Race and clinical outcomes in hormone receptor-positive, HER2-negative, node-positive breast cancer in the randomized RxPONDER trial
Bibliographic record
Abstract
BACKGROUND: The phase III RxPONDER trial has affected treatment for node-positive (1-3), hormone receptor-positive, HER2-negative breast cancer with a 21-gene recurrence score (RS) less than 26. We investigated how these findings apply to different racial and ethnic groups within the trial. METHODS: The trial randomly assigned women to endocrine therapy (ET) or to chemotherapy plus ET. The primary clinical outcome was invasive disease-free survival (IDFS), with distant relapse-free survival (DRFS) as a secondary outcome. Multivariable Cox models were used to evaluate the association between race/ethnicity and survival outcomes, adjusting for clinicopathological characteristics, RS, and treatment. RESULTS: A total of 4048 women with self-reported race/ethnicity were included: Hispanic (15.1%), non-Hispanic Black (NHB) (6.1%), Native American/Pacific Islander (0.8%), Asian (8.0%), and non-Hispanic White (NHW) (70%). No differences in RS distribution, tumor size, or number of positive nodes were observed by race/ethnicity. Relative to NHWs, IDFS was worse for NHB participants (5-year IDFS 91.6% vs 87.1%, HR = 1.37; 95% CI = 1.03 to 1.81) and better for Asians (91.6% vs 93.9%, HR = 0.64; 95% CI = 0.46 to 0.91). Relative to NHW, DRFS was worse for NHB participants (5-year DRFS 95.8% vs 91.0%, HR = 1.65; 95% CI = 1.17 to 2.32) and better for Asians (95.8% vs 96.7%, HR = 0.59; 95% CI = 0.37 to 0.95). Adjusting for clinical characteristics, particularly body mass index, diminished the effect of race on outcomes. Chemotherapy treatment efficacy did not differ by race/ethnicity. CONCLUSIONS: NHB women had worse clinical outcomes compared with NHWs in the RxPONDER trial despite similar RS and comparable treatment. Our study emphasizes the persistent racial disparities in breast cancer outcomes while highlighting complex interactions among contributing factors. TRIAL REGISTRATION: ClinicalTrials.gov: NCT01272037.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".