PAPERS OF THE 136TH ASA ANNUAL MEETING

A Highly Predictive Model for Diagnosis of Colorectal Neoplasms Using Plasma MicroRNA

Improving Specificity and Sensitivity

Carter, Jane V. MB, ChB; Roberts, Henry L. BS; Pan, Jianmin PhD; Rice, Jonathan D. MD; Burton, James F. BA; Galbraith, Norman J. MB, ChB; Eichenberger, Maurice R. MS; Jorden, Jeffery MD; Deveaux, Peter MD; Farmer, Russell MD; Williford, Anna BSN; Kanaan, Ziad MD, PhD; Rai, Shesh N. PhD; Galandiuk, Susan MD

Author Information

^*Price Institute of Surgical Research, Section of Colorectal Surgery, Hiram C. Polk Jr MD Department of Surgery, University of Louisville School of Medicine, Louisville, KY

^†Department of Bioinformatics and Biostatistics, University of Louisville School of Medicine, Louisville, KY

^§Biostatistics Shared Facility, James Graham Brown Cancer Center, University of Louisville, KY

^††Detroit Medical Center, Department of Internal Medicine, Department of Gastroenterology, Wayne State University, Detroit, MI.

Reprints: Susan Galandiuk, MD, Department of Surgery, University of Louisville School of Medicine, Louisville, KY 40292. E-mail: [email protected].

Presentation: Original Study: Presented at the American Surgical Association 136th Annual Meeting, April 14–16, 2016, Chicago, IL.

This publication is made possible in part by the John Williamson and Barbara Thruston Atwood Price Trust and the National Cancer Institute grant R25-CA134283. Dr Rai is partly supported by Dr DM Miller and the Wendell Cherry Endowed Chair in Clinical Trial Research.

Author contributions: Study concept and design—HLR, MRE, ZK, and SG; data acquisition—JVC, HLR, JDR, JFB, JJ, PD, RF, AW, and SG; analysis and interpretation of data—JVC, JDR, HLR, MRE, JFB, NJG, JP, SNR, and SG; drafting of the manuscript—JVC, JFB, NJG, and SG; statistical analysis—JP and SNR; critical revision of the manuscript for intellectual content—SNR, ZK, and SG; and study supervision—SNR and SG.

Funding: The funding sources had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; and preparation, review, or approval of the manuscript.

Disclosure: The author reports no conflicts of interest.

Annals of Surgery 264(4):p 575-584, October 2016. | DOI: 10.1097/SLA.0000000000001873

Abstract

Objective:

To develop a plasma-based microRNA (miRNA) diagnostic assay specific for colorectal neoplasms, building upon our prior work.

Background:

Colorectal neoplasms [colorectal cancer (CRC) and colorectal advanced adenoma (CAA)] frequently develop in individuals at ages when other common cancers also occur. Current screening methods lack sensitivity, specificity, and have poor patient compliance.

Methods:

Plasma was screened for 380 miRNAs using microfluidic array technology from a “Training” cohort of 60 patients, (10 each) control, CRC, CAA, breast cancer, pancreatic cancer, and lung cancer. We identified uniquely dysregulated miRNAs specific for colorectal neoplasia (P < 0.05, false discovery rate: 5%, adjusted α = 0.0038). These miRNAs were evaluated using single assays in a “Test” cohort of 120 patients. A mathematical model was developed to predict blinded sample identity in a 150 patient “Validation” cohort using repeat-sub-sampling validation of the testing dataset with 1000 iterations each to assess model detection accuracy.

Results:

Seven miRNAs (miR-21, miR-29c, miR-122, miR-192, miR-346, miR-372, and miR-374a) were selected based upon P value, area under the curve (AUC), fold change, and biological plausibility. Area under the curve (±95% confidence interval) for “Test” cohort comparisons were 0.91 (0.85–0.96) between all neoplasia and controls, 0.79 (0.70–0.88) between colorectal neoplasia and other cancers, and 0.98 (0.96–1.0) between CRC and colorectal adenomas. In our “Validation” cohort, our mathematical model predicted blinded sample identity with 69% to 77% accuracy, 67% to 76% accuracy, and 86% to 90% accuracy for each comparison, respectively.

Conclusions:

Our plasma miRNA assay and prediction model differentiate colorectal neoplasia from patients with other neoplasms and from controls with higher sensitivity and specificity compared with current clinical standards.

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A Highly Predictive Model for Diagnosis of Colorectal Neoplasms Using Plasma MicroRNA

Improving Specificity and Sensitivity