A Nomogram for Predicting Mammaprint Results in Women with T1-3N0-1M0 Hormone Receptor-Positive and Human Epidermal Growth Factor Receptor-2-Negative Breast Cancer
Abstract PurposeWe aimed to develop a prediction MammaPrint (MMP) genomic risk assessment nomogram model for hormone-receptor-positive and human epidermal growth factor receptor-2 (HER2)-negative breast cancer and minimal axillary burden (N0-1) tumors using clinicopathological factors of patients who underwent an MMP test for decision making regarding adjuvant chemotherapy.MethodsA total of 409 T1-3 N0-1 M0 hormone receptor-positive and HER2-negative breast cancer patients whose MMP genomic risk results were available at Asan Medical Center from 2017 to 2020 were enrolled. Patients were randomly assigned to training and validation subsets and logistic regression was performed. ResultsThe primary cohort (n = 409) included 216 (53.1%) T2-3 and 388 (94.8%) N1 patients. No patients were estrogen-receptor-negative or -weak, 175 (42.7%) had a high proliferation index (Ki-67 ≥ 20%), and 225 (55.0%) were premenopausal. Multivariate analysis revealed that the age at diagnosis, progesterone receptor (PR) score, nuclear grade, and Ki-67 were significantly associated with MMP risk results. We developed an MMP low-risk predictive nomogram. The area under the receiver operating characteristic curve was 0.82 (95% confidence interval [CI], 0.77 to 0.87). When applied to the validation group, the nomogram was accurate with an area under the curve of 0.77 (95% CI, 0.68 to 0.86).Conclusion Our nomogram, which incorporates four traditional prognostic factors, i.e., age, PR, nuclear grade, and Ki-67, could predict the probability of obtaining a low MMP risk in a cohort of intermediate clinical risk patients. This nomogram can aid the selection of patients who need additional MMP testing.