Nomogram Update to Predict the High Genomic Risk Breast Cancer by Different Races

To develop a nomogram to predict the high-risk recurrence score (RS) and to customize the nomogram for different races in early-stage hormone receptor (HoR)-positive, human epidermal growth factor receptor-2 (HER2)-negative breast cancer.

Patients diagnosed between 2010 and 2015 were included from the surveillance, epidemiology, and end results oncotype DX database. The nomogram was assessed with a receiver operating characteristic curve to measure the area under the curve (AUC) with a 95% confidence interval (95% CI). The nomogram was developed and internally validated for discrimination and calibration, and then validated in different races.

A total of 48,464 patients were included and randomly assigned to the training cohort (n = 36370, 75.0%) and validation cohort (n = 12,094, 25.0%). Patients in the training cohort were identified to develop the nomogram, including 32,683 (89.9%) White women, 3135 (8.6%) Black women, and 552 (1.5%) Chinese women. Five independent predictive factors for high-risk RS were included to develop the nomogram, including tumor grade, progesterone receptor status, histological subtype, race, and tumor stage. The AUC was 0.696 (95% CI, 0.682-0.710) in the training cohort and 0.700 (95% CI, 0.676-0.724) in the validation cohort. There was no significant difference between the training cohort and the validation cohort. When validating the nomogram classified by race, the AUC was 0.694 (95% CI, 0.682-0.706) for the White cohort, 0.708 (95% CI, 0.673-0.743) for the Black cohort, and 0.653 (95% CI, 0.565-0.741) for the Chinese cohort.

The developed nomogram for predicting high-risk RS is available for different races in patients with HoR+/HER2- breast cancer, which could be used as qualified surrogates before ordering the 21-gene RS testing.