ER-PREDICT: A novel assay predicting disease recurrence in HR+/HER2- breast cancer

Hormone receptor-positive, human epidermal growth factor 2-negative (HR+/HER2−) breast cancer represents the most common breast cancer subtype (around 70% of all breast cancer diagnoses).Despite showing better cancer-related outcomes compared to other subtypes, around 15% of patients wit HR+/HER2− breast cancer suffer from disease recurrence.

The identification of patients with HR+/HER2− breast cancer at low clinical risk/high genomic risk of recurrence has represented the cornerstone for treatment modulation in the adjuvant therapeutic space. Novel compounds, such as cyclin-dependent kinase 4 and 6 inhibitors (CDK4/6i), have recently expanded the therapeutic options for patients considered at high-clinical risk of relapse. Still, no tool has shown clinical utility to detect patients at low-medium clinical risk which might benefit from escalated adjuvant regimens.

ER-Predict is a novel multigene signature designed to identify HR+/HER- breast cancer patients at high genomic risk of recurrence, providing a powerful tool for precise clinical decision-making. ER-Predict effectively calculates a risk score by combining the determination of: i) the expression level of a gene signature, ii)the tumor size of the primary tumor, iii) the nodal status, iv) the tumor grade.

Advantages:

• Prognostic tool allowing better management of HR+/HER2− breast cancer patients thanks torecurrence risk stratification

• Outperformed multigene panels currently approved in early-stage HR+/HER2- breast cancer

• First tool specifically designed for therapeutic escalation purposes

• Developed on a large cohort of 1413 HR+/HER2- early breast cancer with extended follow up; external validation was performed across eight publicly available cohorts (n=1118)

IP asset: patent application EP26161281.6; co-owned by IEO and University of Milan

Relevant publications: A machine learning assay to predict disease recurrence in hormone receptor-positive breast cancer; ESMO Open, Volume 0, Issue 0, 106064