A Pan-Canadian Validation Study for the Detection of EGFR T790M Mutation Using Circulating Tumor DNA From Peripheral Blood
Bibliographic record
Abstract
Introduction Genotyping circulating tumor DNA (ctDNA) is a promising noninvasive clinical tool to identify the EGFR T790M resistance mutation in patients with advanced NSCLC with resistance to EGFR inhibitors. To facilitate standardization and clinical adoption of ctDNA testing across Canada, we developed a 2-phase multicenter study to standardize T790M mutation detection using plasma ctDNA testing. Methods In phase 1, commercial reference standards were distributed to participating clinical laboratories, to use their existing platforms for mutation detection. Baseline performance characteristics were established using known and blinded engineered plasma samples spiked with predetermined concentrations of T790M, L858R, and exon 19 deletion variants. In phase II, peripheral blood collected from local patients with known EGFR activating mutations and progressing on treatment were assayed for the presence of EGFR variants and concordance with a clinically validated test at the reference laboratory. Results All laboratories in phase 1 detected the variants at 0.5 % and 5.0 % allele frequencies, with no false positives. In phase 2, the concordance with the reference laboratory for detection of both the primary and resistance mutation was high, with next-generation sequencing and droplet digital polymerase chain reaction exhibiting the best overall concordance. Data also suggested that the ability to detect mutations at clinically relevant limits of detection is generally not platform-specific, but rather impacted by laboratory-specific practices. Conclusions Discrepancies among sending laboratories using the same assay suggest that laboratory-specific practices may impact performance. In addition, a negative or inconclusive ctDNA test should be followed by tumor testing when possible.
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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 | 0.011 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".