Abstract A34: BRCA1 and BRCA2 mutation spectrum across 5, 509 high-risk individuals identifies pathogenic variants associated with ethnicity, age of diagnosis, and type of cancer
Bibliographic record
Abstract
Abstract Hereditary Breast and Ovarian Cancer Sydrome (HBOC) accounts for approximately 5-10% of breast and ovarian cancer cases and germline mutations of the BRCA1 and BRCA2 genes confer substantially increased risk. However, the risk of developing cancer, the type of cancer and the associated age at diagnosis vary depending on the type of mutation carried and the individual's ethnic background and gender. Screening of BRCA1 and BRCA2 mutations was conducted on 5, 512 high-risk individuals with a prior probability of carrying a pathogenic mutation (>10% chance) from the Advanced Molecular Diagnostics Laboratory (AMDL) (Mount Sinai Hospital, Toronto). Information on age, type of cancer diagnosed and age at diagnosis, ethnicity and family history were collected. Cumulative incidence competing risk and Fine-Gray proportional hazard regression analyses were generated to determine factors associated with earlier diagnosis of cancer. Among 5,029 women and 480 men who underwent testing, a total of 845 unique variants were identified of which, 289 (34.2%) are pathogenic. There were 603 female mutation carriers, of these, 303 were affected with breast or ovarian cancer (50%), 16 with another cancer (2.25%) and 284 were unaffected (47.1%). We also identified 20 different ethnic groups presenting with at least 3 unique variants. Six ethnic groups carried at least one variant associated with an earlier diagnosis of cancer and differential breast or ovarian cancer incidence. For instance, among the Ashkenazi Jewish cohort, the c.68_69del variant was associated with significantly earlier age at diagnosis for ovarian cancer incidence (Gray's test = 40.6; Fine-Gray HR = 1.12, 95% CI = 1.05 – 1.31). By screening a diverse cohort of high-risk individuals for BRCA1 and BRCA2 mutations, we identified pathogenic variants associated with an earlier age of diagnosis and differential incidence of breast or ovarian cancer among unique ethnic groups. Citation Format: Andrew H. Girgis, Marina Wang, Alexa Fine Kathleen-Rose ZakoorSam Khalouei, George Charames, Jordan Lerner-Ellis. BRCA1 and BRCA2 mutation spectrum across 5, 509 high-risk individuals identifies pathogenic variants associated with ethnicity, age of diagnosis, and type of cancer. [abstract]. In: Proceedings of the AACR Special Conference on Advances in Breast Cancer Research; Oct 17-20, 2015; Bellevue, WA. Philadelphia (PA): AACR; Mol Cancer Res 2016;14(2_Suppl):Abstract nr A34.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".