A Highly Predictive Model for Diagnosis of Colorectal Neoplasms Using Plasma MicroRNA: Improving Specificity and Sensitivity

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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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