EGFR circulating tumour DNA testing: identification of predictors of ctDNA detection and implications for survival outcomes
Bibliographic record
Abstract
BACKGROUND: EGFR T790M testing is the standard of care for activating EGFR mutation (EGFRm) non-small cell lung cancer (NSCLC) progressing on 1st/2nd generation TKIs to select patients for osimertinib. Despite sensitive assays, detection of circulating tumour deoxyribonucleic acid (ctDNA) is variable and influenced by clinical factors. The number and location of sites of progressive disease at time of testing were reviewed to explore the effect on EGFR ctDNA detection. The prognostic value of EGFR ctDNA detection on survival outcomes was assessed. METHODS: Following extraction of cell-free DNA from plasma using the QIAamp Circulating Nucleic Acid Kit, custom droplet digital polymerase chair reaction (ddPCR) assays were used to assess EGFR ctDNA using the Bio-Rad QX200 system. The ddPCR assay has a limit of detection of ≤0.15% variant allele fraction. Baseline characteristics and imaging reports at time of EGFR ctDNA testing were reviewed retrospectively for a 1 year period. RESULTS: The study included 177 patients who had an EGFR ctDNA test. Liver (aOR 3.13) or bone (aOR 2.76) progression or 3-5 sites of progression (aOR 2.22) were predictive of EGFR ctDNA detection. The median OS from first ctDNA test after multiple testing iterations was 12.3 m undetectable EGFR ctDNA, 7.6 m for original EGFR mutation only and 24.1 m with T790M (P=0.001). CONCLUSIONS: Patients with liver or bone progression and 3-5 progressing sites are more likely to have informative EGFR ctDNA testing. Detection of EGFR ctDNA is a negative prognostic indicator in the absence of a T790M resistance mutation, potentially reflecting the disease burden in the absence of targeted therapy options.
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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 | 0.001 | 0.001 |
| 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".