Oncotarget

Priority Research Papers:

Development of a predictive miRNA signature for breast cancer risk among high-risk women

Nicholas H. Farina _, Jon E. Ramsey, Melissa E. Cuke, Thomas P. Ahern, David J. Shirley, Janet L. Stein, Gary S. Stein, Jane B. Lian and Marie E. Wood

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Oncotarget. 2017; 8:112170-112183. https://doi.org/10.18632/oncotarget.22750

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Abstract

Nicholas H. Farina1,2, Jon E. Ramsey1,2, Melissa E. Cuke1,3, Thomas P. Ahern1,2,4, David J. Shirley1,5, Janet L. Stein1,2, Gary S. Stein1,2,3, Jane B. Lian1,2 and Marie E. Wood1,3

1 University of Vermont Cancer Center, The Robert Larner MD College of Medicine, University of Vermont, Burlington, VT, USA

2 Department of Biochemistry, The Robert Larner MD College of Medicine, University of Vermont, Burlington, VT, USA

3 Division of Hematology and Oncology, The Robert Larner MD College of Medicine, University of Vermont, Burlington, VT, USA

4 Department of Surgery, The Robert Larner MD College of Medicine, University of Vermont, Burlington, VT, USA

5 Department of Microbiology and Molecular Genetics, The Robert Larner MD College of Medicine, University of Vermont, Burlington, VT, USA

Correspondence to:

Nicholas H. Farina, email:

Marie E. Wood, email:

Keywords: microRNA; high risk breast cancer; liquid biopsy; risk signature; benign breast disease

Received: September 05, 2017 Accepted: October 13, 2017 Published: November 28, 2017

Abstract

Significant limitations exist in our ability to predict breast cancer risk at the individual level. Circulating microRNAs (C-miRNAs) have emerged as measurable biomarkers (liquid biopsies) for cancer detection. We evaluated the ability of C-miRNAs to identify women most likely to develop breast cancer by profiling miRNA from serum obtained long before diagnosis. 24 breast cancer cases and controls (matched for risk and age) were identified from women enrolled in the High-Risk Breast Program at the UVM Cancer Center. Isolated RNA from serum was profiled for over 2500 human miRNAs. The miRNA expression data were input into a stepwise linear regression model to discover a multivariable miRNA signature that predicts long-term risk of breast cancer. 25 candidate miRNAs were identified that individually classified cases and controls based on statistical methodologies. A refined 6-miRNA risk-signature was discovered following regression modeling that distinguishes cases and controls (AUC0.896, CI 0.804-0.988) in this cohort. A functional relationship between miRNAs that cluster together when cases are contrasted against controls was suggested and confirmed by pathway analyses. The discovered 6 miRNA risk-signature can discriminate high-risk women who ultimately develop breast cancer from those who remain cancer-free, improving current risk assessment models. Future studies will focus on functional analysis of the miRNAs in this signature and testing in larger cohorts. We propose that the combined signature is highly significant for predicting cancer risk, and worthy of further screening in larger, independent clinical cohorts.


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