Digital PCR for the Analysis of MYC Copy Number Variation in Lung Cancer

Background. MYC (v-myc avian myelocytomatosis viral oncogene homolog) is one of the most frequently amplified genes in lung tumors. For the analysis of gene copy number variations, dPCR (digital PCR) is an appropriate tool. The aim of our study was the assessment of dPCR for the detection of MYC copy number variations (CNV) in lung tissue considering clinicopathological parameters. Material and Methods. MYC status was analyzed with dPCR as well as qPCR (quantitative PCR) using gDNA (genomic DNA) from tumor and adjacent nontumor tissue samples of lung cancer patients. The performance of MYC was estimated based on the AUC (area under curve). Results. The results of the MYC amplification correlated significantly between dPCR and qPCR (rS=0.81, P<0.0001). The MYC copy number revealed by dPCR showed statistically significant differences between tumor and adjacent nontumor tissues. For discrimination, a sensitivity of 43% and a specificity of 99% were calculated, representing 55 true-positive and one false-positive tests. No statistically significant differences could be observed for age, sex, and smoking status or the clinicopathological parameters (histological subtype, grade, and stage). Conclusion. The results of the study show that dPCR is an accurate and reliable method for the determination of MYC copy numbers. The application is characterized by high specificity and moderate sensitivity. MYC amplification is a common event in lung cancer patients, and it is indicated that the determination of the MYC status might be useful in clinical diagnostics

Quantification of Four Major Metabolites of Embryotoxic <i>N</i>-Methyl- and <i>N</i>-Ethyl-2-pyrrolidone in Human Urine by Cooled-Injection Gas Chromatography and Isotope Dilution Mass Spectrometry

<i>N</i>-Methyl- and <i>N</i>-ethyl-2-pyrollidone (NMP and NEP) are frequently used industrial solvents and were shown to be embryotoxic in animal experiments. We developed a sensitive, specific, and robust analytical method based on cooled-injection (CIS) gas chromatography and isotope dilution mass spectrometry to analyze 5-hydroxy-<i>N</i>-ethyl-2-pyrrolidone (5-HNEP) and 2-hydroxy-<i>N</i>-ethylsuccinimide (2-HESI), two newly identified presumed metabolites of NEP, and their corresponding methyl counterparts (5-HNMP, 2-HMSI) in human urine. The urine was spiked with deuterium-labeled analogues of these metabolites. The analytes were separated from urinary matrix by solid-phase extraction and silylated prior to quantification. Validation of this method was carried out by using both, spiked pooled urine samples and urine samples from 56 individuals of the general population with no known occupational exposure to NMP and NEP. Interday and intraday imprecision was better than 8% for all metabolites, while the limits of detection were between 5 and 20 μg/L depending on the analyte. The high sensitivity of the method enables us to quantify NMP and NEP metabolites at current environmental exposures by human biomonitoring

Plasma Proteomics Enable Differentiation of Lung Adenocarcinoma from Chronic Obstructive Pulmonary Disease (COPD)

Chronic obstructive pulmonary disease (COPD) is a major risk factor for the development of lung adenocarcinoma (AC). AC often develops on underlying COPD; thus, the differentiation of both entities by biomarker is challenging. Although survival of AC patients strongly depends on early diagnosis, a biomarker panel for AC detection and differentiation from COPD is still missing. Plasma samples from 176 patients with AC with or without underlying COPD, COPD patients, and hospital controls were analyzed using mass-spectrometry-based proteomics. We performed univariate statistics and additionally evaluated machine learning algorithms regarding the differentiation of AC vs. COPD and AC with COPD vs. COPD. Univariate statistics revealed significantly regulated proteins that were significantly regulated between the patient groups. Furthermore, random forest classification yielded the best performance for differentiation of AC vs. COPD (area under the curve (AUC) 0.935) and AC with COPD vs. COPD (AUC 0.916). The most influential proteins were identified by permutation feature importance and compared to those identified by univariate testing. We demonstrate the great potential of machine learning for differentiation of highly similar disease entities and present a panel of biomarker candidates that should be considered for the development of a future biomarker panel

Assessment of MYC\it MYC and TERT\it TERT copy number variations in lung cancer using digital PCR

$\bf Objective$ Lung cancer is the second most frequent cancer type and the most common cause of cancer-related deaths worldwide. Alteration of gene copy numbers are associated with lung cancer and the determination of copy number variations (CNV) is appropriate for the discrimination between tumor and non-tumor tissue in lung cancer. As telomerase reverse transcriptase ($\it TERT$) and v-myc avian myelocytomatosis viral oncogene homolog ($\it MYC$) play a role in lung cancer the aims of this study were the verification of our recent results analyzing $\it MYC$ CNV in tumor and non-tumor tissue of lung cancer patients using an independent study group and the assessment of $\it TERT$ CNV as an additional marker. $\bf Results$ $\it TERT$ and $\it MYC$ status was analyzed using digital PCR (dPCR) in tumor and adjacent non-tumor tissue samples of 114 lung cancer patients. The difference between tumor and non-tumor samples were statistically significant (p < 0.0001) for $\it TERT$ and $\it MYC$. Using a predefined specificity of 99% a sensitivity of 41% and 51% was observed for $\it TERT$ and $\it MYC$, respectively. For the combination of $\it TERT$ and $\it MYC$ the overall sensitivity increased to 60% at 99% specificity. We demonstrated that a combination of markers increases the performance in comparison to individual markers. Additionally, the determination of CNV using dPCR might be an appropriate tool in precision medicine

Methylation of L1RE1, RARB,\textit {L1RE1, RARB,} and RASSF1\it RASSF1 function as possible biomarkers for the differential diagnosis of lung cancer

$\bf Background$ Lung cancer is the major cause of cancer-related deaths worldwide. Differential diagnosis can be difficult, especially when only small samples are available. Epigenetic changes are frequently tissue-specific events in carcinogenesis and hence may serve as diagnostic biomarkers. $\textbf {Material and methods}$ 138 representative formalin-fixed, paraffin-embedded (FFPE) tissues (116 lung cancer cases and 22 benign controls) were used for targeted DNA methylation analysis via pyrosequencing of ten literature-derived methylation markers $\textit ({APC, CDH1, CDKN2A, EFEMP1, FHIT, L1RE1, MGMT, PTEN, RARB,}$ and $\textit {RASSF1}$). Methylation levels were analyzed with the Classification and Regression Tree Algorithm (CART), Conditional Interference Trees (ctree) and ROC. Validation was performed with additional 27 lung cancer cases and 38 benign controls. TCGA data for 282 lung cancer cases was included in the analysis. $\bf Results$ CART and ctree analysis identified the combination of $\it {L1RE1}$ and $\it RARB$ as well as $\it {L1RE1}$ and $\it {RASSF1}$ as independent methylation markers with high discriminative power between tumor and benign tissue (for each combination, 91% specificity and 100% sensitivity). $\it {L1RE1}$ methylation associated significantly with tumor type and grade (p<0.001) with highest methylation in the control group. The opposite was found for $\it RARB$ (p<0.001). $\it RASSF1$ methylation increased with tumor type and grade (p<0.001) with strongest methylation in neuroendocrine tumors (NET). $\bf Conclusion$ Hypomethylation of $\it {L1RE1}$ is frequent in tumors compared to benign controls and associates with higher grade, whereas increasing methylation of $\it RARB$ is an independent marker for tumors and higher grade. $\it RASSF1$ hypermethylation was frequent in tumors and most prominent in NET making it an auxiliary marker for separation of NSCLC and NET. $\it {L1RE1}$ in combination with either $\it RARB$ or $\it RASSF1$ could function as biomarkers for separating lung cancer and non-cancerous tissue and could be useful for samples of limited size such as biopsies

Plasma proteomics enable differentiation of lung adenocarcinoma from chronic obstructive pulmonary disease (COPD)

Chronic obstructive pulmonary disease (COPD) is a major risk factor for the development of lung adenocarcinoma (AC). AC often develops on underlying COPD; thus, the differentiation of both entities by biomarker is challenging. Although survival of AC patients strongly depends on early diagnosis, a biomarker panel for AC detection and differentiation from COPD is still missing. Plasma samples from 176 patients with AC with or without underlying COPD, COPD patients, and hospital controls were analyzed using mass-spectrometry-based proteomics. We performed univariate statistics and additionally evaluated machine learning algorithms regarding the differentiation of AC vs. COPD and AC with COPD vs. COPD. Univariate statistics revealed significantly regulated proteins that were significantly regulated between the patient groups. Furthermore, random forest classification yielded the best performance for differentiation of AC vs. COPD (area under the curve (AUC) 0.935) and AC with COPD vs. COPD (AUC 0.916). The most influential proteins were identified by permutation feature importance and compared to those identified by univariate testing. We demonstrate the great potential of machine learning for differentiation of highly similar disease entities and present a panel of biomarker candidates that should be considered for the development of a future biomarker panel

Major results from the first plasma campaign of the Wendelstein 7-X stellarator

After completing the main construction phase of Wendelstein 7-X (W7-X) and successfully commissioning the device, first plasma operation started at the end of 2015. Integral commissioning of plasma start-up and operation using electron cyclotron resonance heating (ECRH) and an extensive set of plasma diagnostics have been completed, allowing initial physics studies during the first operational campaign. Both in helium and hydrogen, plasma breakdown was easily achieved. Gaining experience with plasma vessel conditioning, discharge lengths could be extended gradually. Eventually, discharges lasted up to 6 s, reaching an injected energy of 4 MJ, which is twice the limit originally agreed for the limiter configuration employed during the first operational campaign. At power levels of 4 MW central electron densities reached 3 1019 m-3, central electron temperatures reached values of 7 keV and ion temperatures reached just above 2 keV. Important physics studies during this first operational phase include a first assessment of power balance and energy confinement, ECRH power deposition experiments, 2nd harmonic O-mode ECRH using multi-pass absorption, and current drive experiments using electron cyclotron current drive. As in many plasma discharges the electron temperature exceeds the ion temperature significantly, these plasmas are governed by core electron root confinement showing a strong positive electric field in the plasma centre.Peer reviewe

Major results from the first plasma campaign of the Wendelstein 7-X stellarator

\u3cp\u3eAfter completing the main construction phase of Wendelstein 7-X (W7-X) and successfully commissioning the device, first plasma operation started at the end of 2015. Integral commissioning of plasma start-up and operation using electron cyclotron resonance heating (ECRH) and an extensive set of plasma diagnostics have been completed, allowing initial physics studies during the first operational campaign. Both in helium and hydrogen, plasma breakdown was easily achieved. Gaining experience with plasma vessel conditioning, discharge lengths could be extended gradually. Eventually, discharges lasted up to 6 s, reaching an injected energy of 4 MJ, which is twice the limit originally agreed for the limiter configuration employed during the first operational campaign. At power levels of 4 MW central electron densities reached 3 10\u3csup\u3e19\u3c/sup\u3e m\u3csup\u3e-3\u3c/sup\u3e, central electron temperatures reached values of 7 keV and ion temperatures reached just above 2 keV. Important physics studies during this first operational phase include a first assessment of power balance and energy confinement, ECRH power deposition experiments, 2nd harmonic O-mode ECRH using multi-pass absorption, and current drive experiments using electron cyclotron current drive. As in many plasma discharges the electron temperature exceeds the ion temperature significantly, these plasmas are governed by core electron root confinement showing a strong positive electric field in the plasma centre.\u3c/p\u3