Intrinsic Breast Tumor Subtypes, Race, and Long-Term Survival in the Carolina Breast Cancer Study
Bibliographic record
Abstract
PURPOSE: Previous research identified differences in breast cancer-specific mortality across 4 intrinsic tumor subtypes: luminal A, luminal B, basal-like, and human epidermal growth factor receptor 2 positive/estrogen receptor negative (HER2(+)/ER(-)). EXPERIMENTAL DESIGN: We used immunohistochemical markers to subtype 1,149 invasive breast cancer patients (518 African American, 631 white) in the Carolina Breast Cancer Study, a population-based study of women diagnosed with breast cancer. Vital status was determined through 2006 using the National Death Index, with median follow-up of 9 years. RESULTS: Cancer subtypes luminal A, luminal B, basal-like, and HER2(+)/ER(-) were distributed as 64%, 11%, 11%, and 5% for whites, and 48%, 8%, 22%, and 7% for African Americans, respectively. Breast cancer mortality was higher for participants with HER2(+)/ER(-) and basal-like breast cancer compared with luminal A and B. African Americans had higher breast cancer-specific mortality than whites, but the effect of race was statistically significant only among women with luminal A breast cancer. However, when compared with the luminal A subtype within racial categories, mortality for participants with basal-like breast cancer was higher among whites (HR = 2.0, 95% CI: 1.2-3.4) than African Americans (HR = 1.5, 95% CI: 1.0-2.4), with the strongest effect seen in postmenopausal white women (HR = 3.9, 95% CI: 1.5-10.0). CONCLUSIONS: Our results confirm the association of basal-like breast cancer with poor prognosis and suggest that basal-like breast cancer is not an inherently more aggressive disease in African American women compared with whites. Additional analyses are needed in populations with known treatment profiles to understand the role of tumor subtypes and race in breast cancer mortality, and in particular our finding that among women with luminal A breast cancer, African Americans have higher mortality than whites.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".