Abstract 4265: Risks of familial breast cancer associated with known and proposed breast cancer susceptibility genes
Bibliographic record
Abstract
Abstract A better understanding of gene-specific risks for development of breast cancer will lead to improved screening, prevention, and therapeutic strategies for individuals identified to carry germline mutations. We performed targeted massively-parallel sequencing to identify mutations and large genomic rearrangements in 26 known or proposed breast cancer susceptibility genes in 2134 BRCA-negative women with familial breast cancer (FBC). A case-control analysis was performed comparing the frequency of internally classified mutations identified in FBC cases to that in non-Finnish European controls from the Exome Aggregation Consortium (ExAC) excluding samples from The Cancer Genome Atlas. Including large genomic rearrangements, mutations were identified in 8.2% of FBC cases compared to 6.2% of ExAC controls, including mutations in high-penetrance genes (0.6% in cases vs. 0.1% in controls), moderate-penetrance genes (3.7% vs 1.7%), and seven cases with two mutations (0.3%). The remainder of FBC cases and ExAC controls had mutations in proposed breast cancer genes (1.6% of cases vs 2.4% of controls), Lynch syndrome genes (0.5% vs. 0.5%) or were heterozygous MUTYH carriers (1.5% vs. 1.5%). Case-control analysis demonstrated significant associations with FBC for ATM, PALB2, and TP53 mutations (OR>3.0, p<10-4), BARD1 mutations (OR=3.2, p=0.012), and CHEK2 truncating mutations (OR=1.6, p=0.041). Our results therefore demonstrate that only approximately 4% of BRCA1/2 negative FBC patients have mutations in genes definitively associated with breast cancer at this time. Large case-control studies are needed to fully evaluate the breast cancer risks associated with moderate penetrance and proposed breast cancer susceptibility genes. Citation Format: Kara N. Maxwell, Thomas Paul Slavin, Jenna M. Lilyquist, Joseph Vijai, Susan L. Neuhausen, Steven N. Hart, Vignesh Ravichandran, Tinu Thomas, Ann Maria, Kasmintan A. Schrader, Raymond Moore, Chunling Hu, Brad Wubbenhorst, Brandon M. Wenz, Kurt D'Andrea, Susan M. Domchek, Mark E. Robson, Paulo Peterlongo, Paolo Radice, James M. Ford, Judy E. Garber, Csilla Szabo, Kenneth Offit, Katherine L. Nathanson, Fergus J. Couch, Jeffrey N. Weitzel. Risks of familial breast cancer associated with known and proposed breast cancer susceptibility genes [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2017; 2017 Apr 1-5; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2017;77(13 Suppl):Abstract nr 4265. doi:10.1158/1538-7445.AM2017-4265
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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 | 0.002 |
| 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 | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".