Population-based germline breast cancer gene association studies and meta-analysis to inform wider mainstream testing
Bibliographic record
Abstract
BACKGROUND: Germline genetic testing, previously restricted to familial and young-onset breast cancer, is now offered increasingly broadly to patients with 'population-type' breast cancer in mainstream oncology clinics, with wide variation in the genes included. PATIENTS AND METHODS: Weighted meta-analysis was carried out for three population-based case-control studies (BRIDGES, CARRIERS and UK Biobank) comprising in total 101 397 women with breast cancer and 312 944 women without breast cancer, to quantify 37 putative breast cancer susceptibility genes (BCSGs) for the frequency of pathogenic variants (PVs) in unselected, 'population-type' breast cancer cases and their association with breast cancer and its subtypes. RESULTS: Meta-analysed odds ratios (ORs) and frequencies of PVs in 'population-type' breast cancer cases were generated for BRCA1 (OR 8.73, 95% confidence interval (CI) 7.47-10.20; 1 in 101), BRCA2 (OR 5.68, 95% CI 5.13-6.30; 1 in 68) and PALB2 (OR 4.30, 95% CI 3.68-5.03; 1 in 187). For both CHEK2 (OR 2.40, 95% CI 2.21-2.62; 1 in 73) and ATM (OR 2.16, 95% CI 1.93-2.41; 1 in 132) subgroup analysis showed a stronger association with oestrogen receptor-positive disease. The magnitude of association and frequency of PVs were low for RAD51C (OR 1.53, 95% CI 1.29-2.04; 1 in 913), RAD51D (OR 1.76, 95% CI 1.29-2.41; 1 in 1079) and BARD1 (OR 2.34, 95% CI 1.85-2.97; 1 in 672); frequencies and associations were higher when the analysis was restricted to triple-negative breast cancers. The PV frequency in 'population-type' breast cancer cases was very low for 'syndromic' BCSGs TP53 (1 in 1844), STK11 (1 in 11 525), CDH1 (1 in 2668), PTEN (1 in 3755) and NF1 (1 in 1470), with metrics of association also modest ranging from OR 3.62 (95% CI 1.98-6.61) for TP53 down to OR 1.60 (95% CI 0.48-5.30) for STK11. CONCLUSIONS: These metrics reflecting 'population-type' breast cancer will be informative in defining the appropriate gene set as we continue to expand to germline testing to an increasingly unselected group of breast cancer cases.
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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 | 0.032 | 0.074 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.019 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".