A Highly Predictive Model for Diagnosis of Colorectal Neoplasms Using Plasma MicroRNA: Improving Specificity and Sensitivity.
A Highly Predictive Model for Diagnosis of Colorectal Neoplasms Using Plasma MicroRNA: Improving Specificity and Sensitivity.
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DOI：
10.1097/sla.0000000000001873
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发表时间：
2016-10
影响因子：
9
通讯作者：
Galandiuk S
中科院分区：
文献类型：
--
作者：
Carter JV;Roberts HL;Pan J;Rice JD;Burton JF;Galbraith NJ;Eichenberger MR;Jorden J;Deveaux P;Farmer R;Williford A;Kanaan Z;Rai SN;Galandiuk S
在我们之前工作的基础上，开发一种针对结直肠肿瘤的基于血浆的microRNA （miRNA）诊断方法。结直肠肿瘤（结直肠癌[CRC]和结直肠晚期腺瘤[CAA]）经常发生在其他常见癌症发生的年龄段。目前的筛查方法缺乏敏感性、特异性，患者依从性差。使用微流控阵列技术从60例患者（各10例）的“Training”队列中筛选出380个mirna，其中包括对照组、结直肠癌、CAA、乳腺癌（BC）、胰腺癌（PC）和肺癌（LC）。我们鉴定出结直肠肿瘤特异的异常mirna （p<0.05，错误发现率：5%，校正后α=0.0038）。这些mirna在120名患者的“测试”队列中使用单一测定法进行评估。我们建立了一个数学模型来预测150名患者“验证”队列中的盲法样本身份，通过对测试数据集进行重复次抽样验证，每次迭代1000次，以评估模型检测的准确性。根据p值、曲线下面积（AUC）、折叠变化和生物学合理性选择7种mirna （miR-21、miR-29c、miR-122、miR-192、miR-346、miR-372、miR-374a）。“Test”队列比较的AUC（±95% CI）分别为0.91（0.85-0.96）、0.79（0.70-0.88）和0.98（0.96-1.0）。我们的数学模型预测，所有肿瘤和对照组之间的盲法样本识别准确率为69-77%，结直肠癌肿瘤和其他癌症之间的准确率为67-76%，结直肠癌和结直肠癌腺瘤之间的准确率为86-90%。与目前的临床标准相比，我们的血浆miRNA检测和预测模型具有更高的灵敏度和特异性，可以将结直肠肿瘤与其他肿瘤患者和对照组区分开来。
Develop a plasma-based microRNA (miRNA) diagnostic assay specific for colorectal neoplasms, building upon our prior work. 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. Plasma was screened for 380 miRNAs using microfluidic array technology from a “Training” cohort of 60 patients, (10 each) control, CRC, CAA, breast (BC), pancreatic (PC) and lung (LC) 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. Seven miRNAs (miR-21, miR-29c, miR-122, miR-192, miR-346, miR-372, miR-374a) were selected based upon p-value, area-under-the-curve (AUC), fold-change, and biological plausibility. AUC (±95% CI) for “Test” cohort comparisons were 0.91 (0.85-0.96), 0.79 (0.70-0.88) and 0.98 (0.96-1.0), respectively. Our mathematical model predicted blinded sample identity with 69-77% accuracy between all neoplasia and controls, 67-76% accuracy between colorectal neoplasia and other cancers, and 86-90% accuracy between colorectal cancer and colorectal adenoma. Our plasma miRNA assay and prediction model differentiates colorectal neoplasia from patients with other neoplasms and from controls with higher sensitivity and specificity compared to current clinical standards.