Abstract P1-08-30: Triple negative breast cancer: Does ethnicity impact survival?
Bibliographic record
Abstract
Abstract Background: Triple negative breast cancer (TNBC) is a heterogeneous disease with poor outcomes relative to hormone positive breast cancer. Observations in the clinical setting lead to the hypothesis that there may be phenotypic differences among ethnic groups. Previous studies have conflicting results; some suggesting no differences, others showing inferior outcomes for black patients (pts). The purpose of this study was to use a population-based approach to examine survival amongst different ethnicities in triple negative breast cancer (TNBC). Methods Retrospective population-based study using data from the surveillance epidemiology and end results (SEER) database to identify TNBC cases diagnosed 2010 - 2014. We divided pts into 6 ethnic groups: White, Black, American Indian/Alaskan Native (AIAN), Pacific Islander (PI), Asian and Asian Indian (AI). Primary outcome: overall survival (OS); secondary outcome: breast-cancer specific survival (BCSS). Survival analysis was performed using Cox proportional hazards and Kaplan-Meier models. Results 31482 pts were included; 22752 (72.3%) were white, 6319 (20.4%) black, 1720 (5.5%) Asian, 262 (0.8%) AI, 179 (0.6%) AIAN, and 150 (0.5%) PI. Asian pts had the best OS (HR 0.82, 95% CI 0.72-0.94), while Black pts had the worst (HR 1.21 95% CI 1.13-1.29) when compared with White pts. Differences between other ethnicities were not statistically significant for OS. Black pts had significantly worse BCSS (HR 1.20, 95% CI 1.12 -1.29); no other ethnicity had statistically significant differences compared to Whites. Comparing Asian with Black pts in multivariate analysis the HR was 0.68 (95% CI 0.59 – 0.79) for OS and 0.71 (95% 0.61 – 0.84) for BCSS. Older age, advanced stage, male sex, and lack of chemotherapy, radiation or surgery were also found to be statistically significant variables in multivariate analysis. Conclusions In this large population study, we found Asian pts had significantly better OS and Black pts had significantly worse OS and BCSS. These findings confirm the clinical observation and warrant closer molecular analysis of TNBC phenotypes based on patient's ethnicity. Findings may offer insight into the biology of TNBC and lead to development of innovative treatment options. Citation Format: Rushton-Marovac M, Zhang T, Song X. Triple negative breast cancer: Does ethnicity impact survival? [abstract]. In: Proceedings of the 2018 San Antonio Breast Cancer Symposium; 2018 Dec 4-8; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2019;79(4 Suppl):Abstract nr P1-08-30.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".