Limits...
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

Representative markers for differential diagnosis. Upper panel sections A and B demonstrate the effect of each variable on class probability. Class probability is given on the y-axis, while delta Ct-values are shown on the x-axis. Dependence of each predictor variable is averaged over the distribution of all modeled variables. The upper panel demonstrates the change of class probability (healthy, cancer, ILD, and COPD) as a function of Ct-value changes for the 4 top markers identified. The lower panels display boxplots of delta Ct-values for each marker. Due to the applied PCR methodology, lower delta Ct-values indicate increased marker methylation.
© Copyright Policy - CC BY-NC-ND
Related In: Results  -  Collection

License
getmorefigures.php?uid=PMC4563135&req=5

f0020: Representative markers for differential diagnosis. Upper panel sections A and B demonstrate the effect of each variable on class probability. Class probability is given on the y-axis, while delta Ct-values are shown on the x-axis. Dependence of each predictor variable is averaged over the distribution of all modeled variables. The upper panel demonstrates the change of class probability (healthy, cancer, ILD, and COPD) as a function of Ct-value changes for the 4 top markers identified. The lower panels display boxplots of delta Ct-values for each marker. Due to the applied PCR methodology, lower delta Ct-values indicate increased marker methylation.

Mentions: Introduction of cutoff values for each diagnostic group was determined by group membership probabilities obtained from gradient boosting classification (Fig. 3B, C). Significance of differentially methylated loci is given as relative variable importance (Fig. 3B, C) reflecting the number of decisions from gradient boosting classification made on basis of each candidate loci. Nonetheless, correlating markers, such as CACNA1B, ZIC1, DLX1, or SIM1, being potentially equally discriminative as the top markers PAX9, HOXD10, PTPRN2, and STAG3 given in Fig. 3B and C, do not appear in the figure as a result of the model building process. Detailed information of all assays including P-values und fold changes is given in Supplemental Table S5 & S6. The four top markers found by multiplexed MSRE enrichment strategy were HOXD10, PAX9, PTPRN2, and STAG 3. HOXD10 and STAG3 were capable of discriminating lung cancer, ILD and COPD from healthy (Fig. 4B), while PAX9 and PTPRN2 demonstrated a strong specificity for lung cancer (Fig. 4A).


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)

Representative markers for differential diagnosis. Upper panel sections A and B demonstrate the effect of each variable on class probability. Class probability is given on the y-axis, while delta Ct-values are shown on the x-axis. Dependence of each predictor variable is averaged over the distribution of all modeled variables. The upper panel demonstrates the change of class probability (healthy, cancer, ILD, and COPD) as a function of Ct-value changes for the 4 top markers identified. The lower panels display boxplots of delta Ct-values for each marker. Due to the applied PCR methodology, lower delta Ct-values indicate increased marker methylation.
© Copyright Policy - CC BY-NC-ND
Related In: Results  -  Collection

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

f0020: Representative markers for differential diagnosis. Upper panel sections A and B demonstrate the effect of each variable on class probability. Class probability is given on the y-axis, while delta Ct-values are shown on the x-axis. Dependence of each predictor variable is averaged over the distribution of all modeled variables. The upper panel demonstrates the change of class probability (healthy, cancer, ILD, and COPD) as a function of Ct-value changes for the 4 top markers identified. The lower panels display boxplots of delta Ct-values for each marker. Due to the applied PCR methodology, lower delta Ct-values indicate increased marker methylation.
Mentions: Introduction of cutoff values for each diagnostic group was determined by group membership probabilities obtained from gradient boosting classification (Fig. 3B, C). Significance of differentially methylated loci is given as relative variable importance (Fig. 3B, C) reflecting the number of decisions from gradient boosting classification made on basis of each candidate loci. Nonetheless, correlating markers, such as CACNA1B, ZIC1, DLX1, or SIM1, being potentially equally discriminative as the top markers PAX9, HOXD10, PTPRN2, and STAG3 given in Fig. 3B and C, do not appear in the figure as a result of the model building process. Detailed information of all assays including P-values und fold changes is given in Supplemental Table S5 & S6. The four top markers found by multiplexed MSRE enrichment strategy were HOXD10, PAX9, PTPRN2, and STAG 3. HOXD10 and STAG3 were capable of discriminating lung cancer, ILD and COPD from healthy (Fig. 4B), while PAX9 and PTPRN2 demonstrated a strong specificity for lung cancer (Fig. 4A).

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