A machine learning assay to predict disease recurrence in hormone receptor-positive breast cancer

Hormone receptor (HR)-positive, human epidermal growth factor 2 (HER2)-negative breast cancer associates with a sustained risk of relapse over time. Current multigene assays offer limited validity to identify clinically low-risk tumors at high risk of recurrence, which is particularly relevant in the context of novel adjuvant therapies. In this study, we developed and validated ER-Predict, a machine learning assay leveraging a 14-gene expression signature to classify early stage HR-positive/HER2-negative breast cancer according to the risk of relapse.

ER-Predict was developed on a cohort of 1413 HR-positive/HER2-negative early breast cancer cases. External validation was carried out across eight publicly available cohorts (n = 1118). Comparative benchmarking was conducted against reproduced prognostic signatures, and particularly, EndoPredict and Oncotype DX. Functional annotation and drug response analysis were carried out using gene expression and pharmacogenomic data from publicly available breast cancer cell lines.

ER-Predict identified high-risk patients with significantly reduced distant metastasis-free survival in the external validation cohort (hazard ratio 2.03, 95% confidence interval 1.57-2.63, P < 0.0001). The assay demonstrated independent prognostic value beyond traditional clinicopathological factors, including tumor grade, tumor size, and nodal status, and consistently outperformed recomputed multigene panels. ER-Predict showed specificity toward luminal-like transcriptomic programs, revealing activation of key cell-cycle regulators governing endocrine resistance among high-risk tumors that had retained expression of retinoblastoma-1, suggesting potential actionability by means of cell-cycle inhibitors.

ER-Predict represents a robust assay with potential utility in early stage HR-positive/HER2-negative breast cancer. Its consistent ability to identify high-risk patients supports further investigation as a decision-support tool to guide treatment intensification in clinically low-risk HR-positive/HER2-negative disease.