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

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.

Figure 1

Study outline. See Text for details. Colorectal cancer (CRC), colorectal advanced adenoma (CAA), breast cancer (BC), lung cancer (LC) and pancreatic cancer (PC).

Figure 2

ROC curves and AUC for the panel of miRNAs for “All neoplasia” vs. “control” [Fig 2A], “CR neoplasia” vs. “Other cancers” [Fig 2B], and “CRC” vs. “CAA” [Fig 2C] in the “Test” cohort. A) ROC curve for the panel of miR-21, miR-29c, miR-346 and miR-374a for “All neoplasia” vs. “Control”. B) ROC curve for the panel of miR-21, miR-29c, miR-372 and miR-374a for “CR neoplasia” vs. “Other cancers”. C) ROC curve for the panel of miR-29c, miR-122, miR-192 and miR-374a for “CRC” vs. “CAA”.

Figure 2

ROC curves and AUC for the panel of miRNAs for “All neoplasia” vs. “control” [Fig 2A], “CR neoplasia” vs. “Other cancers” [Fig 2B], and “CRC” vs. “CAA” [Fig 2C] in the “Test” cohort. A) ROC curve for the panel of miR-21, miR-29c, miR-346 and miR-374a for “All neoplasia” vs. “Control”. B) ROC curve for the panel of miR-21, miR-29c, miR-372 and miR-374a for “CR neoplasia” vs. “Other cancers”. C) ROC curve for the panel of miR-29c, miR-122, miR-192 and miR-374a for “CRC” vs. “CAA”.