Whole Exome Sequencing Suggests Much of Non-BRCA1/BRCA2 Familial Breast Cancer Is Due to Moderate and Low Penetrance Susceptibility Alleles
Bibliographic record
Abstract
The identification of the two most prevalent susceptibility genes in breast cancer, BRCA1 and BRCA2, was the beginning of a sustained effort to uncover new genes explaining the missing heritability in this disease. Today, additional high, moderate and low penetrance genes have been identified in breast cancer, such as P53, PTEN, STK11, PALB2 or ATM, globally accounting for around 35 percent of the familial cases. In the present study we used massively parallel sequencing to analyze 7 BRCA1/BRCA2 negative families, each having at least 6 affected women with breast cancer (between 6 and 10) diagnosed under the age of 60 across generations. After extensive filtering, Sanger sequencing validation and co-segregation studies, variants were prioritized through either control-population studies, including up to 750 healthy individuals, or case-control assays comprising approximately 5300 samples. As a result, a known moderate susceptibility indel variant (CHEK2 1100delC) and a catalogue of 11 rare variants presenting signs of association with breast cancer were identified. All the affected genes are involved in important cellular mechanisms like DNA repair, cell proliferation and survival or cell cycle regulation. This study highlights the need to investigate the role of rare variants in familial cancer development by means of novel high throughput analysis strategies optimized for genetically heterogeneous scenarios. Even considering the intrinsic limitations of exome resequencing studies, our findings support the hypothesis that the majority of non-BRCA1/BRCA2 breast cancer families might be explained by the action of moderate and/or low penetrance susceptibility alleles.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".