Abstract 2598: Predicting breast and ovarian cancer risks for BRCA1 and BRCA2 mutation carriers using polygenic risk scores
Bibliographic record
Abstract
Abstract Women who carry a pathogenic mutation in the BRCA1 or BRCA2 gene are at high risk of breast (BC) and ovarian cancer (OC). Their clinical management usually includes invasive risk-reducing interventions with substantial side effects. Improved personalized cancer risk estimates may help to identify women at particularly high risk or with high risk of disease at early ages who may benefit from early intervention as well as women at lower risk who may opt to delay surgery or chemoprevention. Genome-wide association studies have identified >100 common genetic variants that are associated with BC or OC risks. Several of these variants are also individually associated with risk of BC or OC for BRCA1 and BRCA2 mutation carriers. However, no study has evaluated the combined effects of all the known common genetic variants on BC or OC risk for BRCA1/2 mutation carriers. We constructed polygenic risk scores (PRS) based on results of genetic association studies conducted in the general population. Each PRS was formed by the sum of the number of risk alleles across the variants weighted by their log-Odds Ratio estimate from population-based studies of BC or OC. We investigated 3 PRS for BC (overall, estrogen receptor (ER) positive, and ER-negative) and one PRS for OC. We used data for 15,252 BRCA1 and 8,211 BRCA2 female carriers. The association of each PRS with BC or OC risk was evaluated using a weighted cohort analysis with time to diagnosis as the outcome and estimated the Hazard Ratios (HR) per standard deviation increase in the PRS. All PRS were significantly associated with cancer risks for BRCA1/2 carriers. The PRS for ER-negative BC displayed the strongest association with BC risk in BRCA1 carriers (HR = 1.29 [1.25-1.33], p = 8×10−64). In BRCA2 carriers, the strongest association was seen for the overall BC PRS (HR = 1.26 [1.21-1.31], p = 3×10−27). The OC PRS was strongly associated with OC risk for both BRCA1 and BRCA2 carriers. These relative risks translate to large differences in absolute risks for carriers: e.g., the OC risk was 6% by age 80 for BRCA2 carriers at the 10th percentile of the OC PRS compared with 19% risk for those at the 90th percentile of PRS. Our findings demonstrate that BC and OC PRS derived from studies in the general population are predictive of cancer risks in BRCA1 and BRCA2 carriers. Incorporation of the PRS into risk prediction models would improve risk prediction and hence inform decisions on cancer risk management. Citation Format: Karoline Kuchenbaecker, Jacques Simard, Kenneth Offit, Fergus J. Couch, Douglas F. Easton, Georgia Chenevix-Trench, Antonis C. Antoniou, Consortium of Investigators of Modifiers of BRCA1/2. Predicting breast and ovarian cancer risks for BRCA1 and BRCA2 mutation carriers using polygenic risk scores. [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 2598.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".