Diagnostic Targeted Resequencing in 349 Patients with Drug-Resistant Pediatric Epilepsies Identifies Causative Mutations in 30 Different Genes

Targeted resequencing gene panels are used in the diagnostic setting to identify gene defects in epilepsy. We performed targeted resequencing using a 30-genes panel and a 95-genes panel in 349 patients with drug-resistant epilepsies beginning in the first years of life. We identified 71 pathogenic variants, 42 of which novel, in 30 genes, corresponding to 20.3% of the probands. In 66% of mutation positive patients, epilepsy onset occurred before the age of 6 months. The 95-genes panel allowed a genetic diagnosis in 22 (6.3%) patients that would have otherwise been missed using the 30-gene panel. About 50% of mutations were identified in genes coding for sodium and potassium channel components. SCN2A was the most frequently mutated gene followed by SCN1A, KCNQ2, STXBP1, SCN8A, CDKL5, and MECP2. Twenty-nine mutations were identified in 23 additional genes, most of them recently associated with epilepsy. Our data show that panels targeting about 100 genes represent the best cost-effective diagnostic option in pediatric drug-resistant epilepsies. They enable molecular diagnosis of atypical phenotypes, allowing to broaden phenotype-genotype correlations. Molecular diagnosis might influence patients' management and translate into better and specific treatment recommendations in some conditions

Transdiagnostic individualized clinically based risk calculator for the detection of individuals at risk and the prediction of psychosis: Model refinement including nonlinear effects of age

Background: The first rate-limiting step for primary indicated prevention of psychosis is the detection of young people who may be at risk. The ability of specialized clinics to detect individuals at risk for psychosis is limited. A clinically based, individualized, transdiagnostic risk calculator has been developed and externally validated to improve the detection of individuals at risk in secondary mental health care. This calculator employs core sociodemographic and clinical predictors, including age, which is defined in linear terms. Recent evidence has suggested a nonlinear impact of age on the probability of psychosis onset. Aim: To define at a meta-analytical level the function linking age and probability of psychosis onset. To incorporate this function in a refined version of the transdiagnostic risk calculator and to test its prognostic performance, compared to the original specification. Design: Secondary analyses on a previously published meta-analysis and clinical register-based cohort study based on 2008-2015 routine secondary mental health care in South London and Maudsley (SLaM) National Health Service (NHS) Foundation Trust. Participants: All patients receiving a first index diagnosis of non-organic/non-psychotic mental disorder within SLaM NHS Trust in the period 2008-2015. Main outcome measure: Prognostic accuracy (Harrell's C). Results: A total of 91,199 patients receiving a first index diagnosis of non-organic and non-psychotic mental disorder within SLaM NHS Trust were included in the derivation (33,820) or external validation (54,716) datasets. The mean follow-up was 1,588 days. The meta-analytical estimates showed that a second-degree fractional polynomial model with power (-2, -1: age1 = age-2 and age2 = age-1) was the best-fitting model (P < 0.001). The refined model that included this function showed an excellent prognostic accuracy in the external validation (Harrell's C = 0.805, 95% CI from 0.790 to 0.819), which was statistically higher than the original model, although of modest magnitude (Harrell's C change = 0.0136, 95% CIs from 0.006 to 0.021, P < 0.001). Conclusions: The use of a refined version of the clinically based, individualized, transdiagnostic risk calculator, which allows for nonlinearity in the association between age and risk of psychosis onset, may offer a modestly improved prognostic performance. This calculator may be particularly useful in young individuals at risk of developing psychosis who access secondary mental health care

Social cognition in people with schizophrenia: A cluster-analytic approach

Background The study aimed to subtype patients with schizophrenia on the basis of social cognition (SC), and to identify cut-offs that best discriminate among subtypes in 809 out-patients recruited in the context of the Italian Network for Research on Psychoses. Method A two-step cluster analysis of The Awareness of Social Inference Test (TASIT), the Facial Emotion Identification Test and Mayer-Salovey-Caruso Emotional Intelligence Test scores was performed. Classification and regression tree analysis was used to identify the cut-offs of variables that best discriminated among clusters. Results We identified three clusters, characterized by unimpaired (42%), impaired (50.4%) and very impaired (7.5%) SC. Three theory-of-mind domains were more important for the cluster definition as compared with emotion perception and emotional intelligence. Patients more able to understand simple sarcasm (14 for TASIT-SS) were very likely to belong to the unimpaired SC cluster. Compared with patients in the impaired SC cluster, those in the very impaired SC cluster performed significantly worse in lie scenes (TASIT-LI <10), but not in simple sarcasm. Moreover, functioning, neurocognition, disorganization and SC had a linear relationship across the three clusters, while positive symptoms were significantly lower in patients with unimpaired SC as compared with patients with impaired and very impaired SC. On the other hand, negative symptoms were highest in patients with impaired levels of SC. Conclusions If replicated, the identification of such subtypes in clinical practice may help in tailoring rehabilitation efforts to the person's strengths to gain more benefit to the person

Emerging Hallmarks of Metabolic Reprogramming in Prostate Cancer

Prostate cancer (PCa) is the most common male malignancy and the fifth leading cause of cancer death in men worldwide. Prostate cancer cells are characterized by a hybrid glycolytic/oxidative phosphorylation phenotype determined by androgen receptor signaling. An increased lipogenesis and cholesterogenesis have been described in PCa cells. Many studies have shown that enzymes involved in these pathways are overexpressed in PCa. Glutamine becomes an essential amino acid for PCa cells, and its metabolism is thought to become an attractive therapeutic target. A crosstalk between cancer and stromal cells occurs in the tumor microenvironment because of the release of different cytokines and growth factors and due to changes in the extracellular matrix. A deeper insight into the metabolic changes may be obtained by a multi-omic approach integrating genomics, transcriptomics, metabolomics, lipidomics, and radiomics data

Cancer Stem Cells in Renal Cell Carcinoma: Origins and Biomarkers

The term “cancer stem cell” (CSC) refers to a cancer cell with the following features: clonogenic ability, the expression of stem cell markers, differentiation into cells of different lineages, growth in nonadhesive spheroids, and the in vivo ability to generate serially transplantable tumors that reflect the heterogeneity of primary cancers (tumorigenicity). According to this model, CSCs may arise from normal stem cells, progenitor cells, and/or differentiated cells because of striking genetic/epigenetic mutations or from the fusion of tissue-specific stem cells with circulating bone marrow stem cells (BMSCs). CSCs use signaling pathways similar to those controlling cell fate during early embryogenesis (Notch, Wnt, Hedgehog, bone morphogenetic proteins (BMPs), fibroblast growth factors, leukemia inhibitory factor, and transforming growth factor-β). Recent studies identified a subpopulation of CD133+/CD24+ cells from ccRCC specimens that displayed self-renewal ability and clonogenic multipotency. The development of agents targeting CSC signaling-specific pathways and not only surface proteins may ultimately become of utmost importance for patients with RCC

Complement System and the Kidney: Its Role in Renal Diseases, Kidney Transplantation and Renal Cell Carcinoma

The crosstalk among the complement system, immune cells, and mediators of inflammation provides an efficient mechanism to protect the organism against infections and support the repair of damaged tissues. Alterations in this complex machinery play a role in the pathogenesis of different diseases. Core complement proteins C3 and C5, their activation fragments, their receptors, and their regulators have been shown to be active intracellularly as the complosome. The kidney is particularly vulnerable to complement-induced damage, and emerging findings have revealed the role of complement system dysregulation in a wide range of kidney disorders, including glomerulopathies and ischemia-reperfusion injury during kidney transplantation. Different studies have shown that activation of the complement system is an important component of tumorigenesis and its elements have been proved to be present in the TME of various human malignancies. The role of the complement system in renal cell carcinoma (RCC) has been recently explored. Clear cell and papillary RCC upregulate most of the complement genes relative to normal kidney tissue. The aim of this narrative review is to provide novel insights into the role of complement in kidney disorders

Cellular and Molecular Players in the Tumor Microenvironment of Renal Cell Carcinoma

Globally, clear-cell renal cell carcinoma (ccRCC) represents the most prevalent type of kidney cancer. Surgery plays a key role in the treatment of this cancer, although one third of patients are diagnosed with metastatic ccRCC and about 25% of patients will develop a recurrence after nephrectomy with curative intent. Molecular-target-based agents, such as tyrosine kinase inhibitors (TKIs) and immune checkpoint inhibitors (ICIs), are recommended for advanced cancers. In addition to cancer cells, the tumor microenvironment (TME) includes non-malignant cell types embedded in an altered extracellular matrix (ECM). The evidence confirms that interactions among cancer cells and TME elements exist and are thought to play crucial roles in the development of cancer, making them promising therapeutic targets. In the TME, an unfavorable pH, waste product accumulation, and competition for nutrients between cancer and immune cells may be regarded as further possible mechanisms of immune escape. To enhance immunotherapies and reduce resistance, it is crucial first to understand how the immune cells work and interact with cancer and other cancer-associated cells in such a complex tumor microenvironment