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

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