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JOURNAL ARTICLE
RESEARCH SUPPORT, N.I.H., EXTRAMURAL
RESEARCH SUPPORT, NON-U.S. GOV'T
A Highly Predictive Model for Diagnosis of Colorectal Neoplasms Using Plasma MicroRNA: Improving Specificity and Sensitivity.
Annals of Surgery 2016 October
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.
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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