Background: Malignant pleural mesothelioma (MPM) is a fatal tumor with a poor prognosis. The recent developments of liquid biopsies could provide novel diagnostic and prognostic tools in oncology. However, there is limited information about the feasibility of this technique for MPMs. Here, we investigate whether cancer-specific DNA sequences can be detected in pleural fluids and plasma of MPM patients as free circulating tumor DNA (ctDNA). Methods: We performed whole-exome sequencing on 14 tumor biopsies from 14 patients, and we analyzed 20 patient-specific somatic mutations with digital droplet PCR (ddPCR) in pleural fluids and plasma, using them as cancer-specific tumor biomarkers. Results: Most of the selected mutations could be detected in pleural fluids (94%) and, noteworthy, in plasma (83%) with the use of ddPCR. Pleural fluids showed similar levels of somatically mutated ctDNA (median = 12.75%, average = 16.3%, standard deviation = 12.3) as those detected in solid biopsies (median = 21.95%; average = 22.21%; standard deviation = 9.57), and their paired difference was weakly statistically significant (p = 0.048). On the other hand, the paired difference between solid biopsies and ctDNA from plasma (median = 0.29%, average = 0.89%, standard deviation = 1.40) was highly statistically significant (p = 2.5 × 10−7), corresponding to the important drop of circulating somatically mutated DNA in the bloodstream. However, despite the tiny amount of ctDNA in plasma, varying from 5.57% down to 0.14%, the mutations were detectable at rates similar to those possible for other tumors. Conclusions: We found robust evidence that mutated DNA is spilled from MPMs, mostly into pleural fluids, proving the concept that liquid biopsies are feasible for MPM patients.
To identify a peculiar genetic combination predisposing to differentiated thyroid carcinoma (DTC), we selected a set of single-nucleotide polymorphisms (SNPs) associated with DTC risk, considering polygenic risk score (PRS), Bayesian statistics, and a machine learning (ML) classifier to describe cases and controls in 3 different datasets. Dataset 1 (649 DTC, 431 controls) has been previously genotyped in a genome-wide association study (GWAS) on Italian DTC. Dataset 2 (234 DTC, 101 controls) and dataset 3 (404 DTC, 392 controls) were genotyped. Associations of 171 SNPs reported to predispose to DTC in candidate studies were extracted from the GWAS of dataset 1, followed by replication of SNPs associated with DTC risk (P<0.05) in dataset 2. The reliability of the identified SNPs was confirmed by PRS and Bayesian statistics after merging the three datasets. SNPs were used to describe the case/control state of individuals by ML classifier. Starting from 171 SNPs associated with DTC, 15 were positive in both the datasets 1 and 2. Using these markers, PRS revealed that individuals in the fifth quintile had a 7-fold increased risk of DTC than those in the first. Bayesian inference confirmed that the selected 15 SNPs differentiate cases from controls. Results were corroborated by ML, finding a maximum AUC of about 0.7. A restricted selection of only 15 DTC-associated SNPs is able to describe the inner genetic structure of Italian individuals and ML allows a fair prediction of case or control status based solely on the individual genetic background.
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