Custom hereditary breast cancer gene panel selectively amplifies target genes for reliable variant calling
Bibliographic record
Abstract
Abstract Background: Target enrichment coupled with next generation sequencing provide high-throughput approaches for screening several genes of interest. These approaches facilitate screening a panel of genes for mutations associated with inherited breast cancer for research, diagnostic, and genetic counseling applications. Objective: To evaluate the performance of our custom 13 gene breast cancer panel, based on singleplex PCR, developed by WaferGen BioSystems. The panel was evaluated using patient-derived DNA samples, in terms of target enrichment efficiency, off-target enrichment, uniformity of target capture, effect of GC content of target regions on coverage depth, and concordance with validated variant calls. Results: At least 90% of target sequence for each gene was captured at 30x or greater. We evaluated uniformity of target capture across samples by calculating the percentage of samples with at least 90% of total target captured at 100x or greater and found 92% (33/36 samples) uniformity for our panel. Off-target enrichment ranges between 7.2% and 22.3%. We found perfect concordance between our custom panel and the Qiagen human breast cancer panel for functionally annotated variant calls in high read depth shared target regions. Altogether, there was agreement between the panels for 779 variants at 41 loci. We also confirmed 10 pathogenic mutations, initially discovered by Sanger sequencing, in the appropriate samples following target enrichment using our custom WaferGen panel. Conclusion: Our custom hereditary breast cancer panel is sensitive to the desired target genes and facilitates deep sequencing for reliable variant calling.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".