Epidemiology of Basal-like and Luminal Breast Cancers among Black Women in the AMBER Consortium
Bibliographic record
Abstract
Abstract Background: Evidence suggests etiologic heterogeneity among breast cancer subtypes. Previous studies with six-marker IHC classification of intrinsic subtypes included small numbers of black women. Methods: Using centralized laboratory results for estrogen receptor (ER), progesterone receptor, HER2, proliferation marker, Ki-67, EGFR, and cytokeratin (CK)5/6, we estimated case-only and case–control ORs for established breast cancer risk factors among cases (n = 2,354) and controls (n = 2,932) in the African American Breast Cancer Epidemiology and Risk (AMBER) consortium. ORs were estimated by ER status and intrinsic subtype using adjusted logistic regression. Results: Case-only analyses by ER status showed etiologic heterogeneity by age at menarche, parity (vs. nulliparity), and age at first birth. In case–control analyses for intrinsic subtype, increased body mass index and waist-to-hip ratio (WHR) were associated with increased risk of luminal A subtype, whereas older age at menarche and parity, regardless of breastfeeding, were associated with reduced risk. For basal-like cancers, parity without breastfeeding and increasing WHR were associated with increased risk, whereas breastfeeding and age ≥25 years at first birth were associated with reduced risk among parous women. Basal-like and ER−/HER2+ subtypes had earlier age-at-incidence distribution relative to luminal subtypes. Conclusions: Breast cancer subtypes showed distinct etiologic profiles in the AMBER consortium, a study of more than 5,000 black women with centrally assessed tumor biospecimens. Impact: Among black women, high WHR and parity without breastfeeding are emerging as important intervention points to reduce the incidence of basal-like breast cancer.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".