Benchmarking and improving the performance of variant-calling pipelines with RecallME.

MOTIVATION: The steady increment of Whole Genome/Exome sequencing and the development of novel Next Generation Sequencing-based gene panels requires continuous testing and validation of variant calling (VC) pipelines and the detection of sequencing-related issues to be maintained up-to-date and feasible for the clinical settings. State of the art tools are reliable when used to compute standard performance metrics. However, the need for an automated software to discriminate between bioinformatic and sequencing issues and to optimize VC parameters remains unmet. RESULTS: The aim of the current work is to present RecallME, a bioinformatic suite that tracks down difficult-to-detect variants as insertions and deletions in highly repetitive regions, thus providing the maximum reachable recall for both single nucleotide variants and small insertion and deletions and to precisely guide the user in the pipeline optimization process. AVAILABILITY AND IMPLEMENTATION: Source code is freely available under MIT license at https://github.com/mazzalab-ieo/recallme. RecallME web application is available at https://translational-oncology-lab.shinyapps.io/recallme/. To use RecallME, users must obtain a license for ANNOVAR by themselves.

Interpretation and actionability of genetic variants in cardiomyopathies: a position statement from the European Society of Cardiology Council on cardiovascular genomics.

This document describes the contribution of clinical criteria to the interpretation of genetic variants using heritable Mendelian cardiomyopathies as an example. The aim is to assist cardiologists in defining the clinical contribution to a genetic diagnosis and the interpretation of molecular genetic reports. The identification of a genetic variant of unknown or uncertain significance is a limitation of genetic testing, but current guidelines for the interpretation of genetic variants include essential contributions from clinical family screening that can establish a de novo assignment of the variant or its segregation with the phenotype in the family. A partnership between clinicians and patients helps to solve major uncertainties and provides reliable and clinically actionable information.

Machine learning-based reclassification of germline variants of unknown significance: The RENOVO algorithm.

The increasing scope of genetic testing allowed by next-generation sequencing (NGS) dramatically increased the number of genetic variants to be interpreted as pathogenic or benign for adequate patient management. Still, the interpretation process often fails to deliver a clear classification, resulting in either variants of unknown significance (VUSs) or variants with conflicting interpretation of pathogenicity (CIP); these represent a major clinical problem because they do not provide useful information for decision-making, causing a large fraction of genetically determined disease to remain undertreated. We developed a machine learning (random forest)-based tool, RENOVO, that classifies variants as pathogenic or benign on the basis of publicly available information and provides a pathogenicity likelihood score (PLS). Using the same feature classes recommended by guidelines, we trained RENOVO on established pathogenic/benign variants in ClinVar (training set accuracy = 99%) and tested its performance on variants whose interpretation has changed over time (test set accuracy = 95%). We further validated the algorithm on additional datasets including unreported variants validated either through expert consensus (ENIGMA) or laboratory-based functional techniques (on BRCA1/2 and SCN5A). On all datasets, RENOVO outperformed existing automated interpretation tools. On the basis of the above validation metrics, we assigned a defined PLS to all existing ClinVar VUSs, proposing a reclassification for 67% with >90% estimated precision. RENOVO provides a validated tool to reduce the fraction of uninterpreted or misinterpreted variants, tackling an area of unmet need in modern clinical genetics.

A Multidimensional Approach of Surgical Mortality Assessment and Stratification (Smatt Score).

Surgical mortality is the most significant measure of outcome in surgical healthcare. The objective was to assess surgical 30 days mortality and improve the identification of predictors for personalized risk stratification of patients undergoing elective and emergency surgery. The study was conducted as a single-center cohort retrospective observational study, based on the analysis of data collected from patients surgically treated from 2002 to 2014 in a multi-disciplinary research and care referral hospital with global case mix of 1.27. The overall in-hospital mortality rate was 1.89% (95% CI 1.82-1.95). In the univariable analysis, numerous predictors were significantly associated with in-hospital death following surgery. In the multivariable model, age, BMI (Body Mass Index), ASA score, department, planned surgical complexity, surgical priority, previous surgeries in the same hospitalization, cardiovascular, pulmonary, hepato-renal comorbidities, drug intolerance, cancer and AIDS were independently associated with mortality after surgery. At logistic regression, the computed SMATT score (graded 0-100), generated on the basis of multivariate analysis, demonstrated a good discrimination (10-fold cross-validated AUC-ROC 0.945, 95%CI 0.941-0.948) and correctly classified 98.5% of those admissions with a probability of death >50%. The novel SMATT score, based on individual preoperative and surgical factors, accurately predicts mortality and provides dynamic information of the risk in redo/reoperative surgery.

Pathologic substrate of gastropathy in Anderson-Fabry disease.

In both classic and late-onset AFD, mutations of the GLA gene cause deficient activity of the alpha-galactosidase enzyme resulting in intracellular accumulation of the undigested substrate. Gastrointestinal symptoms (GI) are common but non-specific and imputed to the AFD, irrespective of the demonstration of substrate accumulation in GI cells. We demonstrate substrate accumulation in gastric epithelial, vascular, and nerve cells of patients with classic AFD and, vice versa, absence of accumulation in late-onset AFD and controls.

Correction: Age-specific reference values for carotid arterial stiffness estimated by ultrasonic wall tracking.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

Myths to debunk: the non-compacted myocardium.

Left ventricular non-compaction (LVNC) is defined by the triad: prominent trabecular anatomy, thin compacted layer, and deep inter-trabecular recesses. No person, sick or healthy, demonstrates identical anatomy of the trabeculae; their configuration represents a sort of individual dynamic 'cardiac fingerprinting'. LVNC can be observed in healthy subjects with normal left ventricular (LV) size and function, in athletes, in pregnant women, as well as in patients with haematological disorders, neuromuscular diseases, and chronic renal failure; it can be acquired and potentially reversible. When LVNC is observed in patients with dilated cardiomyopathy (DCM), hypertrophic cardiomyopathy, restrictive cardiomyopathy, or arrhythmogenic cardiomyopathy, the risk exists of misnaming the cardiomyopathy as 'LVNC cardiomyopathy' rather than properly describe, i.e. a 'DCM associated with LVNC'. In rare infantile CMPs (the paradigm is tafazzinopathy or Barth syndrome), the non-compaction (NC) is intrinsically part of the cardiac phenotype. The LVNC is also common in congenital heart disease (CHD) as well as in chromosomal disorders with systemic manifestations. The high prevalence of LVNC in healthy athletes, its possible reversibility or regression, and the increasing detection in healthy subjects suggest a cautious use of the term 'LVNC cardiomyopathy', which describes the morphology, but not the functional profile of the cardiac disease. Genetic testing, when positive, usually reflects the genetic causes of an underlying cardiomyopathy rather than that of the NC, which often does not segregate with CMP phenotype in families. Therefore, when associated with LV dilation and dysfunction, hypertrophy, or CHD, the leading diagnosis is cardiomyopathy or CHD followed by the descriptor LVNC.

Age-specific reference values for carotid arterial stiffness estimated by ultrasonic wall tracking.

Interaction between arterial stiffness and hypertension plays an important role in the development of cardiovascular disease. Accordingly, assessment of arterial stiffness may provide a tool for estimating cardiovascular risk and monitoring therapy in hypertensive patients. Radiofrequency-based vascular ultrasound allows accurate noninvasive assessment of local mechanical properties of large arteries, but for its use in clinical practice, reference values according to age and sex are mandatory for each vascular site. To provide reference values for common carotid artery stiffness as assessed by an echo-tracking imaging system Hitachi-Aloka, we pooled measurements collected in 1847 healthy subjects aged 3-74 years (1008 males and 839 females) recruited in 14 European centers in the E-tracking International Collaboration (ETIC). Statistical models were developed to describe relationships of different stiffness indices with age and to calculate median values and Z-scores corresponding to ± 1 and ± 2 standard deviations. In our apparently healthy population, age accounted for 53% of variability in the elastic modulus (epsilon), 39% in arterial compliance, 47% in stiffness index (ß), and 56% in local pulse wave velocity; on average, blood pressure accounted for a further 7.5% of variability. Dependence on age was not linear; changes in mean values increased at older ages, especially for epsilon and ß. There was an interaction between age and gender for arterial compliance, which was higher in males. We present nomograms and a software that can be used for the automated calculation of Z-scores for local carotid stiffness in individual patients. These tools can be used to establish prognostic indicators or surrogate targets for treatment monitoring.

Cardiac Phenotypes in Hereditary Muscle Disorders: JACC State-of-the-Art Review.

Hereditary muscular diseases commonly involve the heart. Cardiac manifestations encompass a spectrum of phenotypes, including both cardiomyopathies and rhythm disorders. Common biomarkers suggesting cardiomuscular diseases include increased circulating creatine kinase and/or lactic acid levels or disease-specific metabolic indicators. Cardiac and extra-cardiac traits, imaging tests, family studies, and genetic testing provide precise diagnoses. Cardiac phenotypes are mainly dilated and hypokinetic in dystrophinopathies, Emery-Dreifuss muscular dystrophies, and limb girdle muscular dystrophies; hypertrophic in Friedreich ataxia, mitochondrial diseases, glycogen storage diseases, and fatty acid oxidation disorders; and restrictive in myofibrillar myopathies. Left ventricular noncompaction is variably associated with the different myopathies. Conduction defects and arrhythmias constitute a major phenotype in myotonic dystrophies and skeletal muscle channelopathies. Although the actual cardiac management is rarely based on the cause, the cardiac phenotypes need precise characterization because they are often the only or the predominant manifestations and the prognostic determinants of many hereditary muscle disorders.

Common presentation of rare diseases: Aortic aneurysms & valves.

The concept "common presentation of rare diseases" implies that rare diseases are masked by common phenotypic manifestations. This concept applies to both aneurysmal and valvular diseases that can be syndromic and non-syndromic. Syndromic disorders include genetic connective tissue diseases and chromosomal disorders that are diagnosed independently from the aneurysm or valve disease. Non-syndromic diseases, on the other hand, are defined by the presence of aneurysm or valve disease or both. The reasons for suspecting these rare diseases include young age, the absence of risk factors, a positive family history for aortic or valvular disease/event, and extra-cardiovascular traits for syndromes. The probands should receive genetic counseling, genetic testing [single gene in case of precise phenotyping addressing the gene to be tested, or multigene panels, in case of diseases with genetic heterogeneity], post-test counseling, clinical family screening and cascade genetic testing in relatives after the identification of a causative mutation. Segregation studies are essential in case of novel mutations, in particular non-truncation predicting variants. Clinical family screening of syndromic diseases is facilitated by the evaluation of non-cardiovascular traits; this supports early diagnosis and geno-phenotype correlation. Vice versa, family screening studies in non-syndromic aneurysmal and valvular diseases exclusively relies on CV imaging screening of relatives. In this context, conditions such as BAV and related aortopathy are easy to diagnose because BAV is present at birth while aortopathy usually develops during the life course.

International External Validation Study of the 2014 European Society of Cardiology Guidelines on Sudden Cardiac Death Prevention in Hypertrophic Cardiomyopathy (EVIDENCE-HCM).

BACKGROUND: Identification of people with hypertrophic cardiomyopathy (HCM) who are at risk of sudden cardiac death (SCD) and require a prophylactic implantable cardioverter defibrillator is challenging. In 2014, the European Society of Cardiology proposed a new risk stratification method based on a risk prediction model (HCM Risk-SCD) that estimates the 5-year risk of SCD. The aim was to externally validate the 2014 European Society of Cardiology recommendations in a geographically diverse cohort of patients recruited from the United States, Europe, the Middle East, and Asia. METHODS: This was an observational, retrospective, longitudinal cohort study. RESULTS: The cohort consisted of 3703 patients. Seventy three (2%) patients reached the SCD end point within 5 years of follow-up (5-year incidence, 2.4% [95% confidence interval {CI}, 1.9-3.0]). The validation study revealed a calibration slope of 1.02 (95% CI, 0.93-1.12), C-index of 0.70 (95% CI, 0.68-0.72), and D-statistic of 1.17 (95% CI, 1.05-1.29). In a complete case analysis (n= 2147; 44 SCD end points at 5 years), patients with a predicted 5-year risk of <4% (n=1524; 71%) had an observed 5-year SCD incidence of 1.4% (95% CI, 0.8-2.2); patients with a predicted risk of ≥6% (n=297; 14%) had an observed SCD incidence of 8.9% (95% CI, 5.96-13.1) at 5 years. For every 13 (297/23) implantable cardioverter defibrillator implantations in patients with an estimated 5-year SCD risk ≥6%, 1 patient can potentially be saved from SCD. CONCLUSIONS: This study confirms that the HCM Risk-SCD model provides accurate prognostic information that can be used to target implantable cardioverter defibrillator therapy in patients at the highest risk of SCD.