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Motivation Mutational signatures are a critical component in deciphering the genetic alterations that underlie cancer development and have become a valuable resource to understand the genomic changes during tumorigenesis. Therefore, it is essential to employ precise and accurate methods for their extraction to ensure that the underlying patterns are reliably identified and can be effectively utilized in new strategies for diagnosis, prognosis, and treatment of cancer patients. Results We present MUSE-XAE, a novel method for mutational signature extraction from cancer genomes using an explainable autoencoder. Our approach employs a hybrid architecture consisting of a nonlinear encoder that can capture nonlinear interactions among features, and a linear decoder which ensures the interpretability of the active signatures. We evaluated and compared MUSE-XAE with other available tools on both synthetic and real cancer datasets and demonstrated that it achieves superior performance in terms of precision and sensitivity in recovering mutational signature profiles. MUSE-XAE extracts highly discriminative mutational signature profiles by enhancing the classification of primary tumour types and subtypes in real world settings. This approach could facilitate further research in this area, with neural networks playing a critical role in advancing our understanding of cancer genomics. Availability and implementation MUSE-XAE software is freely available at https://github.com/compbiomed-unito/MUSE-XAE. [ABSTRACT FROM AUTHOR]

Missense variation in genomes can affect protein structure stability and, in turn, the cell physiology behavior. Predicting the impact of those variations is relevant, and the best-performing computational tools exploit the protein structure information. However, most of the current protein sequence variants are unresolved, and comparative or ab initio tools can provide a structure. Here, we evaluate the impact of model structures, compared to experimental structures, on the predictors of protein stability changes upon single-point mutations, where no significant changes are expected between the original and the mutated structures. We show that there are substantial differences among the computational tools. Methods that rely on coarse-grained representation are less sensitive to the underlying protein structures. In contrast, tools that exploit more detailed molecular representations are sensible to structures generated from comparative modeling, even on single-residue substitutions. [ABSTRACT FROM AUTHOR]

The high cosine similarity between some single-base substitution mutational signatures and their characteristic flat profiles could suggest the presence of overfitting and mathematical artefacts. The newest version (v3.3) of the signature database available in the Catalogue Of Somatic Mutations In Cancer (COSMIC) provides a collection of 79 mutational signatures, which has more than doubled with respect to previous version (30 profiles available in COSMIC signatures v2), making more critical the associations between signatures and specific mutagenic processes. This study both provides a systematic assessment of the de novo extraction task through simulation scenarios based on the latest version of the COSMIC signatures and highlights, through a novel approach using archetypal analysis, which COSMIC signatures are redundant and more likely to be considered as mathematical artefacts. 29 archetypes were able to reconstruct the profile of all the COSMIC signatures with cosine similarity > 0.8. Interestingly, these archetypes tend to group similar original signatures sharing either the same aetiology or similar biological processes. We believe that these findings will be useful to encourage the development of new de novo extraction methods avoiding the redundancy of information among the signatures while preserving the biological interpretation. [ABSTRACT FROM AUTHOR]

Predicting the difference in thermodynamic stability between protein variants is crucial for protein design and understanding the genotype-phenotype relationships. So far, several computational tools have been created to address this task. Nevertheless, most of them have been trained or optimized on the same and 'all' available data, making a fair comparison unfeasible. Here, we introduce a novel dataset, collected and manually cleaned from the latest version of the ThermoMutDB database, consisting of 669 variants not included in the most widely used training datasets. The prediction performance and the ability to satisfy the antisymmetry property by considering both direct and reverse variants were evaluated across 21 different tools. The Pearson correlations of the tested tools were in the ranges of 0.21–0.5 and 0–0.45 for the direct and reverse variants, respectively. When both direct and reverse variants are considered, the antisymmetric methods perform better achieving a Pearson correlation in the range of 0.51–0.62. The tested methods seem relatively insensitive to the physiological conditions, performing well also on the variants measured with more extreme pH and temperature values. A common issue with all the tested methods is the compression of the |$\Delta \Delta G$| predictions toward zero. Furthermore, the thermodynamic stability of the most significantly stabilizing variants was found to be more challenging to predict. This study is the most extensive comparisons of prediction methods using an entirely novel set of variants never tested before. [ABSTRACT FROM AUTHOR]

Background & Aims Micro RNAs (mi RNAs) have been involved in hepatocarcinogenesis, but little is known on their role in the progression of chronic viral hepatitis. Aim of this study was to identify mi RNA signatures associated with stages of disease progression in patients with chronic viral hepatitis. Methods Mi RNA expression profile was investigated in liver biopsies from patients with chronic viral hepatitis and correlated with clinical, virological and histopathological features. Relevant mi RNAs were further investigated. Results Most of the significant changes in mi RNA expression were associated with liver fibrosis stages and included the significant up-regulation of a group of mi RNAs that were demonstrated to target the master regulators of epithelial-mesenchymal transition ZEB1 and ZEB2 and involved in the preservation of epithelial cell differentiation, but also in cell proliferation and fibrogenesis. In agreement with mi RNA data, immunostaining of liver biopsies showed that expression of the epithelial marker E-cadherin was maintained in severe fibrosis/cirrhosis while expression of ZEBs and other markers of epithelial-mesenchymal transition were low or absent. Severe liver fibrosis was also significantly associated with the down-regulation of mi RNAs with antiproliferative and tumour suppressor activity. Similar changes in mi RNA and target gene expression were demonstrated along with disease progression in a mouse model of carbon tetrachloride ( CCl4)-induced liver fibrosis, suggesting that they might represent a general response to liver injury. Conclusion Chronic viral hepatitis progression is associated with the activation of mi RNA pathways that promote cell proliferation and fibrogenesis, but preserve the differentiated hepatocyte phenotype. [ABSTRACT FROM AUTHOR]

Epigenetic deregulation is a hallmark of cancer characterized by frequent acquisition of new DNA methylation in CpG islands. To gain insight into the methylation changes of canine DLBCL, we investigated the DNA methylome in primary DLBCLs in comparison with control lymph nodes by genome-wide CpG microarray. We identified 1,194 target loci showing different methylation levels in tumors compared with controls. The hypermethylated CpG loci included promoter, 5′-UTRs, upstream and exonic regions. Interestingly, targets of polycomb repressive complex in stem cells were mostly affected suggesting that DLBCL shares a stem cell-like epigenetic pattern. Functional analysis highlighted biological processes strongly related to embryonic development, tissue morphogenesis and cellular differentiation, including HOX, BMP and WNT. In addition, the analysis of epigenetic patterns and genome-wide methylation variability identified cDLBCL subgroups. Some of these epigenetic subtypes showed a concordance with the clinical outcome supporting the hypothesis that the accumulation of aberrant epigenetic changes results in a more aggressive behavior of the tumor. Collectively, our results suggest an important role of DNA methylation in DLBCL where aberrancies in transcription factors were frequently observed, suggesting an involvement during tumorigenesis. These findings warrant further investigation to improve cDLBCL prognostic classification and provide new insights on tumor aggressiveness.

The Spalt-Like Transcription Factor 4 (SALL4) oncogene plays a central function in embryo-fetal development and is absent in differentiated tissues. Evidence suggests that it can be reactivated in several cancers worsening the prognosis. We aimed at investigating the risk associated with SALL4 reactivation for all-cause mortality and recurrence in cancer using the current literature. A PubMed and SCOPUS search until 1st September 2016 was performed, focusing on perspective studies reporting prognostic parameters in cancer data. In addition, 17 datasets of different cancer types from The Cancer Genome Atlas were considered. A total of 9,947 participants across 40 cohorts, followed-up for about 5 years on average, were analyzed comparing patients showing SALL4 presence (SALL4+, n = 1,811) or absence (SALL4-, n = 8,136). All data were summarised using risk ratios (RRs) for the number of deaths/recurrences and hazard ratios (HRs) for the time-dependent risk related to SALL4+, adjusted for potential confounders. SALL4+ significantly increased overall mortality (RR = 1.34, 95% confidence intervals (CI)=1.21-1.48, p<0.0001, I2=66%; HR=1.4; 95%CI: 1.19-1.65; p<0.0001; I2=63%) and recurrence of disease (RR = 1.25, 95% CI = 1.1-1.42, p=0.0006, I2=62%); HR=1.52; 95% CI: 1.22-1.89, p=0.0002; I2=69%) compared to SALL4-. Moreover, SALL4 remained significantly associated with poor prognosis even using HRs adjusted for potential confounders (overall mortality: HR=1.4; 95%CI: 1.19-1.65; p<0.0001; I2=63%; recurrence of disease: HR=1.52; 95% CI: 1.22-1.89, p=0.0002; I2=69%). These results suggest that SALL4 expression increases both mortality and recurrence of cancer, confirming this gene as an important prognostic marker and a potential target for personalized medicine.

Background: High-throughput (HT) technologies provide huge amount of gene expression data that can be used to identify biomarkers useful in the clinical practice. The most frequently used approaches first select a set of genes (i.e. gene signature) able to characterize differences between two or more phenotypical conditions, and then provide a functional assessment of the selected genes with an a posteriori enrichment analysis, based on biological knowledge. However, this approach comes with some drawbacks. First, gene selection procedure often requires tunable parameters that affect the outcome, typically producing many false hits. Second, a posteriori enrichment analysis is based on mapping between biological concepts and gene expression measurements, which is hard to compute because of constant changes in biological knowledge and genome analysis. Third, such mapping is typically used in the assessment of the coverage of gene signature by biological concepts, that is either score-based or requires tunable parameters as well, limiting its power. Results: We present Knowledge Driven Variable Selection (KDVS), a framework that uses a priori biological knowledge in HT data analysis. The expression data matrix is transformed, according to prior knowledge, into smaller matrices, easier to analyze and to interpret from both computational and biological viewpoints. Therefore KDVS, unlike most approaches, does not exclude a priori any function or process potentially relevant for the biological question under investigation. Differently from the standard approach where gene selection and functional assessment are applied independently, KDVS embeds these two steps into a unified statistical framework, decreasing the variability derived from the threshold-dependent selection, the mapping to the biological concepts, and the signature coverage. We present three case studies to assess the usefulness of the method. Conclusions: We showed that KDVS not only enables the selection of known biological functionalities with accuracy, but also identification of new ones. An efficient implementation of KDVS was devised to obtain results in a fast and robust way. Computing time is drastically reduced by the effective use of distributed resources. Finally, integrated visualization techniques immediately increase the interpretability of results. Overall, KDVS approach can be considered as a viable alternative to enrichment-based approaches. [ABSTRACT FROM AUTHOR]

Motivation: The identification of robust lists of molecular biomarkers related to a disease is a fundamental step for early diagnosis and treatment. However, methodologies for the discovery of biomarkers using microarray data often provide results with limited overlap. These differences are imputable to 1) dataset size (few subjects with respect to the number of features); 2) heterogeneity of the disease; 3) heterogeneity of experimental protocols and computational pipelines employed in the analysis. In this paper, we focus on the first two issues and assess, both on simulated (through an in silico regulation network model) and real clinical datasets, the consistency of candidate biomarkers provided by a number of different methods. Methods: We extensively simulated the effect of heterogeneity characteristic of complex diseases on different sets of microarray data. Heterogeneity was reproduced by simulating both intrinsic variability of the population and the alteration of regulatory mechanisms. Population variability was simulated by modeling evolution of a pool of subjects; then, a subset of them underwent alterations in regulatory mechanisms so as to mimic the disease state. Results: The simulated data allowed us to outline advantages and drawbacks of different methods across multiple studies and varying number of samples and to evaluate precision of feature selection on a benchmark with known biomarkers. Although comparable classification accuracy was reached by different methods, the use of external cross-validation loops is helpful in finding features with a higher degree of precision and stability. Application to real data confirmed these results. [ABSTRACT FROM AUTHOR]

Background: Insulin action on protein synthesis (translation of transcripts) and post-translational modifications, especially of those involving the reversible modifications such as phosphorylation of various signaling proteins, are extensively studied but insulin effect on transcription of genes, especially of transcriptional temporal patterns remains to be fully defined. Methodology/Principal Findings: To identify significant transcriptional temporal patterns we utilized primary differentiated rat skeletal muscle myotubes which were treated with insulin and samples were collected every 20 min for 8 hours. Pooled samples at every hour were analyzed by gene array approach to measure transcript levels. The patterns of transcript levels were analyzed based on a novel method that integrates selection, clustering, and functional annotation to find the main temporal patterns associated to functional groups of differentially expressed genes. 326 genes were found to be differentially expressed in response to in vitro insulin administration in skeletal muscle myotubes. Approximately 20% of the genes that were differentially expressed were identified as belonging to the insulin signaling pathway. Characteristic transcriptional temporal patterns include: (a) a slow and gradual decrease in gene expression, (b) a gradual increase in gene expression reaching a peak at about 5 hours and then reaching a plateau or an initial decrease and other different variable pattern of increase in gene expression over time. Conclusion/Significance: The new method allows identifying characteristic dynamic responses to insulin stimulus, common to a number of genes and associated to the same functional group. The results demonstrate that insulin treatment elicited different clusters of gene transcript profile supporting a temporal regulation of gene expression by insulin in skeletal muscle cells. [ABSTRACT FROM AUTHOR]

Background: The identification of robust lists of molecular biomarkers related to a disease is a fundamental step for early diagnosis and treatment. However, methodologies for biomarker discovery using microarray data often provide results with limited overlap. It has been suggested that one reason for these inconsistencies may be that in complex diseases, such as cancer, multiple genes belonging to one or more physiological pathways are associated with the outcomes. Thus, a possible approach to improve list stability is to integrate biological information from genomic databases in the learning process; however, a comprehensive assessment based on different types of biological information is still lacking in the literature. In this work we have compared the effect of using different biological information in the learning process like functional annotations, protein-protein interactions and expression correlation among genes. Results: Biological knowledge has been codified by means of gene similarity matrices and expression data linearly transformed in such a way that the more similar two features are, the more closely they are mapped. Two semantic similarity matrices, based on Biological Process and Molecular Function Gene Ontology annotation, and geodesic distance applied on protein-protein interaction networks, are the best performers in improving list stability maintaining almost equal prediction accuracy. Conclusions: The performed analysis supports the idea that when some features are strongly correlated to each other, for example because are close in the protein-protein interaction network, then they might have similar importance and are equally relevant for the task at hand. Obtained results can be a starting point for additional experiments on combining similarity matrices in order to obtain even more stable lists of biomarkers. The implementation of the classification algorithm is available at the link: http://www.math.unipd.it/dasan/biomarkers.html. [ABSTRACT FROM AUTHOR]

Background: In diabetes chronic hyperinsulinemia contributes to the instability of the atherosclerotic plaque and stimulates cellular proliferation through the activation of the MAP kinases, which in turn regulate cellular proliferation. However, it is not known whether insulin itself could increase the transcription of specific genes for cellular proliferation in the endothelium. Hence, the characterization of transcriptional modifications in endothelium is an important step for a better understanding of the mechanism of insulin action and the relationship between endothelial cell dysfunction and insulin resistance. Methodology and principal findings: The transcriptional response of endothelial cells in the 440 minutes following insulin stimulation was monitored using microarrays and compared to a control condition. About 1700 genes were selected as differentially expressed based on their treated minus control profile, thus allowing the detection of even small but systematic changes in gene expression. Genes were clustered in 7 groups according to their time expression profile and classified into 15 functional categories that can support the biological effects of insulin, based on Gene Ontology enrichment analysis. In terms of endothelial function, the most prominent processes affected were NADH dehydrogenase activity, N-terminal myristoylation domain binding, nitric-oxide synthase regulator activity and growth factor binding. Pathway-based enrichment analysis revealed ''Electron Transport Chain'' significantly enriched. Results were validated on genes belonging to ''Electron Transport Chain'' pathway, using quantitative RT-PCR. Conclusions: As far as we know, this is the first systematic study in the literature monitoring transcriptional response to insulin in endothelial cells, in a time series microarray experiment. Since chronic hyperinsulinemia contributes to the instability of the atherosclerotic plaque and stimulates cellular proliferation, some of the genes identified in the present work are potential novel candidates in diabetes complications related to endothelial dysfunction. [ABSTRACT FROM AUTHOR]

Epithelial ovarian cancer (EOC) outpaces all the other forms of the female reproductive system malignancies. MicroRNAs have emerged as promising predictive biomarkers to therapeutic treatments as their expression might characterize the tumor stage or grade. In EOC, miR-200c is considered a master regulator of oncogenes or tumor suppressors. To investigate novel miR-200c-3p target genes involved in EOC tumorigenesis, we evaluated the association between this miRNA and the mRNA expression of several potential target genes by RNA-seq data of both 46 EOC cell lines from Cancer Cell line Encyclopedia (CCLE) and 456 EOC patient bio-specimens from The Cancer Genome Atlas (TCGA). Both analyses showed a significant anticorrelation between miR-200c-3p and the protein phosphatase 3 catalytic subunit γ of calcineurin (PPP3CC) levels involved in the apoptosis pathway. Quantitative mRNA expression analysis in patient biopsies confirmed the inverse correlation between miR-200c-3p and PPP3CC levels. In vitro regulation of PPP3CC expression through miR-200c-3p and RNA interference technology led to a concomitant modulation of BCL2- and p-AKT-related pathways, suggesting the tumor suppressive role of PPP3CC in EOC. Our results suggest that inhibition of high expression of miR-200c-3p in EOC might lead to overexpression of the tumor suppressor PPP3CC and subsequent induction of apoptosis in EOC patients. [ABSTRACT FROM AUTHOR]

Several studies have linked disruptions of protein stability and its normal functions to disease. Therefore, during the last few decades, many tools have been developed to predict the free energy changes upon protein residue variations. Most of these methods require both sequence and structure information to obtain reliable predictions. However, the lower number of protein structures available with respect to their sequences, due to experimental issues, drastically limits the application of these tools. In addition, current methodologies ignore the antisymmetric property characterizing the thermodynamics of the protein stability: a variation from wild-type to a mutated form of the protein structure ( X W → X M ) and its reverse process ( X M → X W ) must have opposite values of the free energy difference ( Δ Δ G W M = − Δ Δ G M W ). Here we propose ACDC-NN-Seq, a deep neural network system that exploits the sequence information and is able to incorporate into its architecture the antisymmetry property. To our knowledge, this is the first convolutional neural network to predict protein stability changes relying solely on the protein sequence. We show that ACDC-NN-Seq compares favorably with the existing sequence-based methods. [ABSTRACT FROM AUTHOR]

Conventional/targeted chemotherapies and ionizing radiation (IR) are being used both as monotherapies and in combination for the treatment of epithelial ovarian cancer (EOC). Several studies show that these therapies might favor oncogenic signaling and impede anti-tumor responses. MiR-200c is considered a master regulator of EOC-related oncogenes. In this study, we sought to investigate if chemotherapy and IR could influence the expression of miR-200c-3p and its target genes, like the immune checkpoint PD-L1 and other oncogenes in a cohort of EOC patients' biopsies. Indeed, PD-L1 expression was induced, while miR-200c-3p was significantly reduced in these biopsies post-therapy. The effect of miR-200c-3p target genes was assessed in miR-200c transfected SKOV3 cells untreated and treated with olaparib and IR alone. Under all experimental conditions, miR-200c-3p concomitantly reduced PD-L1, c-Myc and β-catenin expression and sensitized ovarian cancer cells to olaparib and irradiation. In silico analyses further confirmed the anti-correlation between miR-200c-3p with c-Myc and β-catenin in 46 OC cell lines and showed that a higher miR-200c-3p expression associates with a less tumorigenic microenvironment. These findings provide new insights into how miR-200c-3p could be used to hold in check the adverse effects of conventional chemotherapy, targeted therapy and radiation therapy, and offer a novel therapeutic strategy for EOC. [ABSTRACT FROM AUTHOR]

Non-invasive scoring systems (NSS) are used to identify patients with non-alcoholic fatty liver disease (NAFLD) who are at risk of advanced fibrosis, but their reliability in predicting long-term outcomes for hepatic/extrahepatic complications or death and their concordance in cross-sectional and longitudinal risk stratification remain uncertain. The most common NSS (NFS, FIB-4, BARD, APRI) and the Hepamet fibrosis score (HFS) were assessed in 1,173 European patients with NAFLD from tertiary centres. Performance for fibrosis risk stratification and for the prediction of long-term hepatic/extrahepatic events, hepatocarcinoma (HCC) and overall mortality were evaluated in terms of AUC and Harrell's c-index. For longitudinal data, NSS-based Cox proportional hazard models were trained on the whole cohort with repeated 5-fold cross-validation, sampling for testing from the 607 patients with all NSS available. Cross-sectional analysis revealed HFS as the best performer for the identification of significant (F0-1 vs. F2-4, AUC = 0.758) and advanced (F0-2 vs. F3-4, AUC = 0.805) fibrosis, while NFS and FIB-4 showed the best performance for detecting histological cirrhosis (range AUCs 0.85-0.88). Considering longitudinal data (follow-up between 62 and 110 months), NFS and FIB-4 were the best at predicting liver-related events (c-indices>0.7), NFS for HCC (c-index = 0.9 on average), and FIB-4 and HFS for overall mortality (c-indices >0.8). All NSS showed limited performance (c-indices <0.7) for extrahepatic events. Overall, NFS, HFS and FIB-4 outperformed APRI and BARD for both cross-sectional identification of fibrosis and prediction of long-term outcomes, confirming that they are useful tools for the clinical management of patients with NAFLD at increased risk of fibrosis and liver-related complications or death. Non-invasive scoring systems are increasingly being used in patients with non-alcoholic fatty liver disease to identify those at risk of advanced fibrosis and hence clinical complications. Herein, we compared various non-invasive scoring systems and identified those that were best at identifying risk, as well as those that were best for the prediction of long-term outcomes, such as liver-related events, liver cancer and death. [Display omitted] • Different non-invasive scoring systems (NSS) have been proposed to stratify patients according to the risk of advanced fibrosis. • In the cross-sectional analysis, HFS showed the best performance for the identification of advanced fibrosis. • NFS and FIB-4 showed the best performance for the detection of histological cirrhosis. • After a median follow-up of ~7 years, NFS, HFS and FIB-4 performed similarly well for the prediction of HCC and overall mortality. • All NSS had limited performance for extrahepatic events, although those incorporating diabetes performed slightly better. [ABSTRACT FROM AUTHOR]

Summary Islet β cells from newborn mammals exhibit high basal insulin secretion and poor glucose-stimulated insulin secretion (GSIS). Here we show that β cells of newborns secrete more insulin than adults in response to similar intracellular Ca 2+ concentrations, suggesting differences in the Ca 2+ sensitivity of insulin secretion. Synaptotagmin 4 (Syt4), a non-Ca 2+ binding paralog of the β cell Ca 2+ sensor Syt7, increased by ∼8-fold during β cell maturation. Syt4 ablation increased basal insulin secretion and compromised GSIS. Precocious Syt4 expression repressed basal insulin secretion but also impaired islet morphogenesis and GSIS. Syt4 was localized on insulin granules and Syt4 levels inversely related to the number of readily releasable vesicles. Thus, transcriptional regulation of Syt4 affects insulin secretion; Syt4 expression is regulated in part by Myt transcription factors, which repress Syt4 transcription. Finally, human SYT4 regulated GSIS in EndoC-βH1 cells, a human β cell line. These findings reveal the role that altered Ca 2+ sensing plays in regulating β cell maturation. [ABSTRACT FROM AUTHOR]