Prevalence of PALB2 mutations in Australian familial breast cancer cases and controls
Bibliographic record
Abstract
INTRODUCTION: PALB2 is emerging as a high-penetrance breast cancer predisposition gene in the order of BRCA1 and BRCA2. However, large studies that have evaluated the full gene rather than just the most common variants in both cases and controls are required before all truncating variants can be included in familial breast cancer variant testing. METHODS: In this study we analyse almost 2000 breast cancer cases sourced from individuals referred to familial cancer clinics, thus representing typical cases presenting in clinical practice. These cases were compared to a similar number of population-based cancer-free controls. RESULTS: We identified a significant excess of truncating variants in cases (1.3 %) versus controls (0.2 %), including six novel variants (p = 0.0001; odds ratio (OR) 6.58, 95 % confidence interval (CI) 2.3-18.9). Three of the four control individuals carrying truncating variants had at least one relative with breast cancer. There was no excess of missense variants in cases overall, but the common c.1676A > G variant (rs152451) was significantly enriched in cases and may represent a low-penetrance polymorphism (p = 0.002; OR 1.24 (95 % CI 1.09-1.47). CONCLUSIONS: Our findings support truncating variants in PALB2 as high-penetrance breast cancer susceptibility alleles, and suggest that a common missense variant may also lead to a low level of increased breast cancer risk.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".