Exosomal miRNA profiles of triple-negative breast cancer in neoadjuvant treatment

Triple-negative breast cancer (TNBC) is characterized by aggressive clinicopathological features and is associated with a poor prognosis. Identifying patients that are non-responsive to chemotherapy remains a critical goal for effective personalized therapies. In the present study, the predictive value of exosomal microRNAs (miRNAs) was investigated in patients with TNBC. Exosomes were isolated from patients with TNBC undergoing neoadjuvant chemotherapy. Microarray-based miRNA profiles were compared between patients with pathological complete response (pCR; n=12) and non-pCR (n=12). Furthermore, the miRNA profiles of non-pCR patients with breast cancer recurrence were compared with those with no recurrence. A total of 16 differentially expressed exosomal miRNAs were identified between the patients with pCR and non-pCR by microarray analysis. Of these, a combined signature of four miRNAs (miR-4448, miR-2392, miR-2467-3p and miR-4800-3p) could be used to discriminate between pCR and non-pCR patients with TNBC with an area under the curve value of 0.7652. Furthermore, this study found 43 differentially expressed miRNAs between the patients with non-pCR and recurrence and non-pCR patients without recurrence. In network analysis, 'pathway in cancer', 'focal adhesion' and 'cell cycle' were identified as the crucial pathways in patients with non-pCR who also developed recurrence. Several exosomal miRNAs may be useful biomarkers to predict treatment efficacy for TNBC. The present study identified patients who were resistant to standard chemotherapy and therefore more likely to develop breast cancer recurrence.

Automated summary

This study investigated the predictive value of exosomal microRNAs in patients with triple-negative breast cancer (TNBC), identifying specific miRNAs that could distinguish patients with pathological complete response from those without. The study also found that some exosomal miRNAs may serve as useful biomarkers for predicting treatment efficacy in TNBC.