Dataset Information

Race, Ethnicity, and Clinical Outcomes in Hormone Receptor-Positive, HER2-Negative, Node-Negative Breast Cancer in the Randomized TAILORx Trial.

ABSTRACT:

Background

Black race is associated with worse outcomes in early breast cancer. We evaluated clinicopathologic characteristics, the 21-gene recurrence score (RS), treatment delivered, and clinical outcomes by race and ethnicity among women who participated in the Trial Assigning Individualized Options for Treatment.

Methods

The association between clinical outcomes and race (White, Black, Asian, other or unknown) and ethnicity (Hispanic vs non-Hispanic) was examined using proportional hazards models. All P values are 2-sided.

Results

Of 9719 eligible women with hormone receptor-positive, HER2-negative, node-negative breast cancer, there were 8189 (84.3%) Whites, 693 (7.1%) Blacks, 405 (4.2%) Asians, and 432 (4.4%) with other or unknown race. Regarding ethnicity, 889 (9.1%) were Hispanic. There were no substantial differences in RS or ESR1, PGR, or HER2 RNA expression by race or ethnicity. After adjustment for other covariates, compared with White race, Black race was associated with higher distant recurrence rates (hazard ratio [HR] = 1.60, 95% confidence intervals [CI] = 1.07 to 2.41) and worse overall survival in the RS 11-25 cohort (HR = 1.51, 95% CI = 1.06 to 2.15) and entire population (HR = 1.41, 95% CI = 1.05 to 1.90). Hispanic ethnicity and Asian race were associated with better outcomes. There was no evidence of chemotherapy benefit for any racial or ethnic group in those with a RS of 11-25.

Conclusions

Black women had worse clinical outcomes despite similar 21-gene assay RS results and comparable systemic therapy in the Trial Assigning Individualized Options for Treatment. Similar to Whites, Black women did not benefit from adjuvant chemotherapy if the 21-gene RS was 11-25. Further research is required to elucidate the basis for this racial disparity in prognosis.

SUBMITTER: Albain KS

PROVIDER: S-EPMC8599918 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:Systemic chemotherapy in the adjuvant setting can cure breast cancer in some patients that would otherwise recur with incurable, metastatic disease. However, since only a fraction of patients would have recurrence after surgery alone, the challenge is to stratify high-risk patients (who stand to benefit from systemic chemotherapy) from low-risk patients (who can safely be spared treatment related toxicities and costs).We focus here on risk stratification in node-negative, ER-positive, HER2-negative breast cancer. We use a large database of publicly available microarray datasets to build a random forests classifier and develop a robust multi-gene mRNA transcription-based predictor of relapse free survival at 10 years, which we call the Random Forests Relapse Score (RFRS). Performance was assessed by internal cross-validation, multiple independent data sets, and comparison to existing algorithms using receiver-operating characteristic and Kaplan-Meier survival analysis. Internal redundancy of features was determined using k-means clustering to define optimal signatures with smaller numbers of primary genes, each with multiple alternates.Internal OOB cross-validation for the initial (full-gene-set) model on training data reported an ROC AUC of 0.704, which was comparable to or better than those reported previously or obtained by applying existing methods to our dataset. Three risk groups with probability cutoffs for low, intermediate, and high-risk were defined. Survival analysis determined a highly significant difference in relapse rate between these risk groups. Validation of the models against independent test datasets showed highly similar results. Smaller 17-gene and 8-gene optimized models were also developed with minimal reduction in performance. Furthermore, the signature was shown to be almost equally effective on both hormone-treated and untreated patients.RFRS allows flexibility in both the number and identity of genes utilized from thousands to as few as 17 or eight genes, each with multiple alternatives. The RFRS reports a probability score strongly correlated with risk of relapse. This score could therefore be used to assign systemic chemotherapy specifically to those high-risk patients most likely to benefit from further treatment.