Dataset Information

Serum-based miRNAs in the prediction and detection of recurrence in melanoma patients.

ABSTRACT: BACKGROUNDIdentification of primary melanoma patients at the highest risk of recurrence remains a critical challenge, and monitoring for recurrent disease is limited to costly imaging studies. We recently reported our array-based discovery of prognostic serum miRNAs in melanoma. In the current study, we examined the clinical utility of these serum-based miRNAs for prognosis as well as detection of melanoma recurrence.METHODSSerum levels of 12 miRNAs were tested using qRT-PCR at diagnosis in 283 melanoma patients (training cohort, n?=?201; independent validation, n?=?82; median follow-up, 68.8 months). A refined miRNA signature was chosen and evaluated. We also tested the potential clinical utility of the miRNAs in early detection and monitoring of recurrence using multiple longitudinal samples (pre- and postrecurrence) in a subset of 82 patients (n?=?225). In addition, we integrated our miRNA signature with publicly available Cancer Genome Atlas data to examine the relevance of these miRNAs to melanoma biology.RESULTSFour miRNAs (miR-150, miR-30d, miR-15b, and miR-425) in combination with stage separated patients by recurrence-free survival (RFS) and overall survival (OS) and improved prediction of recurrence over stage alone in both the training and validation cohorts (training RFS and OS, P?CONCLUSIONSData demonstrate that serum miRNAs can improve melanoma patient stratification over stage and support further testing of miR-15b to guide patient surveillance.

SUBMITTER: Fleming NH

PROVIDER: S-EPMC4270907 | biostudies-literature | 2015 Jan

REPOSITORIES: biostudies-literature

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Serum-based miRNAs in the prediction and detection of recurrence in melanoma patients.

Fleming Nathaniel H NH Zhong Judy J da Silva Inês Pires IP Vega-Saenz de Miera Eleazar E Brady Bobbi B Han Sung Won SW Hanniford Doug D Wang Jinhua J Shapiro Richard L RL Hernando Eva E Osman Iman I

Cancer 20140825 1

<h4>Background</h4>Identification of primary melanoma patients at the highest risk of recurrence remains a critical challenge, and monitoring for recurrent disease is limited to costly imaging studies. We recently reported our array-based discovery of prognostic serum miRNAs in melanoma. In the current study, we examined the clinical utility of these serum-based miRNAs for prognosis as well as detection of melanoma recurrence.<h4>Methods</h4>Serum levels of 12 miRNAs were tested using qRT-PCR at ...[more]

PMID: 25155861

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Project description:Advanced ovarian cancer is one of the most lethal gynecological tumor, mainly due to late diagnoses and acquired drug resistance. MicroRNAs (miRNAs) are small-non coding RNA acting as tumor suppressor/oncogenes differentially expressed in normal and epithelial ovarian cancer and has been recognized as a new class of tumor early detection biomarkers as they are released in blood fluids since tumor initiation process. Here, we evaluated by droplet digital PCR (ddPCR) circulating miRNAs in serum samples from healthy (N = 105) and untreated ovarian cancer patients (stages I to IV) (N = 72), grouped into a discovery/training and clinical validation set with the goal to identify the best classifier allowing the discrimination between earlier ovarian tumors from health controls women. The selection of 45 candidate miRNAs to be evaluated in the discovery set was based on miRNAs represented in ovarian cancer explorative commercial panels. We found six miRNAs showing increased levels in the blood of early or late-stage ovarian cancer groups compared to healthy controls. The serum levels of miR-320b and miR-141-3p were considered independent markers of malignancy in a multivariate logistic regression analysis. These markers were used to train diagnostic classifiers comprising miRNAs (miR-320b and miR-141-3p) and miRNAs combined with well-established ovarian cancer protein markers (miR-320b, miR-141-3p, CA-125 and HE4). The miRNA-based classifier was able to accurately discriminate early-stage ovarian cancer patients from health-controls in an independent sample set (Sensitivity = 80.0%, Specificity = 70.3%, AUC = 0.789). In addition, the integration of the serum proteins in the model markedly improved the performance (Sensitivity = 88.9%, Specificity = 100%, AUC = 1.000). A cross-study validation was carried out using four data series obtained from Gene Expression Omnibus (GEO), corroborating the performance of the miRNA-based classifier (AUCs ranging from 0.637 to 0.979). The clinical utility of the miRNA model should be validated in a prospective cohort in order to investigate their feasibility as an ovarian cancer early detection tool.

Dataset Information

Serum-based miRNAs in the prediction and detection of recurrence in melanoma patients.

Publications

Serum-based miRNAs in the prediction and detection of recurrence in melanoma patients.

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