Serum microRNAs as biomarkers for recurrence in melanoma
1 Interdisciplinary Melanoma Cooperative Group, New York University School of Medicine, New York, NY, USA
2 Department of Surgery, New York University School of Medicine, New York, NY, USA
3 Division of Biostatistics, New York University School of Medicine, New York, NY, USA
4 The Ronald O. Perelman Department of Dermatology, New York University School of Medicine, New York, NY, USA
5 Exiqon A/S, 2950, Vedbaek, Denmark
6 Department of Medicine, New York University School of Medicine, New York, NY, USA
7 Department of Pathology, New York University School of Medicine, New York, NY, USA
Journal of Translational Medicine 2012, 10:155 doi:10.1186/1479-5876-10-155Published: 2 August 2012
Identification of melanoma patients at high risk for recurrence and monitoring for recurrence are critical for informed management decisions. We hypothesized that serum microRNAs (miRNAs) could provide prognostic information at the time of diagnosis unaccounted for by the current staging system and could be useful in detecting recurrence after resection.
We screened 355 miRNAs in sera from 80 melanoma patients at primary diagnosis (discovery cohort) using a unique quantitative reverse transcription-PCR (qRT-PCR) panel. Cox proportional hazard models and Kaplan-Meier recurrence-free survival (RFS) curves were used to identify a miRNA signature with prognostic potential adjusting for stage. We then tested the miRNA signature in an independent cohort of 50 primary melanoma patients (validation cohort). Logistic regression analysis was performed to determine if the miRNA signature can determine risk of recurrence in both cohorts. Selected miRNAs were measured longitudinally in subsets of patients pre-/post-operatively and pre-/post-recurrence.
A signature of 5 miRNAs successfully classified melanoma patients into high and low recurrence risk groups with significant separation of RFS in both discovery and validation cohorts (p = 0.0036, p = 0.0093, respectively). Significant separation of RFS was maintained when a logistic model containing the same signature set was used to predict recurrence risk in both discovery and validation cohorts (p < 0.0001, p = 0.033, respectively). Longitudinal expression of 4 miRNAs in a subset of patients was dynamic, suggesting miRNAs can be associated with tumor burden.
Our data demonstrate that serum miRNAs can improve accuracy in identifying primary melanoma patients with high recurrence risk and in monitoring melanoma tumor burden over time.