Abstract 5220: Evaluation of ACMG guideline classified variants in 180 cancer and incidental non-cancer genes in families with breast/ovarian cancer
Bibliographic record
Abstract
Abstract Sequencing tests assaying panels of genes or whole exomes are widely available for cancer risk evaluation. However, methods for classification of variants resulting from this testing are not well studied. We evaluated the ability of American College of Medical Genetics and Genomics (ACMG) guidelines to define the rate of mutations and variants of uncertain significance (VUS) in 180 medically relevant genes, including all ACMG designated reportable cancer and non-cancer genes, in individuals who met guidelines for hereditary cancer risk evaluation. We performed whole exome sequencing in 404 individuals in 257 families and classified 1640 variants from these genes. Potentially clinically actionable (likely-pathogenic/pathogenic, LP/P) versus nonactionable (VUS/likely-benign/benign) calls were 92% and 88% concordant with locus specific databases and Clinvar, respectively. LP/P mutations were identified in 11 of 25 breast cancer susceptibility genes in 27 BRCA1/2 negative families (11%). Evaluation of 84 additional autosomal dominant cancer susceptibility genes identified LP/P mutations in only four additional families (1.7%), suggesting they do not influence risk in this cohort. However, individuals from nine of 257 families (3.5%) had incidental LP/P mutations in 32 non-cancer disease genes, and 7% of individuals were monoallelic carriers of an LP/P mutation in 39 autosomal recessive cancer syndrome genes. Furthermore, 90% of individuals had at least one VUS. In summary, these data support the clinical utility of ACMG variant classification guidelines. In addition, evaluation of extended panels of cancer genes in breast/ovarian cancer families leads to only an incremental clinical benefit but substantially increases the complexity of the results. Citation Format: Kara N. Maxwell, Steven N. Hart, Joseph Vijai, Kasmintan A. Schrader, Tinu Thomas, Bradley Wubbenhorst, Vignesh Ravichandran, Raymond M. Moore, Chunling Hu, Lucia Guidugli, Brandon Wenz, Thomas P. Slavin, Susan M. Domchek, Mark E. Robson, Csilla Szabo, Susan L. Neuhausen, Jeffrey N. Weitzel, Kenneth Offit, Fergus J. Couch, Katherine L. Nathanson. Evaluation of ACMG guideline classified variants in 180 cancer and incidental non-cancer genes in families with breast/ovarian cancer. [abstract]. In: Proceedings of the 107th Annual Meeting of the American Association for Cancer Research; 2016 Apr 16-20; New Orleans, LA. Philadelphia (PA): AACR; Cancer Res 2016;76(14 Suppl):Abstract nr 5220.
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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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.001 | 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".