Hereditary cancer predisposition syndromes are responsible for approximately 5–10% of all diagnosed cancer cases. In the past, single-gene analysis of specific high risk genes was used for the determination of the genetic cause of cancer heritability in certain families. The application of Next Generation Sequencing (NGS) technology has facilitated multigene panel analysis and is widely used in clinical practice, for the identification of individuals with cancer predisposing gene variants. The purpose of this study was to investigate the extent and nature of variants in genes implicated in hereditary cancer predisposition in individuals referred for testing in our laboratory. In total, 1197 individuals from Greece, Romania and Turkey were referred to our laboratory for genetic testing in the past 4 years. The majority of referrals included individuals with personal of family history of breast and/or ovarian cancer. The analysis of genes involved in hereditary cancer predisposition was performed using a NGS approach. Genomic DNA was enriched for targeted regions of 36 genes and sequencing was carried out using the Illumina NGS technology. The presence of large genomic rearrangements (LGRs) was investigated by computational analysis and Multiplex Ligation-dependent Probe Amplification (MLPA). A pathogenic variant was identified in 264 of 1197 individuals (22.1%) analyzed while a variant of uncertain significance (VUS) was identified in 34.8% of cases. Clinically significant variants were identified in 29 of the 36 genes analyzed. Concerning the mutation distribution among individuals with positive findings, 43.6% were located in the BRCA1/2 genes whereas 21.6, 19.9, and 15.0% in other high, moderate and low risk genes respectively. Notably, 25 of the 264 positive individuals (9.5%) carried clinically significant variants in two different genes and 6.1% had a LGR. In our cohort, analysis of all the genes in the panel allowed the identification of 4.3 and 8.1% additional pathogenic variants in other high or moderate/low risk genes, respectively, enabling personalized management decisions for these individuals and supporting the clinical significance of multigene panel analysis in hereditary cancer predisposition.

Non‑small cell lung cancer (NSCLC) is the most common type of lung cancer and a tumor with a broad spectrum of targeted therapies already available or in clinical trials. Thus, molecular characterization of the tumor using next generation sequencing (NGS) technology, has become a key tool for facilitating treatment decisions and the clinical management of NSCLC patients. The performance of a custom 23 gene multiplex amplification hot spot panel, based on Ion AmpliSeq™ technology, was evaluated for the analysis of tumor DNA extracted from formalin-fixed and paraffin-embedded (FFPE) tissues. Furthermore, the Ion AmpliSeq™ RNA Fusion Lung Cancer Research Panel was used for fusion RNA transcript analysis. The mutation spectrum of the tumors was determined in a cohort of 502 patients with NSCLC using the aforementioned targeted gene panels. The panel used for tumor DNA analysis in this study exhibited high rates (100%) of sensitivity, specificity and reproducibility at a mutation allelic frequency of 3%. At least one DNA mutation was detected in 374 patients (74.5%) and an RNA fusion was identified in 16 patients, (3.2%). In total, alterations in a cancer-driver gene were identified (including point mutations, gene rearrangements and MET amplifications) in 77.6% of the tumors tested. Among the NSCLC patients, 23% presented a mutation in a gene associated with approved or emerging targeted therapy. More specifically, 13.5% (68/502) presented a mutation in a gene with approved targeted therapy (EGFR, ALK, ROS1) and 9.4% (47/502) had an alteration in a gene related to emerging targeted therapies (ERBB2, BRAF, MET and RET). Furthermore, 51.6% of the patients had a mutation in a gene that could be related to an off label therapy or indicative for access to a clinical trial. Thus, the targeted NGS panel used in this study is a reliable approach for tumor molecular profiling and can be applied in personalized treatment decision making for NSCLC patients.

Analysis of circulating tumor nucleic acids in plasma of Non-Small Cell Lung Cancer (NSCLC) patients is the most widespread and documented form of "liquid biopsy" and provides real-time information on the molecular profile of the tumor without an invasive tissue biopsy. Liquid biopsy analysis was requested by the referral physician in 121 NSCLC patients at diagnosis and was performed using a sensitive Next Generation Sequencing assay. Additionally, a comparative analysis of NSCLC patients at relapse following EGFR Tyrosine Kinase Inhibitor (TKIs) treatment was performed in 50 patients by both the cobas and NGS platforms. At least one mutation was identified in almost 49% of the cases by the NGS approach in NSCLC patients analyzed at diagnosis. In 36 cases with paired tissue available a high concordance of 86.11% was observed for clinically relevant mutations, with a Positive Predictive Value (PPV) of 88.89%. Furthermore, a concordance rate of 82% between cobas and the NGS approach for the EGFR sensitizing mutations (in exons 18, 19, 21) was observed in patients with acquired resistance to EGFR TKIs, while this concordance was 94% for the p.T790M mutation, with NGS being able to detect this mutation in three 3 additional patients. This study indicates the feasibility of circulating tumor nucleic acids (ctNA) analysis as a tumor biopsy surrogate in clinical practice for NSCLC personalized treatment decision making. The use of new sensitive NGS techniques can reliably detect tumor-derived mutations in liquid biopsy and provide clinically relevant information both before and after targeted treatment in patients with NSCLC. Thus, it could aid physicians in treatment decision making in clinical practice.

Summary: ExTopoDB is a publicly accessible database of experimentally derived topological models of transmembrane proteins. It contains information collected from studies in the literature that report the use of biochemical methods for the determination of the topology of α-helical transmembrane proteins. Transmembrane protein topology is highly important in order to understand their function and ExTopoDB provides an up to date, complete and comprehensive dataset of experimentally determined topologies of α-helical transmembrane proteins. Topological information is combined with transmembrane topology prediction resulting in more reliable topological models. Availability:http://bioinformatics.biol.uoa.gr/ExTopoDB Contact:shamodr@biol.uoa.gr Supplementary information: Supplementary data are available at Bioinformatics online.