Prognostic Value of a BCSC-associated MicroRNA Signature in Hormone Receptor-Positive HER2-Negative Breast Cancer

Abstract

Purpose: Breast cancer patients with high proportion of cancer stem cells (BCSCs) have unfavorable clinical outcomes. MicroRNAs (miRNAs) regulate key features of BCSCs. We hypothesized that a biology-driven model based on BCSC-associated miRNAs could predict prognosis for the most common subtype, hormone receptor (HR)-positive, HER2-negative breast cancer patients.

Patients and methods: After screening candidate miRNAs based on literature review and a pilot study, we built a miRNA-based classifier using LASSO Cox regression method in the training group (n=202) and validated its prognostic accuracy in an internal (n=101) and two external validation groups (n=308).

Results: In this multicenter study, a 10-miRNA classifier incorporating miR-21, miR-30c, miR-181a, miR-181c, miR-125b, miR-7, miR-200a, miR-135b, miR-22 and miR-200c was developed to predict distant relapse free survival (DRFS). With this classifier, HR+HER2- patients were scored and classified into high-risk and low-risk disease recurrence, which was significantly associated with 5-year DRFS of the patients. Moreover, this classifier outperformed traditional clinicopathological risk factors, IHC4 scoring and 21-gene Recurrence Score (RS). The patients with high-risk recurrence determined by this classifier benefit more from chemotherapy.

Conclusions: Our 10-miRNA-based classifier provides a reliable prognostic model for disease recurrence in HR+HER2- breast cancer patients. This model may facilitate personalized therapy-decision making for HR+HER2- individuals.

Keywords: Biology-driven approach; Breast cancer stem cell; Classifier; Prognosis; miRNA.