Low penetrance breast cancer susceptibility loci are associated with specific breast tumor subtypes: findings from the Breast Cancer Association Consortium
Bibliographic record
Abstract
Breast cancers demonstrate substantial biological, clinical and etiological heterogeneity. We investigated breast cancer risk associations of eight susceptibility loci identified in GWAS and two putative susceptibility loci in candidate genes in relation to specific breast tumor subtypes. Subtypes were defined by five markers (ER, PR, HER2, CK5/6, EGFR) and other pathological and clinical features. Analyses included up to 30 040 invasive breast cancer cases and 53 692 controls from 31 studies within the Breast Cancer Association Consortium. We confirmed previous reports of stronger associations with ER+ than ER- tumors for six of the eight loci identified in GWAS: rs2981582 (10q26) (P-heterogeneity = 6.1 × 10(-18)), rs3803662 (16q12) (P = 3.7 × 10(-5)), rs13281615 (8q24) (P = 0.002), rs13387042 (2q35) (P = 0.006), rs4973768 (3p24) (P = 0.003) and rs6504950 (17q23) (P = 0.002). The two candidate loci, CASP8 (rs1045485, rs17468277) and TGFB1 (rs1982073), were most strongly related with the risk of PR negative tumors (P = 5.1 × 10(-6) and P = 4.1 × 10(-4), respectively), as previously suggested. Four of the eight loci identified in GWAS were associated with triple negative tumors (P ≤ 0.016): rs3803662 (16q12), rs889312 (5q11), rs3817198 (11p15) and rs13387042 (2q35); however, only two of them (16q12 and 2q35) were associated with tumors with the core basal phenotype (P ≤ 0.002). These analyses are consistent with different biological origins of breast cancers, and indicate that tumor stratification might help in the identification and characterization of novel risk factors for breast cancer subtypes. This may eventually result in further improvements in prevention, early detection and treatment.
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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 | 0.005 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".