Validation of the BOADICEA Model in a Prospective Cohort of <i>BRCA1/2</i> Pathogenic Variant Carriers
Bibliographic record
Abstract
Abstract Background No validation has been conducted for the BOADICEA multifactorial breast cancer risk prediction model specifically in BRCA1/2 pathogenic variant (PV) carriers to date. Here, we evaluated the performance of BOADICEA in predicting five-year breast cancer risks in a prospective cohort of BRCA1/2 PV carriers ascertained through clinical genetic centres. Methods We evaluated the model calibration and discriminatory ability in the prospective TRANsIBCCS cohort study comprising 1,614 BRCA1 and 1,365 BRCA2 PV carriers (209 incident cases). Study participants had lifestyle, reproductive, hormonal, anthropometric risk factor information, a polygenic risk score based on 313 single nucleotide polymorphisms, and family history information. Results The full multifactorial model considering family history together with all other risk factors was well calibrated overall (E/O=1.07, 95%CI:0.92-1.24) and in quintiles of predicted risk. Discrimination was maximized when all risk factors were considered (Harrell’s C-index=0.70, 95%CI:0.67-0.74; AUC=0.79, 95%CI:0.76-0.82). The model performance was similar when evaluated separately in BRCA1 or BRCA2 PV carriers. The full model identified 5.8%, 12.9% and 24.0% of BRCA1/2 PV carriers with five-year breast cancer risks of <1.65%, <3% and <5% respectively, risk thresholds commonly used for different management and risk-reduction options. Conclusion BOADICEA may be used to aid personalised cancer risk management and decision making for BRCA1 and BRCA2 PV carriers. It is implemented in the free-access CanRisk tool ( www.canrisk.org ). What is already known on this topic No study has assessed the clinical validity of the multifactorial BOADICEA model for predicting future breast cancer risks specifically for BRCA1/2 pathogenic variant (PV) carriers. What this study adds This is the first study to validate the BOADICEA model based on the joint effects of questionnaire-based risk factors (QRFs), a polygenic risk score (PRS) based on 313 single nucleotide polymorphisms, and cancer family history information in BRCA1/2 PV carriers ascertained through clinical genetic centres. The model is well calibrated and discriminated well in both BRCA1 and BRCA2 carriers. The inclusion of family history, alongside QRFs and the PRS, in predicting cancer risks for PV carriers in clinical genetics settings can improve the calibration within individual risk categories and can result in clinically meaningful levels of breast cancer risk stratification. How this study might affect research, practice or policy BOADICEA is freely available via the CanRisk tool ( www.canrisk.org ). Rather than relying solely on average published penetrance estimates commonly used in genetic clinics for counselling of BRCA1/2 PV carriers, BOADICEA offers more personalized BC risks. This can facilitate informed decision-making regarding the clinical management of BC risk, including considerations for surveillance and the timing of risk-reducing surgery.
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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 | 0.015 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".