Decreased breast cancer risk in systemic lupus erythematosus: the search for a genetic basis continues
Bibliographic record
Abstract
PURPOSE: Recent work has demonstrated an important decrease in breast cancers for women with systemic lupus erythematosus (SLE). The reason behind this phenomenon is unknown. Our purpose was to explore whether the single nucleotide polymorphisms (SNPs) predisposing to SLE might be protective against breast cancer (in women in the general population). METHODS: We focused on loci relevant to 10 SNPs associated with SLE (with a p value of <10(-9)). We determined whether we could establish a decreased frequency of these SNPs in breast cancer cases versus controls, within the general population. To do this we used a large breast cancer genome-wide association study (GWAS) dataset, involving 3,659 breast cancer cases and 4,897 controls. These subjects were all primarily of European ancestry. RESULTS: The population-based GWAS breast cancer data we examined suggested little evidence for important associations between breast cancer and SLE-related SNPs. Within the general population GWAS data, a cytosine(C) nucleotide substitution at rs9888739 (on chromosome 16p11.2) showed a very weak inverse association with breast cancer. The odds ratio (OR) for the rs9888739-C allele was 0.907551 (p value 0.049899) in the GWAS breast cancer sample, compared to controls. There was a slightly stronger, positive, association with breast cancer for rs6445975-G (Guanine) on chromosome 3p14.3, with a breast cancer OR of 1.0911 (p value 0.0097). CONCLUSIONS: Within this large breast cancer dataset, we did not demonstrate important associations with 10 lupus-associated SNPs. If decreased breast cancer risk in SLE is influenced by genetic profiles, this may be due to complex interactions and/or epigenetic factors.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".