Multiplexed Serum Biomarkers for the Detection of Lung Cancer.
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ABSTRACT:
SUBMITTER: Ma S
PROVIDER: S-EPMC5049985 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:PurposeCurrently the screening for lung cancer for risk groups is based on Computed Tomography (CT) or low dose CT (LDCT); however, the lung cancer death rate has not decreased significantly with people undergoing LDCT. We aimed to develop a simple reliable blood test for early detection of all types of lung cancer based on the immunogenicity of aberrant forms of BARD1 that are specifically upregulated in lung cancer.MethodsELISA assays were performed with a panel of BARD1 epitopes to detect serum levels of antibodies against BARD1 epitopes. We tested 194 blood samples from healthy donors and lung cancer patients with a panel of 40 BARD1 antigens. Using fitted Lasso logistic regression we determined the optimal combination of BARD1 antigens to be used in ELISA for discriminating lung cancer from healthy controls. Random selection of samples for training sets or validations sets was applied to validate the accuracy of our test.ResultsFitted Lasso logistic regression models predict high accuracy of the BARD1 autoimmune antibody test with an AUC = 0.96. Validation in independent samples provided and AUC = 0.86 and identical AUCs were obtained for combined stages 1-3 and late stage 4 lung cancers. The BARD1 antibody test is highly specific for lung cancer and not breast or ovarian cancer.ConclusionThe BARD1 lung cancer test shows higher sensitivity and specificity than previously published blood tests for lung cancer detection and/or diagnosis or CT scans, and it could detect all types and all stages of lung cancer. This BARD1 lung cancer test could therefore be further developed as i) screening test for early detection of lung cancers in high-risk groups, and ii) diagnostic aid in complementing CT scan.
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Project description:The aim of this study was to identify differentially-expressed miRNAs in the serum of non-small cell lung cancer (NSCLC) patients that might be a clinically-useful tool for lung cancer early detection. We performed miRNA expression profile analysis using TaqMan OpenArray Human panel in a discovery set of 70 serum samples obtained at lung tumor resection and 22 non-cancer subjects (NC). Selected serum miRNAs were then validated by quantitative PCR using an independent validation set of serum samples from LC patients (n = 84) and NC (n = 23). Sixty miRNAs were significantly up-regulated and 31 were down-regulated in the serum from NSCLC patients versus NC (adjusted p < 0.001). Four miRNAs (miR-193b, miR-301, miR-141 and miR-200b) were selected for validating their diagnostic value in an independent cohort. In the discovery set, the ROC plot derived from the combination of these miRNAs yielded an area under the curve (AUC) of 0.985 (95% CI 0.961-1.000, p < 0.001). In the test set, this miRNA signature exhibited an AUC of 0.993 (95% CI 0.979-1.000, p < 0.001). In conclusion, we identified a serum 4-miRNA signature that discriminated with high accuracy lung cancer patients from NC. Further prospective validation of this miRNA signature is warranted.
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