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Diagnostic Performance of Plasma DNA Methylation Profiles in Lung Cancer, Pulmonary Fibrosis and COPD.

Wielscher M, Vierlinger K, Kegler U, Ziesche R, Gsur A, Weinhäusel A - EBioMedicine (2015)

Bottom Line: Disease-specific alterations of the cell-free DNA methylation status are frequently found in serum samples and are currently considered to be suitable biomarkers.The results were confirmed using an independent sample set (n = 46) by use of the four top markers discovered in the study (HOXD10, PAX9, PTPRN2, and STAG3) yielding an AUC of 0.85 (95%CI: 0.72-0.95).This technique was capable of distinguishing interrelated complex pulmonary diseases suggesting that multiplexed MSRE enrichment might be useful for simple and reliable diagnosis of diverse multifactorial disease states.

View Article: PubMed Central - PubMed

Affiliation: AIT - Austrian Institute of Technology, Health & Environment Department, Molecular Diagnostics Unit, Muthgasse 11/2, 1190 Vienna, Austria.

ABSTRACT
Disease-specific alterations of the cell-free DNA methylation status are frequently found in serum samples and are currently considered to be suitable biomarkers. Candidate markers were identified by bisulfite conversion-based genome-wide methylation screening of lung tissue from lung cancer, fibrotic ILD, and COPD. cfDNA from 400 μl serum (n = 204) served to test the diagnostic performance of these markers. Following methylation-sensitive restriction enzyme digestion and enrichment of methylated DNA via targeted amplification (multiplexed MSRE enrichment), a total of 96 markers were addressed by highly parallel qPCR. Lung cancer was efficiently separated from non-cancer and controls with a sensitivity of 87.8%, (95%CI: 0.67-0.97) and specificity 90.2%, (95%CI: 0.65-0.98). Cancer was distinguished from ILD with a specificity of 88%, (95%CI: 0.57-1), and COPD from cancer with a specificity of 88% (95%CI: 0.64-0.97). Separation of ILD from COPD and controls was possible with a sensitivity of 63.1% (95%CI: 0.4-0.78) and a specificity of 70% (95%CI: 0.54-0.81). The results were confirmed using an independent sample set (n = 46) by use of the four top markers discovered in the study (HOXD10, PAX9, PTPRN2, and STAG3) yielding an AUC of 0.85 (95%CI: 0.72-0.95). This technique was capable of distinguishing interrelated complex pulmonary diseases suggesting that multiplexed MSRE enrichment might be useful for simple and reliable diagnosis of diverse multifactorial disease states.

No MeSH data available.


Related in: MedlinePlus

Proof of Principle: Prospective sample prediction. ROC curve analysis of 46 patient samples. Prediction is based on coefficients derived from the model presented Fig. 3. The dashed line represents the separation of cancer and non-cancer patients, applying a weighted model of all 64 variables. The solid line represents the prediction based on the top 4 markers. Panel below gives boxplots of the delta Ct-values for each marker out of top four marker model. Due to the applied PCR methodology lower delta Ct-values indicate an increased methylation of the marker.
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f0030: Proof of Principle: Prospective sample prediction. ROC curve analysis of 46 patient samples. Prediction is based on coefficients derived from the model presented Fig. 3. The dashed line represents the separation of cancer and non-cancer patients, applying a weighted model of all 64 variables. The solid line represents the prediction based on the top 4 markers. Panel below gives boxplots of the delta Ct-values for each marker out of top four marker model. Due to the applied PCR methodology lower delta Ct-values indicate an increased methylation of the marker.

Mentions: Based on the predictive power of our approach for lung cancer, we then analyzed 46 new samples (healthy: n = 23; lung cancer: n = 23) comparing the full prediction model based on all methylation markers with a prediction model using only the 4 top markers by that addressing quality and stability of our automated prediction procedure (proof of principle; PoP-set, Table 1). ROC curve analysis showed that the 4-marker model (Fig. 6A, solid line) outperformed the full 64-marker model (Fig. 6A, dotted line) yielding an AUC of 0.85 (95%CI: 0.72–0.95) with a sensitivity of 0.97 (95%CI: 0.61–1) and a specificity of 0.73 (95%CI: 0.61–0.83). Using this approach, 22 of the 23 cancer samples were correctly identified, whereas two healthy cases were rated as COPD and 8 healthy controls as cancer (Supplemental Fig. S10).


Diagnostic Performance of Plasma DNA Methylation Profiles in Lung Cancer, Pulmonary Fibrosis and COPD.

Wielscher M, Vierlinger K, Kegler U, Ziesche R, Gsur A, Weinhäusel A - EBioMedicine (2015)

Proof of Principle: Prospective sample prediction. ROC curve analysis of 46 patient samples. Prediction is based on coefficients derived from the model presented Fig. 3. The dashed line represents the separation of cancer and non-cancer patients, applying a weighted model of all 64 variables. The solid line represents the prediction based on the top 4 markers. Panel below gives boxplots of the delta Ct-values for each marker out of top four marker model. Due to the applied PCR methodology lower delta Ct-values indicate an increased methylation of the marker.
© Copyright Policy - CC BY-NC-ND
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4563135&req=5

f0030: Proof of Principle: Prospective sample prediction. ROC curve analysis of 46 patient samples. Prediction is based on coefficients derived from the model presented Fig. 3. The dashed line represents the separation of cancer and non-cancer patients, applying a weighted model of all 64 variables. The solid line represents the prediction based on the top 4 markers. Panel below gives boxplots of the delta Ct-values for each marker out of top four marker model. Due to the applied PCR methodology lower delta Ct-values indicate an increased methylation of the marker.
Mentions: Based on the predictive power of our approach for lung cancer, we then analyzed 46 new samples (healthy: n = 23; lung cancer: n = 23) comparing the full prediction model based on all methylation markers with a prediction model using only the 4 top markers by that addressing quality and stability of our automated prediction procedure (proof of principle; PoP-set, Table 1). ROC curve analysis showed that the 4-marker model (Fig. 6A, solid line) outperformed the full 64-marker model (Fig. 6A, dotted line) yielding an AUC of 0.85 (95%CI: 0.72–0.95) with a sensitivity of 0.97 (95%CI: 0.61–1) and a specificity of 0.73 (95%CI: 0.61–0.83). Using this approach, 22 of the 23 cancer samples were correctly identified, whereas two healthy cases were rated as COPD and 8 healthy controls as cancer (Supplemental Fig. S10).

Bottom Line: Disease-specific alterations of the cell-free DNA methylation status are frequently found in serum samples and are currently considered to be suitable biomarkers.The results were confirmed using an independent sample set (n = 46) by use of the four top markers discovered in the study (HOXD10, PAX9, PTPRN2, and STAG3) yielding an AUC of 0.85 (95%CI: 0.72-0.95).This technique was capable of distinguishing interrelated complex pulmonary diseases suggesting that multiplexed MSRE enrichment might be useful for simple and reliable diagnosis of diverse multifactorial disease states.

View Article: PubMed Central - PubMed

Affiliation: AIT - Austrian Institute of Technology, Health & Environment Department, Molecular Diagnostics Unit, Muthgasse 11/2, 1190 Vienna, Austria.

ABSTRACT
Disease-specific alterations of the cell-free DNA methylation status are frequently found in serum samples and are currently considered to be suitable biomarkers. Candidate markers were identified by bisulfite conversion-based genome-wide methylation screening of lung tissue from lung cancer, fibrotic ILD, and COPD. cfDNA from 400 μl serum (n = 204) served to test the diagnostic performance of these markers. Following methylation-sensitive restriction enzyme digestion and enrichment of methylated DNA via targeted amplification (multiplexed MSRE enrichment), a total of 96 markers were addressed by highly parallel qPCR. Lung cancer was efficiently separated from non-cancer and controls with a sensitivity of 87.8%, (95%CI: 0.67-0.97) and specificity 90.2%, (95%CI: 0.65-0.98). Cancer was distinguished from ILD with a specificity of 88%, (95%CI: 0.57-1), and COPD from cancer with a specificity of 88% (95%CI: 0.64-0.97). Separation of ILD from COPD and controls was possible with a sensitivity of 63.1% (95%CI: 0.4-0.78) and a specificity of 70% (95%CI: 0.54-0.81). The results were confirmed using an independent sample set (n = 46) by use of the four top markers discovered in the study (HOXD10, PAX9, PTPRN2, and STAG3) yielding an AUC of 0.85 (95%CI: 0.72-0.95). This technique was capable of distinguishing interrelated complex pulmonary diseases suggesting that multiplexed MSRE enrichment might be useful for simple and reliable diagnosis of diverse multifactorial disease states.

No MeSH data available.


Related in: MedlinePlus