Abstract
8513 Background: Identification of primary localized melanoma patients at high risk for the development of metastatic disease is critical for informed decisions on patients’ management. Given the advantage of non-invasive collection of blood and evidence of microRNA (miRNA) dysregulation in cancer and stability in serum, we hypothesize that the differential expression of miRNA in the serum could be developed as sensitive and specific marker of metastatic potential of primary melanoma. Methods: Patients prospectively enrolled at New York University Medical Center with sera drawn at the time of primary diagnosis and ≥3 years follow-up were identified for training and validation testing. miRNAs were selected based on: 1) microarray tissue-based data, 2) association with melanoma progression in preclinical models, 3) differential expression in melanoma vs. nevi, and 4) evidence of dysregulation in other cancers. Using RT-qPCR analysis, levels of 16 miRNAs were quantified in the sera of primary melanoma patients and 3 control groups (healthy volunteers, non-melanoma cancers, and systemic inflammatory disease). Raw Ct values were normalized to miR-16 as an internal control. Results: Four miRNAs (miR-15b, -182, -221, -324.3p) were found to be differentially expressed in melanoma patients (n=94) compared to the controls (n=55) (p-values <0.001, two sample t- tests). Logistic regression modeling and the associated ROC analyses found 2 miRNAs (miR-182, -221) able to discriminate melanoma from controls with an AUC= 0.86 (95% CI, 0.80-0.92). Among melanoma patients, miR-182 was upregulated in melanoma patients who metastasized vs. those who did not (31 vs. 63 respectively, p-value<0.03) using a two-sided t-test. A multiple logistic regression model with miRNAs as covariates indicated that miR-15b, miR-182, and miR-221 are statistically significant predictors of melanoma metastasis (p-values <0.04). Conclusions: Our data support the potential of serum miRNAs as non-invasive prognostic biomarkers in melanoma. Longitudinal testing with repeated sampling and integration of clinical data into a predictive model will further define its clinical utility.
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