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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

Results of simulated prospective sample prediction. Simulation was achieved via an adjusted resampling strategy (Supplemental Fig. S1). (A) The upper panel shows pie diagrams of classification results derived from the simulation of prospective samples. The samples arranged according to their clinical diagnosis. Each pie represents one patient group. Each section of the pie represents the predicted sample memberships in percent. No Diagn. reflects 11 samples, which could not be classified to a specific disease or as healthy, because probabilities were below all cut off values. (B) The lower panel shows the classification of 4 representative patients. Patient 1 suffers from lung cancer; patient 2 was diagnosed with a limited UIP, patient 3 was diagnosed with COPD GOLDII and patient 4 is a healthy control. The x-axis represents the class dependence probability for each patient. The error bar indicates the range from the cut off value to a 100% probability.
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f0025: Results of simulated prospective sample prediction. Simulation was achieved via an adjusted resampling strategy (Supplemental Fig. S1). (A) The upper panel shows pie diagrams of classification results derived from the simulation of prospective samples. The samples arranged according to their clinical diagnosis. Each pie represents one patient group. Each section of the pie represents the predicted sample memberships in percent. No Diagn. reflects 11 samples, which could not be classified to a specific disease or as healthy, because probabilities were below all cut off values. (B) The lower panel shows the classification of 4 representative patients. Patient 1 suffers from lung cancer; patient 2 was diagnosed with a limited UIP, patient 3 was diagnosed with COPD GOLDII and patient 4 is a healthy control. The x-axis represents the class dependence probability for each patient. The error bar indicates the range from the cut off value to a 100% probability.

Mentions: The ultimate goal of our approach was an automated assignment of clinical samples to predefined diagnostic entities. Using all methylation markers detected in our analysis, we addressed their predictive power by an adjusted resampling strategy dividing all 204 plasma samples into 10 partitions. Each partition served as an unknown test sample during 10 rounds of automated clinical assignment (Supplemental Fig. S1). The synopsis of the classification is given in Fig. 5A demonstrating (a) the effectiveness of highly multiplexed MSRE enrichment for discrimination of the disease states tested (lung cancer, ILD, COPD and healthy), and (b) the overlaps between these clinical entities. Using cutoff-values derived from the corresponding training sets, it was possible to identify samples from cancer patients in 84.8% (28 of 33 cases). Patient samples derived from ILD patients were detected in 48.5% (33 of 68 cases), whereas COPD patients were discovered in 45.2% (19 of 42 cases). Healthy controls were identified in 50.8% (31 of 61 controls). Specificity was highest for diagnosis of lung cancer as depicted in Fig. 5A and B. A typical example for lung cancer (red) is shown in Fig. 5B demonstrating both the inter- and intra-individual discriminative power of multiplexed MSRE enrichment for lung cancer diagnosis. In comparison to cancer, specificity was lower for both ILD (blue) and COPD (green) samples, probably due to the considerable overlap between both diseases (Fig. 5B, patient 2 and 3). This is confirmed by the number of double positive predictions (n = 48). Discrimination of healthy samples from both cancer and ILD samples was very effective, whereas samples representing healthy and COPD demonstrated a large overlap, possibly due to the fact that in our group of COPD patients, early stage COPD (GOLD grade 1 and 2) was overrepresented (73.8%).


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)

Results of simulated prospective sample prediction. Simulation was achieved via an adjusted resampling strategy (Supplemental Fig. S1). (A) The upper panel shows pie diagrams of classification results derived from the simulation of prospective samples. The samples arranged according to their clinical diagnosis. Each pie represents one patient group. Each section of the pie represents the predicted sample memberships in percent. No Diagn. reflects 11 samples, which could not be classified to a specific disease or as healthy, because probabilities were below all cut off values. (B) The lower panel shows the classification of 4 representative patients. Patient 1 suffers from lung cancer; patient 2 was diagnosed with a limited UIP, patient 3 was diagnosed with COPD GOLDII and patient 4 is a healthy control. The x-axis represents the class dependence probability for each patient. The error bar indicates the range from the cut off value to a 100% probability.
© Copyright Policy - CC BY-NC-ND
Related In: Results  -  Collection

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

f0025: Results of simulated prospective sample prediction. Simulation was achieved via an adjusted resampling strategy (Supplemental Fig. S1). (A) The upper panel shows pie diagrams of classification results derived from the simulation of prospective samples. The samples arranged according to their clinical diagnosis. Each pie represents one patient group. Each section of the pie represents the predicted sample memberships in percent. No Diagn. reflects 11 samples, which could not be classified to a specific disease or as healthy, because probabilities were below all cut off values. (B) The lower panel shows the classification of 4 representative patients. Patient 1 suffers from lung cancer; patient 2 was diagnosed with a limited UIP, patient 3 was diagnosed with COPD GOLDII and patient 4 is a healthy control. The x-axis represents the class dependence probability for each patient. The error bar indicates the range from the cut off value to a 100% probability.
Mentions: The ultimate goal of our approach was an automated assignment of clinical samples to predefined diagnostic entities. Using all methylation markers detected in our analysis, we addressed their predictive power by an adjusted resampling strategy dividing all 204 plasma samples into 10 partitions. Each partition served as an unknown test sample during 10 rounds of automated clinical assignment (Supplemental Fig. S1). The synopsis of the classification is given in Fig. 5A demonstrating (a) the effectiveness of highly multiplexed MSRE enrichment for discrimination of the disease states tested (lung cancer, ILD, COPD and healthy), and (b) the overlaps between these clinical entities. Using cutoff-values derived from the corresponding training sets, it was possible to identify samples from cancer patients in 84.8% (28 of 33 cases). Patient samples derived from ILD patients were detected in 48.5% (33 of 68 cases), whereas COPD patients were discovered in 45.2% (19 of 42 cases). Healthy controls were identified in 50.8% (31 of 61 controls). Specificity was highest for diagnosis of lung cancer as depicted in Fig. 5A and B. A typical example for lung cancer (red) is shown in Fig. 5B demonstrating both the inter- and intra-individual discriminative power of multiplexed MSRE enrichment for lung cancer diagnosis. In comparison to cancer, specificity was lower for both ILD (blue) and COPD (green) samples, probably due to the considerable overlap between both diseases (Fig. 5B, patient 2 and 3). This is confirmed by the number of double positive predictions (n = 48). Discrimination of healthy samples from both cancer and ILD samples was very effective, whereas samples representing healthy and COPD demonstrated a large overlap, possibly due to the fact that in our group of COPD patients, early stage COPD (GOLD grade 1 and 2) was overrepresented (73.8%).

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