Numerous genes are known to cause dilated cardiomyopathy (DCM). However, until now technological limitations have hindered elucidation of the contribution of all clinically relevant disease genes to DCM phenotypes in larger cohorts. We now utilized next-generation sequencing to overcome these limitations and screened all DCM disease genes in a large cohort. In this multi-centre, multi-national study, we have enrolled 639 patients with sporadic or familial DCM. To all samples, we applied a standardized protocol for ultra-high coverage next-generation sequencing of 84 genes, leading to 99.1% coverage of the target region with at least 50-fold and a mean read depth of 2415. In this well characterized cohort, we find the highest number of known cardiomyopathy mutations in plakophilin-2, myosin-binding protein C-3, and desmoplakin. When we include yet unknown but predicted disease variants, we find titin, plakophilin-2, myosin-binding protein-C 3, desmoplakin, ryanodine receptor 2, desmocollin-2, desmoglein-2, and SCN5A variants among the most commonly mutated genes. The overlap between DCM, hypertrophic cardiomyopathy (HCM), and channelopathy causing mutations is considerably high. Of note, we find that >38% of patients have compound or combined mutations and 12.8% have three or even more mutations. When comparing patients recruited in the eight participating European countries we find remarkably little differences in mutation frequencies and affected genes. This is to our knowledge, the first study that comprehensively investigated the genetics of DCM in a large-scale cohort and across a broad gene panel of the known DCM genes. Our results underline the high analytical quality and feasibility of Next-Generation Sequencing in clinical genetic diagnostics and provide a sound database of the genetic causes of DCM.

One of the areas where Artificial Intelligence is having more impact is machine learning, which develops algorithms able to learn patterns and decision rules from data. Machine learning algorithms have been embedded into data mining pipelines, which can combine them with classical statistical strategies, to extract knowledge from data. Within the EU-funded MOSAIC project, a data mining pipeline has been used to derive a set of predictive models of type 2 diabetes mellitus (T2DM) complications based on electronic health record data of nearly one thousand patients. Such pipeline comprises clinical center profiling, predictive model targeting, predictive model construction and model validation. After having dealt with missing data by means of random forest (RF) and having applied suitable strategies to handle class imbalance, we have used Logistic Regression with stepwise feature selection to predict the onset of retinopathy, neuropathy, or nephropathy, at different time scenarios, at 3, 5, and 7 years from the first visit at the Hospital Center for Diabetes (not from the diagnosis). Considered variables are gender, age, time from diagnosis, body mass index (BMI), glycated hemoglobin (HbA1c), hypertension, and smoking habit. Final models, tailored in accordance with the complications, provided an accuracy up to 0.838. Different variables were selected for each complication and time scenario, leading to specialized models easy to translate to the clinical practice.

In recent years, high-throughput sequencing technologies provide unprecedented opportunity to depict cancer samples at multiple molecular levels. The integration and analysis of these multi-omics datasets is a crucial and critical step to gain actionable knowledge in a precision medicine framework. This paper explores recent data-driven methodologies that have been developed and applied to respond major challenges of stratified medicine in oncology, including patients' phenotyping, biomarker discovery, and drug repurposing. We systematically retrieved peer-reviewed journals published from 2014 to 2019, select and thoroughly describe the tools presenting the most promising innovations regarding the integration of heterogeneous data, the machine learning methodologies that successfully tackled the complexity of multi-omics data, and the frameworks to deliver actionable results for clinical practice. The review is organized according to the applied methods: Deep learning, Network-based methods, Clustering, Features Extraction, and Transformation, Factorization. We provide an overview of the tools available in each methodological group and underline the relationship among the different categories. Our analysis revealed how multi-omics datasets could be exploited to drive precision oncology, but also current limitations in the development of multi-omics data integration.

A number of potential candidate genes in a variety of biological pathways have been associated with longevity in model organisms. Many of these genes have human homologs and thus have the potential to provide insights into human longevity. Recently, several studies suggested that FOXO3A functions as a key bridge for various signaling pathways that influence aging and longevity. Interestingly, Willcox and colleagues identified several variants that displayed significant genotype–gender interaction in male human longevity. In particular, a nested case–control study was performed in an ethnic Japanese population in Hawaii, and five candidate longevity genes were chosen based on links to the insulin–insulin-like growth factor-1 (IGF-1) signaling pathway. In the Willcox study, the investigated genetic variations (rs2802292, rs2764264, and rs13217795) within the FOXO3A gene were significantly associated with longevity in male centenarians. We validated the association of FOXO3A polymorphisms with extreme longevity in males from the Southern Italian Centenarian Study. Particularly, rs2802288, a proxy of rs2802292, showed the best allelic association—minor allele frequency (MAF) = 0.49; p = 0.003; odds ratio (OR) = 1.51; 95% confidence interval (CI), 1.15–1.98). Furthermore, we undertook a meta-analysis to explore the significance of rs2802292 association with longevity by combining the association results of the current study and the findings coming from the Willcox et al. investigation. Our data point to a key role of FOXO3A in human longevity and confirm the feasibility of the identification of such genes with centenarian–controls studies. Moreover, we hypothesize the susceptibility to the longevity phenotype may well be the result of complex interactions involving genes and environmental factors but also gender.