Fundamental Concepts in the Application of Plasma Genotyping (Liquid Biopsy) to <i>EGFR</i> Mutation Detection in Non–Small-Cell Lung Cancer
Bibliographic record
Abstract
Plasma genotyping has rapidly evolved from an investigational technology into a standard-of-care tool used to direct therapy in metastatic non–small-cell lung cancer (NSCLC). Multiple testing platforms exist for plasma genotyping, each with unique test characteristics and scientific validation. The optimal use and interpretation of plasma genotyping requires understanding of cell-free DNA biology, the assay characteristics of the available testing technologies, and the application of testing in each clinical scenario. Current recommendations for plasma genotyping in metastatic NSCLC are limited to patients with newly diagnosed disease and those with acquired resistance to targeted therapy, in particular, epidermal growth factor receptor (EGFR) kinase inhibitors. In newly diagnosed metastatic NSCLC, under certain circumstances, plasma genotyping is useful for the detection of targetable genomic alterations or nontargetable driver alterations (eg, KRAS) that are mutually exclusive with targetable alterations. In patients with acquired resistance to therapy, such as EGFR T790M-mediated acquired resistance to EGFR kinase inhibitors, plasma genotyping may detect resistance mutations missed by standard tissue genotyping because of tumor heterogeneity. In both scenarios, the high specificity and positive predictive value of validated plasma genotyping assays allow for the reliable selection of therapy on the basis of a positive plasma genotyping result. However, the modest sensitivity of these assays requires that negative results be confirmed by tissue genotyping with repeat biopsy, if necessary. There is considerable potential for plasma genotyping in the detection of early-stage disease, for patients at risk for disease recurrence, and as an integrated biomarker of therapeutic response in clinical trials of novel therapies.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.000 | 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 teacher head, 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".