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Record W4405227354 · doi:10.1093/jnci/djae314

Race and clinical outcomes in hormone receptor-positive, HER2-negative, node-positive breast cancer in the randomized RxPONDER trial

2024· article· en· W4405227354 on OpenAlexaff
Yara Abdou, William E. Barlow, Julie R. Gralow, Funda Meric‐Bernstam, Kathy S. Albain, Daniel F. Hayes, Nancy U. Lin, Edith Perez, Lori J. Goldstein, Stephen Chia, Sukhbinder Dhesy‐Thind, Priya Rastogi, Emilio Alba, Suzette Delaloge, Anne F. Schott, Steven Shak, Priyanka Sharma, Danika L. Lew, Jieling Miao, Joseph M. Unger, Debasish Tripathy, Gabriel N. Hortobágyi, Lajos Pusztai, Kevin Kalinsky

Bibliographic record

VenueJNCI Journal of the National Cancer Institute · 2024
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicBreast Cancer Treatment Studies
Canadian institutionsMcMaster UniversityBC Cancer Agency
FundersNational Cancer InstituteNational Institutes of HealthAstraZenecaHope FoundationAbbVieGenomic HealthBreast Cancer Research Foundation
KeywordsBreast cancerHER2 negativeOncologyHormone receptorInternal medicineMedicineRace (biology)Randomized controlled trialCancerBiologyMetastatic breast cancer

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.005
metaresearch head score (Gemma)0.006
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.005
Threshold uncertainty score0.025

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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.

Opus teacher head0.025
GPT teacher head0.361
Teacher spread0.336 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations10
Published2024
Admission routes1
Has abstractyes

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