Functional Characterization and a Real-World Clinical Laboratory Pilot of the Foundation for the National Institutes of Health Circulating Tumor DNA Quality Control Materials
Bibliographic record
Abstract
PURPOSE We previously developed quality control materials (QCMs) to aid in the development of circulating tumor DNA (ctDNA) assays. In this study, we further characterize the performance of the QCMs relative to clinical samples. METHODS QCMs were provided by three manufacturers. To functionally characterize the QCMs, we (1) evaluated EGFR L858R and ex19del (range, 0.5%-5.0% variant allele frequency [VAF]) in QCMs compared with clinical samples by droplet digital polymerase chain reaction (ddPCR), targeted-amplicon sequencing (Tag-seq), and hybrid capture next-generation sequencing (NGS); and (2) evaluated the QCMs and clinical samples near the Tag-seq limit of detection. A clinical pilot was also conducted in 11 clinical laboratories spanning four continents. RESULTS For functional characterization, part 1, QCM VAFs for hybrid capture were similar to ddPCR for EGFR L858R but lower for ex19del. By contrast, hybrid capture results for EGFR L858R clinical samples showed a positive trend compared with ddPCR. For amplicon NGS, QCMs performed similarly to clinical samples for both variants. For part 2, observed hit rates approximated expected values. In the clinical pilot, median ex19del VAF was higher for Tag-seq than hybrid capture for both 1.0% and 0.5% QCM formulations. Median QCM L858R VAFs were similar for Tag-seq and hybrid capture, with greatest interlaboratory differences observed for Thermo Fisher Scientific QCMs. For non- EGFR variants, we observed assay and QCM-dependent trends, with no particular QCM or assay driving these trends. CONCLUSION This project revealed unexpected differences in performance of both assays and QCMs. These findings highlight the need for further validation across diverse alteration types and merit consideration by laboratories that rely on QCMs to develop and perform ctDNA assays for diagnostic applications.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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".