Pathogenic and likely pathogenic variant prevalence among the first 10,000 patients referred for next-generation cancer panel testing
Bibliographic record
Abstract
PURPOSE: Germ-line testing for panels of cancer genes using next-generation sequencing is becoming more common in clinical care. We report our experience as a clinical laboratory testing both well-established, high-risk cancer genes (e.g., BRCA1/2, MLH1, MSH2) as well as more recently identified cancer genes (e.g., PALB2, BRIP1), many of which have increased but less well-defined penetrance. METHODS: Clinical genetic testing was performed on over 10,000 consecutive cases referred for evaluation of germ-line cancer genes, and results were analyzed for frequency of pathogenic or likely pathogenic variants, and were stratified by testing panel, gene, and clinical history. RESULTS: Overall, a molecular diagnosis was made in 9.0% of patients tested, with the highest yield in the Lynch syndrome/colorectal cancer panel. In patients with breast, ovarian, or colon/stomach cancer, positive yields were 9.7, 13.4, and 14.8%, respectively. Approximately half of the pathogenic variants identified in patients with breast or ovarian cancer were in genes other than BRCA1/2. CONCLUSION: The high frequency of positive results in a wide range of cancer genes, including those of high penetrance and with clinical care guidelines, underscores both the genetic heterogeneity of hereditary cancer and the usefulness of multigene panels over genetic tests of one or two genes.Genet Med 18 8, 823-832.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".