Molecular Portrait of Clear Cell Renal Cell Carcinoma: An Integrative Analysis of Gene Expression and Genomic Copy Number Profiling

Renal cell carcinoma (RCC) incidence accounts for about 3 to 10 cases per 100,000 individuals with a predilection for adult males over 60 year old (1.6:1 male/female ratio) (Chow, 2010; Nese, 2009). In Europe, about 60,000 individuals are affected by RCC every year, with a mortality rate of about 18,000 subjects and an incidence rate for all stages steadily rising over the last three decades. Although inherited forms occur in a number of familial cancer syndromes, as the well-known von Hippel-Lindau (VHL) syndrome, RCC is commonly sporadic (Cohen & McGovern, 2005; Kaelin, 2007) and, as recently highlighted by the National Cancer Institute (NCI), influenced by the interplay between exposure to environmental risk factors and genetic susceptibility of exposed individuals (Chow et al., 2010). Being poorly symptomatic in early phases, many cases become clinically detectable only when already advanced and, as such, therapy-resistant (Motzer, 2011). Based on histology, RCC can be classified into several subtypes, i.e., clear cell (80% of cases), papillary (10%), chromophobe (5%) and oncocytoma (5%), each one characterized by specific histo- pathological features, malignant potential and clinical outcome (Cohen & McGovern, 2005). Patient stratification is normally achieved using prognostic algorithms and nomograms based on multiple clinico-pathological factors such as TNM stage, Fuhrman nuclear grade, tumor size, performance status, necrosis and other hematological indices (Flanigan et al., 2011), although the most efficient predictors of survival and recurrence are based on nuclear grade alone (Nese et al., 2009). As recently reviewed by Brannon et al. (Brannon & Rathmell, 2010), a finer RCC subtype classification could be obtained exploiting the vast amount of genomic and transcriptional data that have been presented in numerous studies. For instance, several authors proposed a molecular classification of RCC based on differential gene expression profiles, with any subtype characterized by the activation of distinct gene sets (Brannon, 2010; Furge, 2004; Skubitz, 2006; Su\u308ltmann, 2005; Zhang, 2008), while others identified RCC-specific biomarkers (e.g. CA9, ki67, VEGF proteins, phosphorylated AKT, PTEN, HIF-1). Lately, it has been reported that microRNAs, a small class of non coding RNA molecules, could contribute to RCC development at different levels and may represent a new group of potential tumor biomarkers (Redova et al., 2011). Despite the numerous efforts in dissecting the molecular features of RCC through functional genomics, not a single transcriptional signature or biomarker has gained approval for clinical application yet (Arsanious, 2009; Eichelberg, 2009; Lam, 2007; Yin-Goen, 2006), so that the identification of novel molecular markers to improve early diagnosis and prognostic prediction and of candidate targets to develop new therapeutic approaches remains of primary importance for this pathology

Renal cell carcinoma primary cultures maintain genomic and phenotypic profile of parental tumor tissues

<p>Abstract</p> <p>Background</p> <p>Clear cell renal cell carcinoma (ccRCC) is characterized by recurrent copy number alterations (CNAs) and loss of heterozygosity (LOH), which may have potential diagnostic and prognostic applications. Here, we explored whether ccRCC primary cultures, established from surgical tumor specimens, maintain the DNA profile of parental tumor tissues allowing a more confident CNAs and LOH discrimination with respect to the original tissues.</p> <p>Methods</p> <p>We established a collection of 9 phenotypically well-characterized ccRCC primary cell cultures. Using the Affymetrix SNP array technology, we performed the genome-wide copy number (CN) profiling of both cultures and corresponding tumor tissues. Global concordance for each culture/tissue pair was assayed evaluating the correlations between whole-genome CN profiles and SNP allelic calls. CN analysis was performed using the two CNAG v3.0 and Partek software, and comparing results returned by two different algorithms (Hidden Markov Model and Genomic Segmentation).</p> <p>Results</p> <p>A very good overlap between the CNAs of each culture and corresponding tissue was observed. The finding, reinforced by high whole-genome CN correlations and SNP call concordances, provided evidence that each culture was derived from its corresponding tissue and maintained the genomic alterations of parental tumor. In addition, primary culture DNA profile remained stable for at least 3 weeks, till to third passage. These cultures showed a greater cell homogeneity and enrichment in tumor component than original tissues, thus enabling a better discrimination of CNAs and LOH. Especially for hemizygous deletions, primary cultures presented more evident CN losses, typically accompanied by LOH; differently, in original tissues the intensity of these deletions was weaken by normal cell contamination and LOH calls were missed.</p> <p>Conclusions</p> <p>ccRCC primary cultures are a reliable <it>in vitro </it>model, well-reproducing original tumor genetics and phenotype, potentially useful for future functional approaches aimed to study genes or pathways involved in ccRCC etiopathogenesis and to identify novel clinical markers or therapeutic targets. Moreover, SNP array technology proved to be a powerful tool to better define the cell composition and homogeneity of RCC primary cultures.</p

MASTINO: Learning Bayesian Networks Using R

Bayesian Networks are increasingly used to represent conditional independence relations among variables and causal information in problem domains in which decisions are based on probabilistic reasoning. Structural learning is NPhard therefore the database of observed cases must be often supplemented with search heuristics based on prior information. In this paper we present a software package for R, called MASTINO, that extends the existing DEAL package by providing new tools for learning Bayesian Networks and Conditional Gaussian networks in a score-and-search framework, such as the score function P-metric and the M-GA genetic algorithm. MASTINO is freely available under the terms of the GNU General Public License Version 2, and it has been recently submitted to be part of the CRAN repository. Meanwhile it can be downloaded from the website: http://statind.jrc.it/mastino. Keywords: Bayesian Networks, Structural Learning, R Package 1 Introduction Bayesian Networks (BNs), Cowell et al. (1999), are a widespread tool in many areas of artificial intelligence and statistics because of efficient algorithms which make probabilistic inference effective in highly structured problem domains. BNs are suited to represent conditional independence relationships but they have been extended to represent causal information, Spirtes et al. (2000), and utility of decisions, so that probabilistic expert systems are increasingly developed in areas ranging from technology to medical problem domains. Inference about the structure of a BN, also called structural learning, has been proved to be a NP-hard problem, Chickering (1995). Structural learning is typically performed by combining expert¿s priori knowledge with the information contained in a database of cases. Several heuristics have been shown to work in practice and it seems that specialized problem domains take benefits from problem-dependent tuning. A software package for R, (R Development Core Team, 2008), suited to quickly implement hypothesized heuristics andJRC.G.9-Econometrics and statistical support to antifrau

Tocilizumab for patients with COVID-19 pneumonia. The single-arm TOCIVID-19 prospective trial

BackgroundTocilizumab blocks pro-inflammatory activity of interleukin-6 (IL-6), involved in pathogenesis of pneumonia the most frequent cause of death in COVID-19 patients.MethodsA multicenter, single-arm, hypothesis-driven trial was planned, according to a phase 2 design, to study the effect of tocilizumab on lethality rates at 14 and 30 days (co-primary endpoints, a priori expected rates being 20 and 35%, respectively). A further prospective cohort of patients, consecutively enrolled after the first cohort was accomplished, was used as a secondary validation dataset. The two cohorts were evaluated jointly in an exploratory multivariable logistic regression model to assess prognostic variables on survival.ResultsIn the primary intention-to-treat (ITT) phase 2 population, 180/301 (59.8%) subjects received tocilizumab, and 67 deaths were observed overall. Lethality rates were equal to 18.4% (97.5% CI: 13.6-24.0, P=0.52) and 22.4% (97.5% CI: 17.2-28.3, P<0.001) at 14 and 30 days, respectively. Lethality rates were lower in the validation dataset, that included 920 patients. No signal of specific drug toxicity was reported. In the exploratory multivariable logistic regression analysis, older age and lower PaO2/FiO2 ratio negatively affected survival, while the concurrent use of steroids was associated with greater survival. A statistically significant interaction was found between tocilizumab and respiratory support, suggesting that tocilizumab might be more effective in patients not requiring mechanical respiratory support at baseline.ConclusionsTocilizumab reduced lethality rate at 30 days compared with null hypothesis, without significant toxicity. Possibly, this effect could be limited to patients not requiring mechanical respiratory support at baseline.Registration EudraCT (2020-001110-38); clinicaltrials.gov (NCT04317092)