A Window Into Clinical Next-Generation Sequencing–Based Oncology Testing Practices
Bibliographic record
Abstract
CONTEXT: - Detection of acquired variants in cancer is a paradigm of precision medicine, yet little has been reported about clinical laboratory practices across a broad range of laboratories. OBJECTIVE: - To use College of American Pathologists proficiency testing survey results to report on the results from surveys on next-generation sequencing-based oncology testing practices. DESIGN: - College of American Pathologists proficiency testing survey results from more than 250 laboratories currently performing molecular oncology testing were used to determine laboratory trends in next-generation sequencing-based oncology testing. RESULTS: - These presented data provide key information about the number of laboratories that currently offer or are planning to offer next-generation sequencing-based oncology testing. Furthermore, we present data from 60 laboratories performing next-generation sequencing-based oncology testing regarding specimen requirements and assay characteristics. The findings indicate that most laboratories are performing tumor-only targeted sequencing to detect single-nucleotide variants and small insertions and deletions, using desktop sequencers and predesigned commercial kits. Despite these trends, a diversity of approaches to testing exists. CONCLUSIONS: - This information should be useful to further inform a variety of topics, including national discussions involving clinical laboratory quality systems, regulation and oversight of next-generation sequencing-based oncology testing, and precision oncology efforts in a data-driven manner.
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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 | 0.067 | 0.100 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".