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Background/aim: The application of next-generation sequencing (NGS) technology in the genetic investigation of hereditary cancer is important for clinical surveillance, therapeutic approach, and reducing the risk of developing new malignancies. The aim of the study was to explore genetic predisposition in individuals referred for hereditary cancer., Materials and Methods: A total of 8,261 individuals were referred for multigene genetic testing, during the period 2020-2023, in the laboratory, and underwent multigene genetic testing using NGS. Among the examined individuals, 56.17% were diagnosed with breast cancer, 6.77% with ovarian cancer, 2.88% with colorectal cancer, 1.91% with prostate cancer, 6.43% were healthy with a significant family history of cancer, while 3.06% had a different type of cancer and 0.21% had not provided any information. Additionally, in 85 women with breast cancer we performed whole exome sequencing analysis., Results: 20% of the examined individuals carried a pathogenic variant. Specifically, 54.8% of the patients had a pathogenic variant in a clinically significant gene (BRCA1, BRCA2, PALB2, RAD51C, PMS2, CDKN2A, MLH1, MSH2, TP53, MSH6, APC, RAD51D, PTEN, RET, CDH1, MEN1, and VHL). Among the different types of pathogenic variants detected, a significant percentage (6.52%) represented copy number variation (CNV). With WES analysis, the following findings were detected: CTC1: c.880C>T, p.(Gln294*); MLH3: c.405del, p.(Asp136Metfs*2), PPM1D: c.1426_1430del, p.(Glu476Leufs*3), and SDHB: c.395A>G, p.(His132Arg)., Conclusion: Comprehensive multigene genetic testing is necessary for appropriate clinical management of pathogenic variants' carriers. Additionally, the information obtained is important for determining the risk of malignancy development in family members of the examined individuals., (Copyright © 2024, International Institute of Anticancer Research (Dr. George J. Delinasios), All rights reserved.)

Breast cancer (BC) is the most prominent tumor type among women, accounting for 32% of newly diagnosed cancer cases. BC risk factors include inherited germline pathogenic gene variants and family history of disease. However, the etiology of the disease remains occult in most cases. Therefore, in the absence of high-risk factors, a polygenic basis has been suggested to contribute to susceptibility. This information is utilized to calculate the Polygenic Risk Score (PRS) which is indicative of BC risk. This study aimed to evaluate retrospectively the clinical usefulness of PRS integration in BC risk calculation, utilizing a group of patients who have already been diagnosed with BC. The study comprised 105 breast cancer patients with hereditary genetic analysis results obtained by NGS. The selection included all testing results: high-risk gene-positive, intermediate/low-risk gene-positive, and negative. PRS results were obtained from an external laboratory (Allelica). PRS-based BC risk was computed both with and without considering additional risk factors, including gene status and family history. A significantly different PRS percentile distribution consistent with higher BC risk was observed in our cohort compared to the general population. Higher PRS-based BC risks were detected in younger patients and in those with FH of cancers. Among patients with a pathogenic germline variant detected, reduced PRS values were observed, while the BC risk was mainly determined by a monogenic etiology. Upon comprehensive analysis encompassing FH, gene status, and PRS, it was determined that 41.90% (44/105) of the patients demonstrated an elevated susceptibility for BC. Moreover, 63.63% of the patients with FH of BC and without an inherited pathogenic genetic variant detected showed increased BC risk by incorporating the PRS result. Our results indicate a major utility of PRS calculation in women with FH in the absence of a monogenic etiology detected by NGS. By combining high-risk strategies, such as inherited disease analysis, with low-risk screening strategies, such as FH and PRS, breast cancer risk stratification can be improved. This would facilitate the development of more effective preventive measures and optimize the allocation of healthcare resources.

Background/aim: Gliomas are highly heterogeneous malignancies originating from diverse cell types within the brain. Although their precise etiology is frequently unknown, risk factors, such as chemical exposure, radiation, and specific uncommon genetic disorders have been identified. Diagnosis typically entails imaging tests, such as magnetic resonance imaging and computed tomography, complemented by a biopsy for confirmation, which may be further validated through genetic testing., Case Report: Next-generation sequencing technology revealed germline co-deletion deletion of cyclin-dependent kinase inhibitor 2 A and B genes (CDKN2A and CDKN2B) in a patient diagnosed with pleomorphic xanthoastrocytoma based on the tumor's molecular characteristics. Following this result, we performed focused genetic analysis with use of multiplex ligation-dependent probe amplification technology for the mother that revealed the same co-deletion. Moreover, due to the father's neuroendocrine pancreatic cancer, application of the NGS technology detected a pathogenic variant in the BRCA1-interacting helicase 1 (BRIP1) gene. Comprehensive multi-gene testing conducted within the familial context, marked by a varied spectrum of cancer type, revealed a constellation of genetic predispositions., Conclusion: This case study underscores the critical importance of molecular testing for tumor characterization and highlights the pivotal role of genetic testing in facilitating early intervention and screening for at-risk family members. Furthermore, the identification of germline co-deletions in cancer lays the foundation for the development of targeted therapeutic strategies aimed at restoring normal cellular regulation and improving patient management., (Copyright © 2024, International Institute of Anticancer Research (Dr. George J. Delinasios), All rights reserved.)

Despite ongoing oncological advances, pancreatic ductal adenocarcinoma (PDAC) continues to have an extremely poor prognosis with limited targeted and immunotherapeutic options. Its genomic background has not been fully characterized yet in large-scale populations all over the world. Methods: Replicating a recent study from China, we collected tissue samples from consecutive Greek patients with pathologically-confirmed metastatic/unresectable PDAC and retrospectively investigated their genomic landscape using next generation sequencing (NGS). Findings: From a cohort of 409 patients, NGS analysis was successfully achieved in 400 cases (56.50% males, median age: 61.8 years). Consistent with a previous study, KRAS was the most frequently mutated gene in 81.50% of tested samples, followed by TP53 (50.75%), CDKN2 (8%), and SMAD4 (7.50%). BRCA1/2 variants with on-label indications were detected in 2%, and 87.50% carried a variant associated with off-label treatment ( KRAS , ERBB2 , STK11 , or HRR-genes), while 3.5% of the alterations had unknown/preliminary-studied actionability ( TP53/CDKN2A ). Most of HRR-alterations were in intermediate- and low-risk genes ( CHEK2 , RAD50 , RAD51 , ATM , FANCA , FANCL , FANCC , BAP1 ), with controversial actionability: 8% harbored a somatic non- BRCA1/2 alteration, 6 cases had a high-risk alteration ( PALB2 , RAD51C ), and one co-presented a PALB2/BRCA2 alteration. Elevated LOH was associated with HRR-mutated status and TP53 mutations while lowered LOH was associated with KRAS alterations. Including TMB/MSI data, the potential benefit from an NGS-oriented treatment was increased from 1.91% to 13.74% (high-MSI: 0.3%, TMB > 10 muts/MB: 12.78%). TMB was slightly increased in females (4.75 vs. 4.46 muts/MB) and in individuals with age > 60 (4.77 vs. 4.40 muts/MB). About 28.41% showed PD-L1 > 1% either in tumor or immune cells, 15.75% expressed PD-L1 ≥ 10%, and only 1.18% had PD-L1 ≥ 50%. This is the largest depiction of real-world genomic characteristics of European patients with PDAC, which offers some useful clinical and research insights.

Background: Hereditary cancer predisposition syndromes are responsible for approximately 5-10% of all diagnosed cancer cases. In order to identify individuals at risk in a cost-efficient manner, family members of individuals carrying pathogenic alterations are tested only for the specific variant that was identified in their carrier relative. The purpose of this study was to investigate the clinical use and implementation of cascade family testing (CFT) in families of breast cancer patients with pathogenic/likely pathogenic variants (PVs/LPVs) in cancer-related predisposition genes., Methods: Germline sequencing was carried out with NGS technology using a 52-gene panel, and cascade testing was performed by Sanger sequencing or MLPA., Results: In a cohort of 1785 breast cancer patients (families), 20.3% were found to have PVs/LPVs. Specifically, 52.2%, 25.1%, and 22.7% of patients had positive findings in high-, intermediate-, and low-penetrance breast cancer susceptibility genes, respectively. Although CFT was recommended to all families, only 117 families (32.3%) agreed to proceed with genetic testing. Among the first-degree relatives who underwent CFT, 70.3% were female, and 108 of 121 (89.3%) were cancer free. Additionally, 42.7%, 36.7%, and 20.6% were offspring, siblings, and parents of the subject, respectively. Our data suggest that CFT was mostly undertaken (104/117, 88.8%) in families with positive findings in high-risk genes., Conclusions: Cascade family testing can be a powerful tool for primary cancer prevention by identifying at-risk family members. It is of utmost importance to implement genetic counseling approaches leading to increased awareness and communication of genetic testing results., Competing Interests: The authors declare no conflicts of interest.

Background/aim: Germline copy number variation (CNV) is a type of genetic variant that predisposes significantly to inherited cancers. Today, next-generation sequencing (NGS) technologies have contributed to multi gene panel analysis in clinical practice., Materials and Methods: A total of 2,163 patients were screened for cancer susceptibility, using a solution-based capture method. A panel of 52 genes was used for targeted NGS. The capture-based approach enables computational analysis of CNVs from NGS data. We studied the performance of the CNV module of the commercial software suite SeqPilot (JSI Medical Systems) and of the non-commercial tool panelcn.MOPS. Additionally, we tested the performance of digital multiplex ligation-dependent probe amplification (digitalMLPA)., Results: Pathogenic/likely pathogenic variants (P/LP) were identified in 464 samples (21.5%). CNV accounts for 10.8% (50/464) of pathogenic variants, referring to deletion/duplication of one or more exons of a gene. In patients with breast and ovarian cancer, CNVs accounted for 10.2% and 6.8% of pathogenic variants, respectively. In colorectal cancer patients, CNV accounted for 28.6% of pathogenic/likely pathogenic variants., Conclusion: In silico CNV detection tools provide a viable and cost-effective method to identify CNVs from NGS experiments. CNVs constitute a substantial percentage of P/LP variants, since they represent up to one of every ten P/LP findings identified by NGS multigene analysis; therefore, their evaluation is highly recommended to improve the diagnostic yield of hereditary cancer analysis., (Copyright © 2023, International Institute of Anticancer Research (Dr. George J. Delinasios), All rights reserved.)

Alternative splicing (AS), a crucial cellular process, is a source of transcriptomic expansion and protein variability. Its contribution to cancer development and progression among a vast repertoire of human diseases, is highlighted lately and is under extensive investigation. In this review, the relative recent aspects of AS as a hallmark of cancer are described. In parallel, the importance of the identification of splicing-related variants through next-generation sequencing technologies is discussed. Cancer therapy and the management of patients and their families can highly benefit by the classification of these variants., (Copyright © 2023, International Institute of Anticancer Research (Dr. George J. Delinasios), All rights reserved.)

Objective: Identify the disease-causing mutation in a patient with features of X-linked hypohidrotic ectodermal dysplasia, which is a genetic disorder characterized by hypodontia, hypohidrosis and hypotrichosis. It is caused by mutations in Ectodysplasin A gene, which encodes ectodysplasin A, a member of the tumor necrosis factor superfamily., Design: Genetic analysis, was performed using chromosomal microarray analysis, whole exome sequencing and multiplex ligation-dependent probe amplification analysis in a 4-year-old boy with hypohidrotic ectodermal dysplasia features. Moreover, the boy's parents were tested for clinically significant findings identified in order to elucidate the pattern of inheritance of the finding detected in the proband., Results: A novel deletion of entire exon 4 in Ectodysplasin A gene identified in the 4-year-old patient. This deletion was found in heterozygous state in the mother of the proband and was not detected in his father. RNA analysis revealed an in-frame deletion r.527_706del, p.(176_236del) in exon 4 of the Ectodysplasin A gene., Conclusion: We identified a novel gross deletion in the Ectodysplasin A gene in a male patient with X-linked hypohidrotic ectodermal dysplasia. Clinical and molecular genetic analysis are crucial to set an accurate diagnosis in patients with hypohidrotic ectodermal dysplasia. These results highlight the importance of the collagen domain of Ectodysplasin A, encoded by exon 4, for its function in vivo., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 Elsevier Ltd. All rights reserved.)

Background/aim: Male breast cancer (MBC) is a very rare disorder affecting approximately 1 in 833 men. Genetic predisposition is one of the most important risk factors of MBC with BRCA2 being the most commonly mutated gene in males diagnosed with breast cancer. However, a large part of MBC heritability is still unexplained. This study sought to add to the data already available on the genetics of MBC., Materials and Methods: Our study initially involved comprehensive analysis of BRCA1 and BRCA2, followed by analysis of 43 genes implicated in cancer predisposition in a series of 100 Greek patients diagnosed with MBC between 1995-2015., Results: Pathogenic variants were identified in 13 patients, with BRCA2 being the most commonly affected gene, followed by BRCA1, RAD50, RAD51B, and MSH3., Conclusion: In agreement with previous reports, BRCA2 is the most important genetic factor of MBC predisposition, while the remaining known cancer predisposition genes are each very rarely involved, rendering conclusions as to their cumulative effect difficult to draw., (Copyright © 2022 International Institute of Anticancer Research (Dr. George J. Delinasios), All rights reserved.)

Next-generation sequencing (NGS) technology is used to evaluate hereditary cancer risks of patients worldwide; however, information concerning the germline multigene mutational spectrum among patients with breast cancer (BC) with consanguineous marriage (CM) is limited. Therefore, this prospective study aimed to determine the molecular characteristics of patients with BC who were tested with multigene hereditary cancer predisposition NGS panel and to show the effect of CM on cancer-related genes. Patients with BC with or without CM and family history (FH) of BC treated in our breast center were selected according to The National Comprehensive Cancer Network (NCCN) criteria for hereditary BC. In these patients, the analysis of a panel of 33 genes involved in hereditary cancer predisposition was performed after genetic counseling by using NGS. The pathogenic variant (PV) and the variant of uncertain significance (VUS) were found to be 15.8 and 47.4%, respectively. PVs were identified in 10/33 genes in 34 patients; 38.2% in BRCA1/2 genes; 6, 24, and 14% in other high, moderate and low-risk genes, respectively. The CM rate was 17.7% among the 215 patients with BC. The PV rate was 13.2% in patients with CM and 16.4% in patients without CM (P=0.80). When PV and VUS were evaluated together, the PV+VUS ratio was significantly higher in patients with CM and FH of BC than patients without CM and FH of BC (88.2 vs. 63.3%, P=0.045). Analysis of multigene panel provided 9.76% additional PVs in moderate/low-risk genes. The PV rate was similar in patients with BC with or without CM. A high PV+VUS ratio in patients with CM and FH of BC suggests that genes whose importance are unknown are likely to be pathogenic genes later., Competing Interests: The authors declare that they have no competing interests., (Copyright: © Ozmen et al.)

Background/aim: The use of multi-gene panels for germline testing in breast cancer enables the estimation of cancer risk and guides risk-reducing management options. The aim of this study was to present data that demonstrate the different levels of actionability for multi-gene panels used in genetic testing of breast cancer patients and their family members., Materials and Methods: We performed an analysis in our clinical database to identify breast cancer patients undergoing genetic testing. We reviewed positive results in respect of risk estimation and management, cascade family testing, secondary findings and information for treatment decision-making., Results: A total of 415 positive test reports were identified with 57.1%, 18.1%, 10.8% and 13.5% of individuals having pathogenic/likely pathogenic variants in high, moderate, low and with insufficient evidence for breast cancer risk genes, respectively. Six point seven percent of individuals were double heterozygotes., Conclusion: Germline findings in 92% of individuals are linked to evidence-based treatment information and risk estimates for predisposition to breast and/or other cancer types. The use of germline findings for treatment decision making expands the indication of genetic testing to include individuals that could benefit from targeted treatments., (Copyright © 2022, International Institute of Anticancer Research (Dr. George J. Delinasios), All rights reserved.)

Objective: Several microRNA (miRNA) polymorphisms have been associated with susceptibility to specific health disorders, including cardiovascular diseases. The aim of the present study was to investigate whether four well-studied miRNA polymorphisms in non-Caucasian populations, namely miR146a G>C (rs2910164), miR149 C>T (rs2292832), miR196a2 C>T (rs11614913) and miR499 A>G (rs3746444), contribute to the risk for the development of premature Coronary Artery Disease (CAD) in the Greek population., Methods: We used a case-control study to examine these associations in 400 individuals: 200 CAD patients [including a subgroup of myocardial infraction (MI) patients] and 200 healthy controls, all of Greek origin. MiRNA polymorphisms were genotyped using three different assays: Polymerase chain reaction - restriction fragment length polymorphism (PCR-RFLP), High resolution Melting (HRM) and Sanger sequencing., Results: Two of these polymorphisms, miR196a2 C>T (rs11614913) and miR499 A>G (rs3746444) were found to be strongly associated with increased risk for CAD (p=0.0388 and p=0.0013, respectively) and for MI (p=0.0281 and p=0.0273, respectively). Furthermore, miR146C-miR149C-miR196T-miR499G allele combination appeared to be significantly related to CAD (p=0.0185) and MI (p=0.0337) prevalence., Conclusions: Our results suggest that at least two of the studied polymorphisms, miR196a2 C>T (rs11614913) and miR499 A>G (rs3746444), as well as the miR146C-miR149C-miR196T-miR499G allele combination could represent useful biomarkers of CAD and/or MI susceptibility in the Greek population. These special genetic characteristics, in combination with environmental factors and personal habits, might contribute to CAD and/or MI prevalence., Competing Interests: Conflict of interest The authors have no conflict of interest to declare., (Copyright © 2020 Hellenic Society of Cardiology. Published by Elsevier B.V. All rights reserved.)

Background: Classification of splicing variants (SVs) in genes associated with hereditary cancer is often challenging. The aim of this study was to investigate the occurrence of SVs in hereditary cancer genes and the clinical utility of RNA analysis., Material and Methods: 1518 individuals were tested for cancer predisposition, using a Next Generation Sequencing (NGS) panel of 36 genes. Splicing variant analysis was performed using RT-PCR and Sanger Sequencing., Results: In total, 34 different SVs were identified, 53% of which were classified as pathogenic or likely pathogenic. The remaining 16 variants were initially classified as Variant of Uncertain Significance (VUS). RNA analysis was performed for 3 novel variants., Conclusion: The RNA analysis assisted in the reclassification of 20% of splicing variants from VUS to pathogenic. RNA analysis is essential in the case of uncharacterized splicing variants, for proper classification and personalized management of these patients., (Copyright© 2021, International Institute of Anticancer Research (Dr. George J. Delinasios), All rights reserved.)

Background: Carriers with pathogenic variants in MSH2 have increased risk to develop colorectal, endometrium, ovarian, and other types of cancer. The PALB2 is associated with breast, ovarian, pancreatic, and prostate cancer. We describe the case of a 42-year-old female diagnosed with endometrial cancer at the age of 42 years with a strong family history of colorectal cancer, which was referred to our private diagnostic laboratory for genetic testing., Methods: In this study, we performed next-generation sequencing (NGS) using an amplicon based 26 genes panel. The presence of multi-exonic copy number variations (CNVs) was investigated by computational analysis and Multiplex Ligation-dependent Probe Amplification (MLPA)., Results: A gross deletion of the genomic region encompassing exons 11-16 of the MSH2 and the loss-of-function variant c.757_758delCT, p.(Leu253Ilefs*3) in the PALB2 were identified in the proband., Conclusions: Multigene analysis using NGS technology allows the identification of pathogenic variants in genes that would normally not be tested based on the patient diagnosis. In our case these results explained not only the personal and/or family history of cancer but also allowed the surveillance for prevention of other cancer types. Moreover, the detection of large genomic rearrangements should be routinely included in hereditary cancer testing., (© 2020 The Authors. Molecular Genetics & Genomic Medicine published by Wiley Periodicals, Inc.)

The majority of all proteins in cells interact with membranes either permanently or temporarily. Peripheral membrane proteins form transient complexes with membrane proteins and/or lipids, via non-covalent interactions and are of outmost importance, due to numerous cellular functions in which they participate. In an effort to collect data regarding this heterogeneous group of proteins we designed and constructed a database, called PerMemDB. PerMemDB is currently the most complete and comprehensive repository of data for eukaryotic peripheral membrane proteins deposited in UniProt or predicted with the use of MBPpred - a computational method that specializes in the detection of proteins that interact non-covalently with membrane lipids, via membrane binding domains. The first version of the database contains 231,770 peripheral membrane proteins from 1009 organisms. All entries have cross-references to other databases, literature references and annotation regarding their interactions with other proteins. Moreover, additional sequence annotation of the characteristic domains that allow these proteins to interact with membranes is available, due to the application of MBPpred. Through the web interface of PerMemDB, users can browse the contents of the database, submit advanced text searches and BLAST queries against the protein sequences deposited in PerMemDB. We expect this repository to serve as a source of information that will allow the scientific community to gain a deeper understanding of the evolution and function of peripheral membrane proteins via the enhancement of proteome-wide analyses. The database is available at: http://bioinformatics.biol.uoa.gr/db=permemdb., (Copyright © 2019 Elsevier B.V. All rights reserved.)

Summary: JUCHMME is an open-source software package designed to fit arbitrary custom Hidden Markov Models (HMMs) with a discrete alphabet of symbols. We incorporate a large collection of standard algorithms for HMMs as well as a number of extensions and evaluate the software on various biological problems. Importantly, the JUCHMME toolkit includes several additional features that allow for easy building and evaluation of custom HMMs, which could be a useful resource for the research community., Availability and Implementation: http://www.compgen.org/tools/juchmme, https://github.com/pbagos/juchmme., Supplementary Information: Supplementary data are available at Bioinformatics online., (© The Author(s) 2019. Published by Oxford University Press. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.)

Background: CHEK2 is involved in the DNA damage repair response Fanconi anemia (FA)-BRCA pathway. An increased risk for breast and other cancers has been documented in individuals who carry a single pathogenic CHEK2 variant. As for other genes involved in cancer predisposition, different types of pathogenic variants have been observed, including single nucleotide variations, short insertions/deletions, large genomic rearrangements and splicing variants. Splicing variants occurring in the splicing acceptor or donor site result in alternative mature mRNA produced and can cause intron retention, exon skipping, or creation of alternative 3' and 5' splice site. Thus, the pathogenicity of this type of alterations should always be explored experimentally and their effect in the mRNA and consequently the protein produced, should be defined. The aim of this study was the delineation of the effect of a splicing variant in the CHEK2 gene., Case Presentation: A healthy 28-year-old woman with a family history of breast and ovarian cancer was referred for genetic testing. The variant c.793-1G > A (rs730881687) was identified by Next Generation Sequencing (NGS) using a solution-based capture method, targeting 33 cancer predisposition genes (SeqCap EZ Probe library, Roche NimbleGen). Experimental analysis in patient-derived leukocytes using RT-PCR of mRNA followed by cDNA sequencing revealed the deletion of one base from the alternative transcript created (r.793del). This resulted in a frameshift leading to premature termination codon within exon 7 (p.(Asp265Thrfs*10))., Conclusions: This finding suggests that the CHEK2 splicing variant c.793-1G > A is a deleterious variant. Our case shows that RNA analysis is a valuable tool for uncharacterized splice site variants in individuals referred for testing and facilitates their personalized management.

Background: 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., Methods: 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)., Results: 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., Conclusions: 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.

Germline mutations in the BRCA1 and BRCA2 genes are associated with hereditary predisposition to breast and ovarian cancer. Sensitive and accurate detection of BRCA1 and BRCA2 mutations is crucial for personalized clinical management of individuals affected by breast or ovarian cancer, and for the identification of at-risk healthy relatives. We performed molecular analysis of the BRCA1 and BRCA2 genes in 898 Greek families, using Sanger sequencing or Next Generation Sequencing for the detection of small insertion/deletion frameshift, nonsynonymous, truncating and splice-site alterations and MLPA for the detection of large genomic rearrangements. In total, a pathogenic mutation was identified in 12.9% of 898 families analyzed. Of the 116 mutations identified in total 9% were novel and 14.7% were large genomic rearrangements. Our results indicate that different types of mutational events in the BRCA1 and BRCA2 genes are responsible for the hereditary component of breast/ovarian cancer in the Greek population. Therefore the methodology used in the analysis of Greek patients must be able to detect both point and small frameshift mutations in addition to large genomic rearrangements across the entire coding region of the two genes., (Copyright © 2017 Elsevier Inc. All rights reserved.)

Alpha helical transmembrane (TM) proteins constitute an important structural class of membrane proteins involved in a wide variety of cellular functions. The prediction of their transmembrane topology, as well as their discrimination in newly sequenced genomes, is of great importance for the elucidation of their structure and function. Several methods have been applied for the prediction of the transmembrane segments and the topology of alpha helical transmembrane proteins utilizing different algorithmic techniques. Hidden Markov Models (HMMs) have been efficiently used in the development of several computational methods used for this task. In this chapter we give a brief review of different available prediction methods for alpha helical transmembrane proteins pointing out sequence and structural features that should be incorporated in a prediction method. We then describe the procedure of the design and development of a Hidden Markov Model capable of predicting the transmembrane alpha helices in proteins and discriminating them from globular proteins.

Transmembrane beta-barrels (TMBBs) constitute an important structural class of membrane proteins located in the outer membrane of gram-negative bacteria, and in the outer membrane of chloroplasts and mitochondria. They are involved in a wide variety of cellular functions and the prediction of their transmembrane topology, as well as their discrimination in newly sequenced genomes is of great importance as they are promising targets for antimicrobial drugs and vaccines. Several methods have been applied for the prediction of the transmembrane segments and the topology of beta barrel transmembrane proteins utilizing different algorithmic techniques. Hidden Markov Models (HMMs) have been efficiently used in the development of several computational methods used for this task. In this chapter we give a brief review of different available prediction methods for beta barrel transmembrane proteins pointing out sequence and structural features that should be incorporated in a prediction method. We then describe the procedure of the design and development of a Hidden Markov Model capable of predicting the transmembrane beta strands of TMBBs and discriminating them from globular proteins.

A large number of modular domains that exhibit specific lipid binding properties are present in many membrane proteins involved in trafficking and signal transduction. These domains are present in either eukaryotic peripheral membrane or transmembrane proteins and are responsible for the non-covalent interactions of these proteins with membrane lipids. Here we report a profile Hidden Markov Model based method capable of detecting Membrane Binding Proteins (MBPs) from information encoded in their amino acid sequence, called MBPpred. The method identifies MBPs that contain one or more of the Membrane Binding Domains (MBDs) that have been described to date, and further classifies these proteins based on their position in respect to the membrane, either as peripheral or transmembrane. MBPpred is available online at http://bioinformatics.biol.uoa.gr/MBPpred. This method was applied in selected eukaryotic proteomes, in order to examine the characteristics they exhibit in various eukaryotic kingdoms and phyla., (Copyright © 2016 Elsevier B.V. All rights reserved.)

During the last two decades a large number of computational methods have been developed for predicting transmembrane protein topology. Current predictors rely on topogenic signals in the protein sequence, such as the distribution of positively charged residues in extra-membrane loops and the existence of N-terminal signals. However, phosphorylation and glycosylation are post-translational modifications (PTMs) that occur in a compartment-specific manner and therefore the presence of a phosphorylation or glycosylation site in a transmembrane protein provides topological information. We examine the combination of phosphorylation and glycosylation site prediction with transmembrane protein topology prediction. We report the development of a Hidden Markov Model based method, capable of predicting the topology of transmembrane proteins and the existence of kinase specific phosphorylation and N/O-linked glycosylation sites along the protein sequence. Our method integrates a novel feature in transmembrane protein topology prediction, which results in improved performance for topology prediction and reliable prediction of phosphorylation and glycosylation sites. The method is freely available at http://bioinformatics.biol.uoa.gr/HMMpTM., (Copyright © 2013 Elsevier B.V. All rights reserved.)

A major part of membrane function is conducted by proteins, both integral and peripheral. Peripheral membrane proteins temporarily adhere to biological membranes, either to the lipid bilayer or to integral membrane proteins with noncovalent interactions. The aim of this study was to construct and analyze the interactions of the human plasma membrane peripheral proteins (peripherome hereinafter). For this purpose, we collected a dataset of peripheral proteins of the human plasma membrane. We also collected a dataset of experimentally verified interactions for these proteins. The interaction network created from this dataset has been visualized using Cytoscape. We grouped the proteins based on their subcellular location and clustered them using the MCL algorithm in order to detect functional modules. Moreover, functional and graph theory based analyses have been performed to assess biological features of the network. Interaction data with drug molecules show that ~10% of peripheral membrane proteins are targets for approved drugs, suggesting their potential implications in disease. In conclusion, we reveal novel features and properties regarding the protein-protein interaction network created by peripheral proteins of the human plasma membrane.

Summary: Molecular recognition features (MoRFs) are small, intrinsically disordered regions in proteins that undergo a disorder-to-order transition on binding to their partners. MoRFs are involved in protein-protein interactions and may function as the initial step in molecular recognition. The aim of this work was to collect, organize and store all membrane proteins that contain MoRFs. Membrane proteins constitute ∼30% of fully sequenced proteomes and are responsible for a wide variety of cellular functions. MoRFs were classified according to their secondary structure, after interacting with their partners. We identified MoRFs in transmembrane and peripheral membrane proteins. The position of transmembrane protein MoRFs was determined in relation to a protein's topology. All information was stored in a publicly available mySQL database with a user-friendly web interface. A Jmol applet is integrated for visualization of the structures. mpMoRFsDB provides valuable information related to disorder-based protein-protein interactions in membrane proteins., Availability: http://bioinformatics.biol.uoa.gr/mpMoRFsDB

Molecular Recognition Features (MoRFs) are defined as short, intrinsically disordered regions in proteins that undergo disorder-to-order transition upon binding to their partners. As their name suggests, they are implicated in molecular recognition, which serves as the initial step for protein-protein interactions. Membrane proteins constitute approximately 30% of fully sequenced proteomes and are responsible for a wide variety of cellular functions. The aim of the current study was to identify and analyze MoRFs in membrane proteins. Two datasets of MoRFs, transmembrane and peripheral membrane protein MoRFs, were constructed from the Protein Data Bank, and sequence, structural and functional analysis was performed. Characterization of our datasets revealed their unique compositional biases and membrane protein MoRFs were categorized depending on their secondary structure after the interaction with their partners. Moreover, the position of transmembrane protein MoRFs in relation with the protein's topology was determined. Further studies were focused on functional analyses of MoRF-containing proteins and MoRFs' partners, associating them with protein binding, regulation and cell signaling, indicating half of them as putative hubs in protein-protein interaction networks. In conclusion, we provide insights into the disorder-based protein-protein interactions involving membrane proteins., (Copyright © 2013 Elsevier B.V. All rights reserved.)

Unlabelled: 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.

It has been shown that the progress in the determination of membrane protein structure grows exponentially, with approximately the same growth rate as that of the water-soluble proteins. In order to investigate the effect of this, on the performance of prediction algorithms for both alpha-helical and beta-barrel membrane proteins, we conducted a prospective study based on historical records. We trained separate hidden Markov models with different sized training sets and evaluated their performance on topology prediction for the two classes of transmembrane proteins. We show that the existing top-scoring algorithms for predicting the transmembrane segments of alpha-helical membrane proteins perform slightly better than that of beta-barrel outer membrane proteins in all measures of accuracy. With the same rationale, a meta-analysis of the performance of the secondary structure prediction algorithms indicates that existing algorithmic techniques cannot be further improved by just adding more non-homologous sequences to the training sets. The upper limit for secondary structure prediction is estimated to be no more than 70% and 80% of correctly predicted residues for single sequence based methods and multiple sequence based ones, respectively. Therefore, we should concentrate our efforts on utilizing new techniques for the development of even better scoring predictors.