Three ct-miRNA signature predicts disease outcome in early breast cancer women.
TL;DR
Considering the accessibility and stability of ct-miRNAs, this signature might improve the recurrence risk prediction identifying who, despite the early diagnosis, might need a more intensive screening or secondary prevention strategies.
Abstract
Background
Breast cancer is still a leading cause of tumor mortality in women. Indeed, despite advancements in early diagnosis, molecular profiling and novel therapeutic approaches, the disease outcome is still not always predictable. This evidence underlines the need for validated biomarkers to predict recurrences, to personalize both disease monitoring and tailored therapies. And to this aim, miRNAs have shown promising applications as circulating biomarkers. Starting from plasma samples collected from women with early-stage breast cancer at the time of diagnosis, we explored the expression of ct-miRNAs to define a molecular signature predictive of recurrence.
Methods
Two independent cohorts of plasma samples were retrospectively and prospectively collected at Fondazione IRCCS Istituto Nazionale dei Tumori di Milano (INT) for a total of 203 patients. Ct-miRNAs were previously profiled by using the OpenArray Human microRNA panel (OA) (Thermo Fisher Scientific). Relapse-free survival (RFS) was analyzed using Cox regression models adjusted for cohort to assess associations with clinicopathological variables and circulating miRNA levels. Models performance was evaluated using c-statistics (95% Confidence interval) and an internal validation was performed with bootstrap resamples. Clinicopathological variables were added to the signature in multivariate models to evaluate their independent prognostic value.
Results
We identified a three ct-miRNA (miR-125b, miR-26b-3p and miR-532-5p) signature associated with disease outcome, with a hazard ratio of 2.803 (95% CI, 1.721-4.565). The c-statistic was 0.72 (95%, CI 0.62-0.83) and was confirmed by the resampling procedure, with a c-median bootstrap statistic of 0.73 (IQR, 0.65-0.81). The 3-circulating miRNA signature retained its significant prognostic performance with respect to RFS even after the inclusion of clinicopathological variables in multivariate models. Considering both ct-miRNA signature and Nottingham Prognostic Index (NPI), the c-statistic of the bivariate model was equal to 0.80 (95% CI, 0.70; 0.90).
Conclusion
We identified a three ct-miRNA prognostic signature in early breast cancer women. Considering the accessibility and stability of ct-miRNAs, this signature might improve the recurrence risk prediction identifying who, despite the early diagnosis, might need a more intensive screening or secondary prevention strategies.