Noninvasive Diagnosis of Actionable Mutations by Deep Sequencing of Circulating Free DNA in Lung Cancer from Never-Smokers: A Proof-of-Concept Study from BioCAST/IFCT-1002
Bibliographic record
Abstract
PURPOSE: Tumor somatic mutation analysis is part of the standard management of metastatic lung cancer. However, physicians often have to deal with small biopsies and consequently with challenging mutation testing. Circulating free DNA (cfDNA) is a promising tool for accessing the tumor genome as a liquid biopsy. Here, we evaluated next-generation sequencing (NGS) on cfDNA samples obtained from a consecutive series of patients for the screening of a range of clinically relevant mutations. EXPERIMENTAL DESIGN: A total of 107 plasma samples were collected from the BioCAST/IFCT-1002 lung cancer study (never-smokers cohort). Matched tumor DNA (tDNA) was obtained for 68 cases. Multiplex PCR-based assays were designed to target specific coding regions in EGFR, KRAS, BRAF, ERBB2, and PI3KCA genes, and amplicon sequencing was performed at deep coverage on the cfDNA/tDNA pairs using the NGS IonTorrent Personal Genome Machine Platform. RESULTS: CfDNA concentration in plasma was significantly associated with both stage and number of metastatic sites. In tDNA, 50 mutations (36 EGFR, 5 ERBB2, 4 KRAS, 3 BRAF, and 2 PIK3CA) were identified, of which 26 were detected in cfDNA. Sensitivity of the test was 58% (95% confidence interval, 43%-71%) and the estimated specificity was 87% (62%-96%). CONCLUSION: These data demonstrate the feasibility and potential utility of mutation screening in cfDNA using IonTorrent NGS for the detection of a range of tumor biomarkers in patients with metastatic lung cancer.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".