UK Breast Cancer Research Symposium 2016: Submitted Abstracts
Bibliographic record
Abstract
Genetic variants at 6q25 are associated with breast cancer in the general population and in BRCA1 mutation carriers.To identify the causal variants underlying these associations, we analysed 3872 SNPs across 6q25 in 118,816 subjects from three international consortia.We observed five separate regions of association-surrounding ESR1-the most obvious target gene in the region.We used epidemiological methods to define the best causal candidate variants and examined their phenotypic associations:At four of the five regions, the causal candidate risk alleles display a stronger association with oestrogen receptor-negative (ER-) than oestrogen receptor-positive (ER+) tumours.Three of these four are associated with ''triple-negative'' and the fourth with HER2+ tumour subtypes.Two are additionally associated with mammographic density.The candidate causal variants in the fifth region are more strongly associated with high-grade ER+ breast tumours.These causal candidates lie in five cis-regulatory elements.Chromosome conformation capture confirmed that these elements directly contact the promoters of the ESR1, RMND1, ARMT1 and CCDC170 genes.In the ER-associated regions, the best causal candidates overlap four separate enhancers and reporter assays indicated their risk alleles putatively reduced expression of ESR1, RMND1 and CCDC170.These findings were validated using immunohistochemical and other studies.Luciferase assay constructs carrying the causal candidate risk alleles decreased ESR1, RMND1 and CCDC170 promoter activity.By contrast, the risk alleles most strongly associated with ER+ tumour risk disrupt a silencer element and increase ESR1 and RMND1 expression.Risk alleles disrupting the enhancer elements reduce oestrogen receptor expression and increase the risk of ER-tumour subtypes, while disrupting the silencer element increases oestrogen receptor expression and risk of high-grade ER+ tumours, suggesting there may be a ''Goldilocks level'' of ESR1 expression for breast cancer protection.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.281 | 0.137 |
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".