cell-free DNA was carried out using the FDA-approved cobas® EGFR mutation test v2 and compared to next generation sequencing (NGS) cfDNA panels. Circulating tumor cell (CTC) numbers were correlated with treatment response and EGFR exon 20 p.T790M. The prognostic significance of the neutrophil to lymphocyte ratio (NLR) and lactate dehydrogenase (LDH) was also investigated. Patients in cohort 1 with an EGFR exon 20 p.T790M mutation progressed more rapidly than those with an EGFR sensitizing mutation, while patients in cohort 2 had a significantly longer progression-free survival (p = 0.04). EGFR exon 20 p.T790M was detected by liquid biopsy prior to disease progression indicated by computed tomography (CT) imaging. The cobas® EGFR mutation test detected a significantly greater number of exon 20 p.T790M mutations (p = 0.05). High NLR and derived neutrophil to lymphocyte ratio (dNLR) were associated with shorter time to progression and worse survival outcomes (p < 0.05). High LDH levels were significantly associated with shorter time to disease progression (p = 0.03). These data support the use of liquid biopsy for monitoring EGFR mutations and inflammatory markers as prognostic indicators in 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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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) | 0.001 | 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".