Comparison of Next-Generation Sequencing Panels and Platforms for Detection and Verification of Somatic Tumor Variants for Clinical Diagnostics
Bibliographic record
Abstract
Use of next-generation sequencing to detect somatic variants in DNA extracted from formalin-fixed, paraffin-embedded tumor tissues poses a challenge for clinical molecular diagnostic laboratories because of variable DNA quality and quantity, and the potential to detect low allele frequency somatic variants difficult to verify by non-next-generation sequencing methods. We evaluated somatic variant detection performance of the MiSeq and Ion Proton benchtop sequencers using two commercially available panels, the TruSeq Amplicon Cancer Panel and the AmpliSeq Cancer Hotspot Panel Version 2. Both the MiSeq-TruSeq Amplicon Cancer Panel and Ion Proton-AmpliSeq Cancer Hotspot Panel Version 2 were comparable in terms of detection of somatic variants and allele frequency determination using DNA extracted from tumor tissue. Concordance was 100% between the panels for detection of somatic variants in genomic regions tested by both panels, including 27 variants present at low somatic allele frequency (<15%). Use of both the MiSeq and Ion Proton platforms in a combined workflow enabled detection of potentially actionable variants with importance for patient diagnosis, prognosis, or treatment in 49% (305/621) of cases. Overall, a combined workflow using both platforms enabled successful molecular profiling of 96% (621/644) of tumor samples, and provided an approach for verification of somatic variants not amenable to verification by Sanger sequencing (<15% variant allele frequency).
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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 | 0.012 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 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".