Inherited cardiovascular diseases are highly heterogeneous conditions with multiple genetic loci involved. The application of advanced molecular tools, such as Next Generation Sequencing, has facilitated the genetic analysis of these disorders. Accurate analysis and variant identification are required to maximize the quality of the sequencing data. Therefore, the application of NGS for clinical purposes should be limited to laboratories with a high level of technological expertise and resources. In addition, appropriate gene selection and variant interpretation can result in the highest possible diagnostic yield. Implementation of genetics in cardiology is imperative for the accurate diagnosis, prognosis and management of several inherited disorders and could eventually lead to the realization of precision medicine in this field. However, genetic testing should also be accompanied by an appropriate genetic counseling procedure that clarifies the significance of the genetic analysis results for the proband and his family. In this regard, a multidisciplinary collaboration among physicians, geneticists, and bioinformaticians is imperative. In the present review, we address the current state of knowledge regarding genetic analysis strategies employed in the field of cardiogenetics. Variant interpretation and reporting guidelines are explored. Additionally, gene selection procedures are accessed, with a particular emphasis on information concerning gene-disease associations collected from international alliances such as the Gene Curation Coalition (GenCC). In this context, a novel approach to gene categorization is proposed. Moreover, a sub-analysis is conducted on the 1,502,769 variation records with submitted interpretations in the Clinical Variation (ClinVar) database, focusing on cardiology-related genes. Finally, the most recent information on genetic analysis's clinical utility is reviewed.

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.

Implementation of next-generation sequencing (NGS) for the genetic analysis of hereditary diseases has resulted in a vast number of genetic variants identified daily, leading to inadequate variant interpretation and, consequently, a lack of useful clinical information for treatment decisions. Herein, we present MARGINAL 1.0.0, a machine learning (ML)-based software for the interpretation of rare BRCA1 and BRCA2 germline variants. MARGINAL software classifies variants into three categories, namely, (likely) pathogenic, of uncertain significance and (likely) benign, implementing the criteria established by the American College of Medical Genetics and Genomics and the Association for Molecular Pathology (ACMG-AMP). We first annotated BRCA1 and BRCA2 variants using various sources. Then, we automatically implemented the ACMG-AMP criteria, and we finally constructed the ML model for variant classification. To maximize accuracy, we compared the performance of eight different ML algorithms in a classification scheme based on a serial combination of two classifiers. The model showed high predictive abilities with maximum accuracy of 92% and 98%, recall of 92% and 98% and specificity of 90% and 98% for the first and second classifiers, respectively. Our results indicate that using a gene and disease-specific ML automated software for clinical variant evaluation can minimize conflicting interpretations.

In the era of precision oncology, comprehensive molecular profiling is critical for guiding targeted and immunotherapy strategies. This study presents the analytical and clinical validation of a 1021-gene next-generation sequencing (NGS) panel, designed for use with both formalin-fixed paraffin-embedded (FFPE) tissue- and liquid-biopsy specimens. Analytical validation confirmed the assay’s high sensitivity and specificity across variant types—including SNVs (Single Nucleotide Variations), indels, CNVs (Copy Number Variations), and fusions—down to a 0.5% variant allele frequency. The assay also accurately identified microsatellite instability (MSI) and tumor mutational burden (TMB), essential biomarkers for immunotherapy. Clinical validation was performed on over 1300 solid tumor samples from diverse histologies, revealing actionable alterations in over 50% of cases. The panel detected on-label treatment biomarkers in 12.57% of patients, increasing to 20.15% when immunotherapy markers were included. Additionally, the assay demonstrated strong concordance with orthogonal methods and was effective in detecting variants in plasma-derived circulating tumor DNA in 70% of evaluable cases. These findings support the robust performance and broad clinical applicability of the 1021-gene panel for comprehensive genomic profiling in both tissue and liquid biopsies, offering a valuable tool for personalized cancer treatment.

Early-onset breast cancer constitutes a major criterion for genetic testing referral. Nevertheless, studies focusing on breast cancer patients (≤30 years) are limited. We investigated the contribution and spectrum of known breast-cancer-associated genes in 267 Greek women with breast cancer ≤30 years while monitoring their clinicopathological characteristics and outcomes. In this cohort, a significant proportion (39.7%) carried germline pathogenic variants (PVs) distributed in 8 genes. The majority, namely 36.7%, involved BRCA1, TP53, and BRCA2. PVs in BRCA1 were the most prevalent (28.1%), followed by TP53 (4.5%) and BRCA2 (4.1%) PVs. The contribution of PVs in CHEK2, ATM, PALB2, PTEN, and RAD51C was limited to 3%. In the patient group ≤26 years, TP53 PVs were significantly higher compared to the group 26–30 years (p = 0.0023). A total of 74.8% of TP53 carriers did not report a family history of cancer. Carriers of PVs receiving neoadjuvant chemotherapy showed an improved event-free survival (p < 0.0001) compared to non-carriers. Overall, many women with early-onset breast cancer carry clinically actionable variants, mainly in the BRCA1/2 and TP53 genes. The inclusion of timely testing of TP53 in these patients provides essential information for appropriate clinical management. This is important for countries where reimbursement involves the cost of genetic analysis of BRCA1/2 only.

Gliomas are the most common malignant primary brain tumors characterized by poor prognosis. The genotyping of tumors using next generation sequencing (NGS) platforms enables the identification of genetic alterations that constitute diagnostic, prognostic and predictive biomarkers. The present study investigated the molecular profile of 32 tumor samples from 32 patients with high‑grade gliomas by implementing a broad 80‑gene targeted NGS panel while reporting their clinicopathological characteristics and outcomes. Subsequently, 14 of 32 tumor specimens were also genotyped using a 55‑gene NGS panel to validate the diagnostic accuracy and clinical utility of the extended panel. The median follow‑up was 19.2 months. In total, 129 genetic alterations including 33 structural variants were identified in 38 distinct genes. Among 96 variants (single nucleotide variants and insertions and deletions), 38 were pathogenic and 58 variants of unknown clinical significance. TP53 was the most frequently mutated gene, followed by PTEN and IDH1 genes. Glioma patients with IDH1 mutant tumors were younger and had significantly longer overall survival compared to patients with wild‑type IDH1 tumors. Similarly, tumors with TP53 mutations were more likely observed in younger patients with glioma. Subsequently, a comparison of mutational profiles of samples analyzed by both panels was also performed. Implementation of the comprehensive pan‑cancer and the MOL panels resulted in the identification of 37 and 15 variants, respectively. Of those, 13 were common. Comprehensive pan‑cancer panel identified 24 additional variants, 22 of which were located in regions that were not targeted by the MOL panel. By contrast, the MOL panel identified two additional variants. Overall, the present study demonstrated that using an extended tumor profile assay instead of a glioma‑specific tumor profile panel identified additional genetic changes that may be taken into consideration as potential therapeutic targets for glioma diagnosis and molecular classification.