745-4 Sudden “Arrhythmic” Death in Young People with Apparently Normal Heart

The aim of the present study was to assess whether and how often the ultimate diagnosis of structural heart disease underlying sudden cardiac arrest depends on histologic examination of the myocardium and specialized conduction system. Among 186 cases of consecutive sudden cardiovascular death in young people (≤35 yrs) studied from 1979 to 1994, in 59 (32%) gross examination failed to show any cardiac cause of sudden death such as obstructive coronary atherosclerosis, congenital coronary anomaly, cardiomyopathy, valve disease and aortic dissection. The grossly normal heart group consisted of 40 males and 19 females, aged 4–35 yrs (mean 23.7); 27 patients experienced warning symptoms and signs consisting of syncope in 12, ECG abnormalities in 16, and arrhythmias in 11. None had been diagnosed while alive. Detailed histologic study, including examination of ordinary ventricular myocardium as well as serial sections of specialized conduction system, disclosed: 1)focal myocarditis in 17 patients; 2) conduction system abnormalities leading to heart block in 6 patients (sick sinus syndrome in 1, lipomatous discontinuity between the atrial myocardium and the atrioventricular node in 2, sclerotic interruption of His and bundle branches in 2, and longitudinal dissociation of the His bundle in 1). and to ventricular pre-excitation in 15 (atrioventricular by-pass fibers in 9, nodoventricular Mahaim fibers in 4, atriofascicular tract in 1, AV nodal hypoplasia in 1); 3) focal fibrousfatty replacement of the right anterior wall and infundibulum in 5 patients. Sudden death remained unexplained in 16 cases. In conclusion, gross heart features were normal in nearly one third of the young sudden cardiovascular death victims; in 73% of them, however, there was histopathologic evidence of concealed “arrhythmogenic” substrates mostly consisting of conduction system pathology and focal myocarditis

Novel perspectives in redox biology and pathophysiology of failing myocytes: modulation of the intramyocardial redox milieu for therapeutic interventions - A review article from the Working Group of Cardiac Cell Biology, Italian Society of Cardiology

The prevalence of heart failure (HF) is still increasing worldwide, with enormous human, social, and economic costs, in spite of huge efforts in understanding pathogeneticmechanisms and in developing effective therapies that have transformed this syndrome into a chronic disease. Myocardial redox imbalance is a hallmark of this syndrome, since excessive reactive oxygen and nitrogen species can behave as signaling molecules in the pathogenesis of hypertrophy and heart failure, leading to dysregulation of cellular calcium handling, of the contractile machinery, of myocardial energetics and metabolism, and of extracellular matrix deposition. Recently, following new interesting advances in understanding myocardial ROS and RNS signaling pathways, new promising therapeutical approaches with antioxidant properties are being developed, keeping in mind that scavenging ROS and RNS tout court is detrimental as well, since these molecules also play a role in physiological myocardial homeostasis

Cancer Markers Selection Using Network-Based Cox Regression: A Methodological and Computational Practice.

International initiatives such as the Cancer Genome Atlas (TCGA) and the International Cancer Genome Consortium (ICGC) are collecting multiple datasets at different genome-scales with the aim of identifying novel cancer biomarkers and predicting survival of patients. To analyze such data, several statistical methods have been applied, among them Cox regression models. Although these models provide a good statistical framework to analyze omic data, there is still a lack of studies that illustrate advantages and drawbacks in integrating biological information and selecting groups of biomarkers. In fact, classical Cox regression algorithms focus on the selection of a single biomarker, without taking into account the strong correlation between genes. Even though network-based Cox regression algorithms overcome such drawbacks, such network-based approaches are less widely used within the life science community. In this article, we aim to provide a clear methodological framework on the use of such approaches in order to turn cancer research results into clinical applications. Therefore, we first discuss the rationale and the practical usage of three recently proposed network-based Cox regression algorithms (i.e., Net-Cox, AdaLnet, and fastcox). Then, we show how to combine existing biological knowledge and available data with such algorithms to identify networks of cancer biomarkers and to estimate survival of patients. Finally, we describe in detail a new permutation-based approach to better validate the significance of the selection in terms of cancer gene signatures and pathway/networks identification. We illustrate the proposed methodology by means of both simulations and real case studies. Overall, the aim of our work is two-fold. Firstly, to show how network-based Cox regression models can be used to integrate biological knowledge (e.g., multi-omics data) for the analysis of survival data. Secondly, to provide a clear methodological and computational approach for investigating cancers regulatory networks

Combining Pathway Identification and Breast Cancer Survival Prediction via Screening-Network Methods.

Breast cancer is one of the most common invasive tumors causing high mortality among women. It is characterized by high heterogeneity regarding its biological and clinical characteristics. Several high-throughput assays have been used to collect genome-wide information for many patients in large collaborative studies. This knowledge has improved our understanding of its biology and led to new methods of diagnosing and treating the disease. In particular, system biology has become a valid approach to obtain better insights into breast cancer biological mechanisms. A crucial component of current research lies in identifying novel biomarkers that can be predictive for breast cancer patient prognosis on the basis of the molecular signature of the tumor sample. However, the high dimension and low sample size of data greatly increase the difficulty of cancer survival analysis demanding for the development of ad-hoc statistical methods. In this work, we propose novel screening-network methods that predict patient survival outcome by screening key survival-related genes and we assess the capability of the proposed approaches using METABRIC dataset. In particular, we first identify a subset of genes by using variable screening techniques on gene expression data. Then, we perform Cox regression analysis by incorporating network information associated with the selected subset of genes. The novelty of this work consists in the improved prediction of survival responses due to the different types of screenings (i.e., a biomedical-driven, data-driven and a combination of the two) before building the network-penalized model. Indeed, the combination of the two screening approaches allows us to use the available biological knowledge on breast cancer and complement it with additional information emerging from the data used for the analysis. Moreover, we also illustrate how to extend the proposed approaches to integrate an additional omic layer, such as copy number aberrations, and we show that such strategies can further improve our prediction capabilities. In conclusion, our approaches allow to discriminate patients in high-and low-risk groups using few potential biomarkers and therefore, can help clinicians to provide more precise prognoses and to facilitate the subsequent clinical management of patients at risk of disease

Network-based survival analysis methods for pathway detection in cancer

We compare three penalized Cox regression methods for high-dimensional survival data in order to identify the pathways involved into cancer occurrence and pro- gression. We analyze each method with three gene expression datasets including breast, lung and ovarian cancer. More precisely, we focus on cancer survival prediction and on top signature genes. The goal of this study is to gain a deeper insight of the benefits and drawbacks of the regression techniques in order to find the pathways involved in a specific type of cancer and identify cancer biomarkers useful for prognosis, diagnosis and treatment

The Role of Congestion in Cardiorenal Syndrome Type 2: New Pathophysiological Insights into an Experimental Model of Heart Failure

BACKGROUND: In cardiorenal syndrome type 2 (CRS2), the role of systemic congestion in heart failure (HF) is still obscure. We studied a model of CRS2 [monocrotaline (MCT)-treated rats] secondary to pulmonary hypertension and right ventricular (RV) failure in order to evaluate the contribution of prevalent congestion to the development of kidney injury. METHODS: Ten animals were treated with MCT for 4 weeks until they developed HF. Eleven animals were taken as controls. Signs of hypertrophy and dilatation of the right ventricle demonstrated the occurrence of HF. Brain natriuretic peptide (BNP), serum creatinine (sCreatinine), both kidney and heart neutrophil gelatinase-associated lipocalin (NGAL), matrix metallopeptidase 9 (MMP9), serum cytokines as well as kidney and heart cell death, as assessed by TUNEL, were studied. RESULTS: Rats with HF showed higher BNP levels [chronic HF (CHF) 4.8 ± 0.5 ng/ml; controls 1.5 ± 0.2 ng/ml; p < 0.0001], marked RV hypertrophy and dilatation (RV mass/RV volume: CHF 1.46 ± 0.31, controls 2.41 ± 0.81; p < 0.01) as well as pleural and peritoneal effusions. A significant increase in proinflammatory cytokines and sCreatinine was observed (CHF 3.06 ± 1.3 pg/ml vs. controls 0.54 ± 0.23 pg/ml; p = 0.04). Serum (CHF 562.7 ± 93.34 ng/ml vs. controls 245.3 ± 58.19 ng/ml; p = 0.02) as well as renal and heart tissue NGAL levels [CHF 70,680 ± 4,337 arbitrary units (AU) vs. controls 32,120 ± 4,961 AU; p = 0.001] rose significantly, and they were found to be complexed with MMP9 in CHF rats. A higher number of kidney TUNEL-positive tubular cells was also detected (CHF 114.01 ± 45.93 vs. controls 16.36 ± 11.60 cells/mm(2); p = 0.0004). CONCLUSION: In this model of CHF with prevalent congestion, kidney injury is characterized by tubular damage and systemic inflammation. The upregulated NGAL complexed with MMP9 perpetuates the vicious circle of kidney/heart damage by enhancing the enzymatic activity of MMP9 with extracellular matrix degradation, worsening heart remodeling

Personalized Medicine in Gastrointestinal Stromal Tumor (GIST): Clinical Implications of the Somatic and Germline DNA Analysis

Gastrointestinal stromal tumors (GIST) are the most common mesenchymal tumors of the gastrointestinal tract. They are characterized by gain of function mutations in KIT or PDGFRA tyrosine kinase receptors, with their consequent constitutive activation. The gold standard therapy is imatinib that offers a good and stable response for approximately 18-36 months. However, resistance is very common and it is vital to identify new biomarkers. Up until now, there have been two main approaches with focus to characterize novel targets. On the one hand, the focus is on the tumor genome, as the final clinical outcome depends mainly from the cancer specific mutations/alterations patterns. However, the germline DNA is important as well, and it is inconceivable to think the patients response to the drug is not related to it. Therefore the aim of this review is to outline the state of the art of the personalized medicine in GIST taking into account both the tumor DNA (somatic) and the patient DNA (germline)

Abiraterone acetate in metastatic castration-resistant prostate cancer after chemotherapy. A retrospective “Real Life” analysis of activity and safety

Abiraterone acetate (AA) is a potent, selective androge (CYP17) biosynthesis inhibitor, which showed to improve overall survival (HR = 0.646) in mCRPC patients progressing after docetaxel. In this retrospective analysis we assessed the safety and efficacy of AA in patients affected with mCRPC progressing after chemotherapy, treated in the normal clinical practice, in several Italian Oncologic Units, after the approval of the drug from the Italian Drug Agency (AIFA)