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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

Differential diagnosis approach. (A) The classification scheme starts with the separation of cancer samples, which are subsequently subdivided into TNMI&II and TNMIII&IV. The non-cancer samples are classified into healthy, fibrosis and COPD. This scheme implicates three prediction rounds, with results given in (B) and (C). The result for the separation of cancer TNMI&II and cancer TNMIII&IV is given in Supplemental Fig. S9. Percent values indicate the correct classification rate applying the determined cut off value given in sections B and C for each disease. (B and C) Bar plots indicate the relative variable importance for each model. The relative variable importance reflects the contribution of each variable to the prediction success. ROC curves indicate the quality of group separation including the chosen cut off value given as black dot. The rightmost panel summarizes the ROC curve analysis starting with the values (Area under curve, Sensitivity and Specificity) derived from conventional ROC curve analysis. The section below, starting with “CutOff”, lists the values gained by the application of the specific cut off values. (B) Distinguishes between cancer and residual samples(C) classify residual samples into healthy, fibrotic ILD and COPD.
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f0015: Differential diagnosis approach. (A) The classification scheme starts with the separation of cancer samples, which are subsequently subdivided into TNMI&II and TNMIII&IV. The non-cancer samples are classified into healthy, fibrosis and COPD. This scheme implicates three prediction rounds, with results given in (B) and (C). The result for the separation of cancer TNMI&II and cancer TNMIII&IV is given in Supplemental Fig. S9. Percent values indicate the correct classification rate applying the determined cut off value given in sections B and C for each disease. (B and C) Bar plots indicate the relative variable importance for each model. The relative variable importance reflects the contribution of each variable to the prediction success. ROC curves indicate the quality of group separation including the chosen cut off value given as black dot. The rightmost panel summarizes the ROC curve analysis starting with the values (Area under curve, Sensitivity and Specificity) derived from conventional ROC curve analysis. The section below, starting with “CutOff”, lists the values gained by the application of the specific cut off values. (B) Distinguishes between cancer and residual samples(C) classify residual samples into healthy, fibrotic ILD and COPD.

Mentions: For differential diagnosis, the same resampling strategy was used (Fig. 2B). As depicted in Fig. 3A, three prediction rounds were performed to classify each sample of the entire sample pool. The first prediction round separated cancer from non-cancer cases in 90.6% (Fig. 3A and B). In the second round, cancer cases were subdivided into TNM classes I and II or TNM classes III and IV (Supplemental Fig. S9). The third prediction round distinguished between healthy controls, COPD, and ILD cases (Fig. 3A, 3C).


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)

Differential diagnosis approach. (A) The classification scheme starts with the separation of cancer samples, which are subsequently subdivided into TNMI&II and TNMIII&IV. The non-cancer samples are classified into healthy, fibrosis and COPD. This scheme implicates three prediction rounds, with results given in (B) and (C). The result for the separation of cancer TNMI&II and cancer TNMIII&IV is given in Supplemental Fig. S9. Percent values indicate the correct classification rate applying the determined cut off value given in sections B and C for each disease. (B and C) Bar plots indicate the relative variable importance for each model. The relative variable importance reflects the contribution of each variable to the prediction success. ROC curves indicate the quality of group separation including the chosen cut off value given as black dot. The rightmost panel summarizes the ROC curve analysis starting with the values (Area under curve, Sensitivity and Specificity) derived from conventional ROC curve analysis. The section below, starting with “CutOff”, lists the values gained by the application of the specific cut off values. (B) Distinguishes between cancer and residual samples(C) classify residual samples into healthy, fibrotic ILD and COPD.
© Copyright Policy - CC BY-NC-ND
Related In: Results  -  Collection

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

f0015: Differential diagnosis approach. (A) The classification scheme starts with the separation of cancer samples, which are subsequently subdivided into TNMI&II and TNMIII&IV. The non-cancer samples are classified into healthy, fibrosis and COPD. This scheme implicates three prediction rounds, with results given in (B) and (C). The result for the separation of cancer TNMI&II and cancer TNMIII&IV is given in Supplemental Fig. S9. Percent values indicate the correct classification rate applying the determined cut off value given in sections B and C for each disease. (B and C) Bar plots indicate the relative variable importance for each model. The relative variable importance reflects the contribution of each variable to the prediction success. ROC curves indicate the quality of group separation including the chosen cut off value given as black dot. The rightmost panel summarizes the ROC curve analysis starting with the values (Area under curve, Sensitivity and Specificity) derived from conventional ROC curve analysis. The section below, starting with “CutOff”, lists the values gained by the application of the specific cut off values. (B) Distinguishes between cancer and residual samples(C) classify residual samples into healthy, fibrotic ILD and COPD.
Mentions: For differential diagnosis, the same resampling strategy was used (Fig. 2B). As depicted in Fig. 3A, three prediction rounds were performed to classify each sample of the entire sample pool. The first prediction round separated cancer from non-cancer cases in 90.6% (Fig. 3A and B). In the second round, cancer cases were subdivided into TNM classes I and II or TNM classes III and IV (Supplemental Fig. S9). The third prediction round distinguished between healthy controls, COPD, and ILD cases (Fig. 3A, 3C).

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