Transmembrane Protein TMEM230, Regulator of Glial Cell Vascular Mimicry and Endothelial Cell Angiogenesis in High-Grade Heterogeneous Infiltrating Gliomas and Glioblastoma.

High-grade gliomas (HGGs) and glioblastoma multiforme (GBM) are characterized by a heterogeneous and aggressive population of tissue-infiltrating cells that promote both destructive tissue remodeling and aberrant vascularization of the brain. The formation of defective and permeable blood vessels and microchannels and destructive tissue remodeling prevent efficient vascular delivery of pharmacological agents to tumor cells and are the significant reason why therapeutic chemotherapy and immunotherapy intervention are primarily ineffective. Vessel-forming endothelial cells and microchannel-forming glial cells that recapitulate vascular mimicry have both infiltration and destructive remodeling tissue capacities. The transmembrane protein TMEM230 (C20orf30) is a master regulator of infiltration, sprouting of endothelial cells, and microchannel formation of glial and phagocytic cells. A high level of TMEM230 expression was identified in patients with HGG, GBM, and U87-MG cells. In this study, we identified candidate genes and molecular pathways that support that aberrantly elevated levels of TMEM230 play an important role in regulating genes associated with the initial stages of cell infiltration and blood vessel and microchannel (also referred to as tumor microtubule) formation in the progression from low-grade to high-grade gliomas. As TMEM230 regulates infiltration, vascularization, and tissue destruction capacities of diverse cell types in the brain, TMEM230 is a promising cancer target for heterogeneous HGG tumors.

scMuffin: an R package to disentangle solid tumor heterogeneity by single-cell gene expression analysis.

INTRODUCTION: Single-cell (SC) gene expression analysis is crucial to dissect the complex cellular heterogeneity of solid tumors, which is one of the main obstacles for the development of effective cancer treatments. Such tumors typically contain a mixture of cells with aberrant genomic and transcriptomic profiles affecting specific sub-populations that might have a pivotal role in cancer progression, whose identification eludes bulk RNA-sequencing approaches. We present scMuffin, an R package that enables the characterization of cell identity in solid tumors on the basis of a various and complementary analyses on SC gene expression data. RESULTS: scMuffin provides a series of functions to calculate qualitative and quantitative scores, such as: expression of marker sets for normal and tumor conditions, pathway activity, cell state trajectories, Copy Number Variations, transcriptional complexity and proliferation state. Thus, scMuffin facilitates the combination of various evidences that can be used to distinguish normal and tumoral cells, define cell identities, cluster cells in different ways, link genomic aberrations to phenotypes and identify subtle differences between cell subtypes or cell states. We analysed public SC expression datasets of human high-grade gliomas as a proof-of-concept to show the value of scMuffin and illustrate its user interface. Nevertheless, these analyses lead to interesting findings, which suggest that some chromosomal amplifications might underlie the invasive tumor phenotype and the presence of cells that possess tumor initiating cells characteristics. CONCLUSIONS: The analyses offered by scMuffin and the results achieved in the case study show that our tool helps addressing the main challenges in the bioinformatics analysis of SC expression data from solid tumors.

Characterization and comparison of gene-centered human interactomes.

The complex web of macromolecular interactions occurring within cells-the interactome-is the backbone of an increasing number of studies, but a clear consensus on the exact structure of this network is still lacking. Different genome-scale maps of human interactome have been obtained through several experimental techniques and functional analyses. Moreover, these maps can be enriched through literature-mining approaches, and different combinations of various 'source' databases have been used in the literature. It is therefore unclear to which extent the various interactomes yield similar results when used in the context of interactome-based approaches in network biology. We compared a comprehensive list of human interactomes on the basis of topology, protein complexes, molecular pathways, pathway cross-talk and disease gene prediction. In a general context of relevant heterogeneity, our study provides a series of qualitative and quantitative parameters that describe the state of the art of human interactomes and guidelines for selecting interactomes in future applications.

Clinical relevance of clonal hematopoiesis in persons aged ≥80 years.

Clonal hematopoiesis of indeterminate potential (CHIP) is associated with increased risk of cancers and inflammation-related diseases. This phenomenon becomes common in persons aged ≥80 years, in whom the implications of CHIP are not well defined. We performed a mutational screening in 1794 persons aged ≥80 years and investigated the relationships between CHIP and associated pathologies. Mutations were observed in one-third of persons aged ≥80 years and were associated with reduced survival. Mutations in JAK2 and splicing genes, multiple mutations (DNMT3A, TET2, and ASXL1 with additional genetic lesions), and variant allele frequency ≥0.096 had positive predictive value for myeloid neoplasms. Combining mutation profiles with abnormalities in red blood cell indices improved the ability of myeloid neoplasm prediction. On this basis, we defined a predictive model that identifies 3 risk groups with different probabilities of developing myeloid neoplasms. Mutations in DNMT3A, TET2, ASXL1, or JAK2 were associated with coronary heart disease and rheumatoid arthritis. Cytopenia was common in persons aged ≥80 years, with the underlying cause remaining unexplained in 30% of cases. Among individuals with unexplained cytopenia, the presence of highly specific mutation patterns was associated with myelodysplastic-like phenotype and a probability of survival comparable to that of myeloid neoplasms. Accordingly, 7.5% of subjects aged ≥80 years with cytopenia had presumptive evidence of myeloid neoplasm. In summary, specific mutational patterns define different risk of developing myeloid neoplasms vs inflammatory-associated diseases in persons aged ≥80 years. In individuals with unexplained cytopenia, mutational status may identify those subjects with presumptive evidence of myeloid neoplasms.

Gene relevance based on multiple evidences in complex networks.

MOTIVATION: Multi-omics approaches offer the opportunity to reconstruct a more complete picture of the molecular events associated with human diseases, but pose challenges in data analysis. Network-based methods for the analysis of multi-omics leverage the complex web of macromolecular interactions occurring within cells to extract significant patterns of molecular alterations. Existing network-based approaches typically address specific combinations of omics and are limited in terms of the number of layers that can be jointly analysed. In this study, we investigate the application of network diffusion to quantify gene relevance on the basis of multiple evidences (layers). RESULTS: We introduce a gene score (mND) that quantifies the relevance of a gene in a biological process taking into account the network proximity of the gene and its first neighbours to other altered genes. We show that mND has a better performance over existing methods in finding altered genes in network proximity in one or more layers. We also report good performances in recovering known cancer genes. The pipeline described in this article is broadly applicable, because it can handle different types of inputs: in addition to multi-omics datasets, datasets that are stratified in many classes (e.g., cell clusters emerging from single cell analyses) or a combination of the two scenarios. AVAILABILITY AND IMPLEMENTATION: The R package 'mND' is available at URL: https://www.itb.cnr.it/mnd. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

isma: an R package for the integrative analysis of mutations detected by multiple pipelines.

BACKGROUND: Recent comparative studies have brought to our attention how somatic mutation detection from next-generation sequencing data is still an open issue in bioinformatics, because different pipelines result in a low consensus. In this context, it is suggested to integrate results from multiple calling tools, but this operation is not trivial and the burden of merging, comparing, filtering and explaining the results demands appropriate software. RESULTS: We developed isma (integrative somatic mutation analysis), an R package for the integrative analysis of somatic mutations detected by multiple pipelines for matched tumor-normal samples. The package provides a series of functions to quantify the consensus, estimate the variability, underline outliers, integrate evidences from publicly available mutation catalogues and filter sites. We illustrate the capabilities of isma analysing breast cancer somatic mutations generated by The Cancer Genome Atlas (TCGA) using four pipelines. CONCLUSIONS: Comparing different "points of view" on the same data, isma generates a unique mutation catalogue and a series of reports that underline common patterns, variability, as well as sites already catalogued by other studies (e.g. TCGA), so as to design and apply filtering strategies to screen more reliable sites. The package is available for non-commercial users at the URL https://www.itb.cnr.it/isma .

Network-Based Integrative Analysis of Genomics, Epigenomics and Transcriptomics in Autism Spectrum Disorders.

Current studies suggest that autism spectrum disorders (ASDs) may be caused by many genetic factors. In fact, collectively considering multiple studies aimed at characterizing the basic pathophysiology of ASDs, a large number of genes has been proposed. Addressing the problem of molecular data interpretation using gene networks helps to explain genetic heterogeneity in terms of shared pathways. Besides, the integrative analysis of multiple omics has emerged as an approach to provide a more comprehensive view of a disease. In this work, we carry out a network-based meta-analysis of the genes reported as associated with ASDs by studies that involved genomics, epigenomics, and transcriptomics. Collectively, our analysis provides a prioritization of the large number of genes proposed to be associated with ASDs, based on genes' relevance within the intracellular circuits, the strength of the supporting evidence of association with ASDs, and the number of different molecular alterations affecting genes. We discuss the presence of the prioritized genes in the SFARI (Simons Foundation Autism Research Initiative) database and in gene networks associated with ASDs by other investigations. Lastly, we provide the full results of our analyses to encourage further studies on common targets amenable to therapy.

Systems medicine of inflammaging.

Systems Medicine (SM) can be defined as an extension of Systems Biology (SB) to Clinical-Epidemiological disciplines through a shifting paradigm, starting from a cellular, toward a patient centered framework. According to this vision, the three pillars of SM are Biomedical hypotheses, experimental data, mainly achieved by Omics technologies and tailored computational, statistical and modeling tools. The three SM pillars are highly interconnected, and their balancing is crucial. Despite the great technological progresses producing huge amount of data (Big Data) and impressive computational facilities, the Bio-Medical hypotheses are still of primary importance. A paradigmatic example of unifying Bio-Medical theory is the concept of Inflammaging. This complex phenotype is involved in a large number of pathologies and patho-physiological processes such as aging, age-related diseases and cancer, all sharing a common inflammatory pathogenesis. This Biomedical hypothesis can be mapped into an ecological perspective capable to describe by quantitative and predictive models some experimentally observed features, such as microenvironment, niche partitioning and phenotype propagation. In this article we show how this idea can be supported by computational methods useful to successfully integrate, analyze and model large data sets, combining cross-sectional and longitudinal information on clinical, environmental and omics data of healthy subjects and patients to provide new multidimensional biomarkers capable of distinguishing between different pathological conditions, e.g. healthy versus unhealthy state, physiological versus pathological aging.

Methods for the integration of multi-omics data: mathematical aspects.

BACKGROUND: Methods for the integrative analysis of multi-omics data are required to draw a more complete and accurate picture of the dynamics of molecular systems. The complexity of biological systems, the technological limits, the large number of biological variables and the relatively low number of biological samples make the analysis of multi-omics datasets a non-trivial problem. RESULTS AND CONCLUSIONS: We review the most advanced strategies for integrating multi-omics datasets, focusing on mathematical and methodological aspects.

Stochastic neutral modelling of the Gut Microbiota's relative species abundance from next generation sequencing data.

BACKGROUND: Interest in understanding the mechanisms that lead to a particular composition of the Gut Microbiota is highly increasing, due to the relationship between this ecosystem and the host health state. Particularly relevant is the study of the Relative Species Abundance (RSA) distribution, that is a component of biodiversity and measures the number of species having a given number of individuals. It is the universal behaviour of RSA that induced many ecologists to look for theoretical explanations. In particular, a simple stochastic neutral model was proposed by Volkov et al. relying on population dynamics and was proved to fit the coral-reefs and rain forests RSA. Our aim is to ascertain if this model also describes the Microbiota RSA and if it can help in explaining the Microbiota plasticity. RESULTS: We analyzed 16S rRNA sequencing data sampled from the Microbiota of three different animal species by Jeraldo et al. Through a clustering procedure (UCLUST), we built the Operational Taxonomic Units. These correspond to bacterial species considered at a given phylogenetic level defined by the similarity threshold used in the clustering procedure. The RSAs, plotted in the form of Preston plot, were fitted with Volkov's model. The model fits well the Microbiota RSA, except in the tail region, that shows a deviation from the neutrality assumption. Looking at the model parameters we were able to discriminate between different animal species, giving also a biological explanation. Moreover, the biodiversity estimator obtained by Volkov's model also differentiates the animal species and is in good agreement with the first and second order Hill's numbers, that are common evenness indexes simply based on the fraction of individuals per species. CONCLUSIONS: We conclude that the neutrality assumption is a good approximation for the Microbiota dynamics and the observation that Volkov's model works for this ecosystem is a further proof of the RSA universality. Moreover, the ability to separate different animals with the model parameters and biodiversity number are promising results if we think about future applications on human data, in which the Microbiota composition and biodiversity are in close relationships with a variety of diseases and life-styles.

Genetic Contribution to Medium-Term Disease Activity in Multiple Sclerosis.

Multiple sclerosis (MS) is a complex disorder characterized by high heterogeneity in terms of phenotypic expression, prognosis and treatment response. In the present study, we aimed to explore the genetic contribution to MS disease activity at different levels: genes, pathways and tissue-specific networks. Two cohorts of relapsing-remitting MS patients who started a first-line treatment (n = 1294) were enrolled to evaluate the genetic association with disease activity after 4 years of follow-up. The analyses were performed at whole-genome SNP and gene level, followed by the construction of gene-gene interaction networks specific for brain and lymphocytes. The resulting gene modules were evaluated to highlight key players from a topological and functional perspective. We identified 23 variants and 223 genes with suggestive association to 4-years disease activity, highlighting genes like PON2 involved in oxidative stress and in mitochondria functions and other genes, like ILRUN, involved in the modulation of the immune system. Network analyses led to the identification of a brain module composed of 228 genes and a lymphocytes module composed of 287 genes. The network analysis allowed us to prioritize genes relevant for their topological properties; among them, there are MPHOSPH9 (connector hub in both brain and lymphocyte module) and OPA1 (in brain module), two genes already implicated in MS. Modules showed the enrichment of both shared and tissue-specific pathways, mainly implicated in inflammation. In conclusion, our results suggest that the processes underlying disease activity act on shared mechanisms across brain and lymphocyte tissues.

Hepatic and adipose tissue transcriptome analysis highlights a commonly deregulated autophagic pathway in severe MASLD.

OBJECTIVE: The incidence of metabolic dysfunction-associated steatotic liver disease (MASLD) is rapidly ramping up due to the spread of obesity, which is characterized by expanded and dysfunctional visceral adipose tissue (VAT). Previous studies have investigated the hepatic transcriptome across MASLD, whereas few studies have focused on VAT. METHODS: We performed RNA sequencing in 167 hepatic samples from patients with obesity and in a subset of 79 matched VAT samples. Circulating cathepsin D (CTSD), a lysosomal protease, was measured by ELISA, whereas the autophagy-lysosomal pathway was assessed by Western blot in hepatic and VAT samples (n = 20). RESULTS: Inflammation, extracellular matrix remodeling, and mitochondrial dysfunction were upregulated in severe MASLD in both tissues, whereas autophagy and oxidative phosphorylation were reduced. Tissue comparative analysis revealed 13 deregulated genes, including CTSD, which showed the most robust diagnostic accuracy in discriminating mild and severe MASLD. CTSD expression correlated with circulating protein, whose increase was further validated in 432 histologically characterized MASLD patients, showing a high accuracy in foreseeing severe liver injury. In addition, the assessment of serum CTSD increased the performance of fibrosis 4 in diagnosing advanced disease. CONCLUSIONS: By comparing the hepatic and VAT transcriptome during MASLD, we refined the concept by which CTSD may represent a potential biomarker of severe disease.

SNPranker 2.0: a gene-centric data mining tool for diseases associated SNP prioritization in GWAS.

BACKGROUND: The capability of correlating specific genotypes with human diseases is a complex issue in spite of all advantages arisen from high-throughput technologies, such as Genome Wide Association Studies (GWAS). New tools for genetic variants interpretation and for Single Nucleotide Polymorphisms (SNPs) prioritization are actually needed. Given a list of the most relevant SNPs statistically associated to a specific pathology as result of a genotype study, a critical issue is the identification of genes that are effectively related to the disease by re-scoring the importance of the identified genetic variations. Vice versa, given a list of genes, it can be of great importance to predict which SNPs can be involved in the onset of a particular disease, in order to focus the research on their effects. RESULTS: We propose a new bioinformatics approach to support biological data mining in the analysis and interpretation of SNPs associated to pathologies. This system can be employed to design custom genotyping chips for disease-oriented studies and to re-score GWAS results. The proposed method relies (1) on the data integration of public resources using a gene-centric database design, (2) on the evaluation of a set of static biomolecular annotations, defined as features, and (3) on the SNP scoring function, which computes SNP scores using parameters and weights set by users. We employed a machine learning classifier to set default feature weights and an ontological annotation layer to enable the enrichment of the input gene set. We implemented our method as a web tool called SNPranker 2.0 (http://www.itb.cnr.it/snpranker), improving our first published release of this system. A user-friendly interface allows the input of a list of genes, SNPs or a biological process, and to customize the features set with relative weights. As result, SNPranker 2.0 returns a list of SNPs, localized within input and ontologically enriched genes, combined with their prioritization scores. CONCLUSIONS: Different databases and resources are already available for SNPs annotation, but they do not prioritize or re-score SNPs relying on a-priori biomolecular knowledge. SNPranker 2.0 attempts to fill this gap through a user-friendly integrated web resource. End users, such as researchers in medical genetics and epidemiology, may find in SNPranker 2.0 a new tool for data mining and interpretation able to support SNPs analysis. Possible scenarios are GWAS data re-scoring, SNPs selection for custom genotyping arrays and SNPs/diseases association studies.

A novel gene signature to diagnose MASLD in metabolically unhealthy obese individuals.

Visceral adipose tissue (VAT) contributes to metabolic dysfunction-associated steatotic liver disease (MASLD), releasing lipogenic substrates and cytokines which promote inflammation. Metabolic healthy obese individuals (MHO) may shift towardsunhealthy ones (MUHO) who develop MASLD, although the mechanisms are still unexplained. Therefore, we aimed to identify dysfunctional pathways and transcriptomic signatures shared by liver and VAT and to outline novel obesity-related biomarkers which feature MASLD in MUHO subjects, at higher risk of progressive liver disease and extrahepatic comorbidities. We performed RNA-sequencing in 167 hepatic samples and in a subset of 79 matched VAT, stratified in MHO and MUHO. A validation analysis was performed in hepatic samples and primary adipocytes from 12 bariatric patients, by qRT-PCR and western blot. We identified a transcriptomic signature that discriminate MUHO vs MHO, including 498 deregulated genes in liver and 189 in VAT. According to pathway and network analyses, oxidative phosphorylation resulted the only significantly downregulated pathway in both tissues in MUHO subjects. Next, we highlighted 5 genes commonly deregulated in liver and VAT, encompassing C6, IGF1, OXA1L, NDUFB11 and KLHL5 and we built a tissue-related score by integrating their expressions. Accordingly to RNAseq data, serum levels of C6 and IGF1, which are the only secreted proteins among those included in the gene signature were downregulated in MUHO vs MHO. Finally, the expression pattern of this 5-genes was confirmed in hepatic and VAT samples. We firstly identified the liver and VAT transcriptional phenotype of MUHO and a gene signature associated with the presence of MASLD in these at risk individuals.

Analysis of Faecal Microbiota and Small ncRNAs in Autism: Detection of miRNAs and piRNAs with Possible Implications in Host-Gut Microbiota Cross-Talk.

Intestinal microorganisms impact health by maintaining gut homeostasis and shaping the host immunity, while gut dysbiosis associates with many conditions, including autism, a complex neurodevelopmental disorder with multifactorial aetiology. In autism, gut dysbiosis correlates with symptom severity and is characterised by a reduced bacterial variability and a diminished beneficial commensal relationship. Microbiota can influence the expression of host microRNAs that, in turn, regulate the growth of intestinal bacteria by means of bidirectional host-gut microbiota cross-talk. We investigated possible interactions among intestinal microbes and between them and host transcriptional modulators in autism. To this purpose, we analysed, by "omics" technologies, faecal microbiome, mycobiome, and small non-coding-RNAs (particularly miRNAs and piRNAs) of children with autism and neurotypical development. Patients displayed gut dysbiosis related to a reduction of healthy gut micro- and mycobiota as well as up-regulated transcriptional modulators. The targets of dysregulated non-coding-RNAs are involved in intestinal permeability, inflammation, and autism. Furthermore, microbial families, underrepresented in patients, participate in the production of human essential metabolites negatively influencing the health condition. Here, we propose a novel approach to analyse faeces as a whole, and for the first time, we detected miRNAs and piRNAs in faecal samples of patients with autism.

A novel expressed prostatic secretion (EPS)-urine metabolomic signature for the diagnosis of clinically significant prostate cancer.

OBJECTIVE: Significant efforts are currently being made to identify novel biomarkers for the diagnosis and risk stratification of prostate cancer (PCa). Metabolomics can be a very useful approach in biomarker discovery because metabolites are an important read-out of the disease when characterized in biological samples. We aimed to determine a metabolomic signature which can accurately distinguish men with clinically significant PCa from those affected by benign prostatic hyperplasia (BPH). METHODS: We first performed untargeted metabolomics using ultrahigh-performance liquid chromatography tandem mass spectrometry on expressed prostatic secretion urine (EPS-urine) from 25 patients affected by BPH and 25 men with clinically significant PCa (defined as Gleason score ≥ 3 + 4). Diagnosis was histologically confirmed after surgical treatment. The EPS-urine metabolomic approach was then applied to a larger, prospective cohort of 92 consecutive patients undergoing multiparametric magnetic resonance imaging for clinical suspicion of PCa prior to biopsy. RESULTS: We established a novel metabolomic signature capable of accurately distinguishing PCa from benign tissue. A metabolomic signature was associated with clinically significant PCa in all subgroups of the Prostate Imaging Reporting and Data System (PI-RADS) classification (100% and 89.13% of accuracy when the PI-RADS was in range of 1-2 and 4-5, respectively, and 87.50% in the more critical cases when the PI-RADS was 3). CONCLUSIONS: A combination of metabolites and clinical variables can effectively help in identifying PCa patients that might be overlooked by current imaging technologies. Metabolites from EPS-urine should help in defining the diagnostic pathway of PCa, thus improving PCa detection and decreasing the number of unnecessary prostate biopsies.

Multi-omic analyses in Abyssinian cats with primary renal amyloid deposits.

The amyloidoses constitute a group of diseases occurring in humans and animals that are characterized by abnormal deposits of aggregated proteins in organs, affecting their structure and function. In the Abyssinian cat breed, a familial form of renal amyloidosis has been described. In this study, multi-omics analyses were applied and integrated to explore some aspects of the unknown pathogenetic processes in cats. Whole-genome sequences of two affected Abyssinians and 195 controls of other breeds (part of the 99 Lives initiative) were screened to prioritize potential disease-associated variants. Proteome and miRNAome from formalin-fixed paraffin-embedded kidney specimens of fully necropsied Abyssinian cats, three affected and three non-amyloidosis-affected were characterized. While the trigger of the disorder remains unclear, overall, (i) 35,960 genomic variants were detected; (ii) 215 and 56 proteins were identified as exclusive or overexpressed in the affected and control kidneys, respectively; (iii) 60 miRNAs were differentially expressed, 20 of which are newly described. With omics data integration, the general conclusions are: (i) the familial amyloid renal form in Abyssinians is not a simple monogenic trait; (ii) amyloid deposition is not triggered by mutated amyloidogenic proteins but is a mix of proteins codified by wild-type genes; (iii) the form is biochemically classifiable as AA amyloidosis.

Transmembrane Protein TMEM230, a Target of Glioblastoma Therapy.

Glioblastomas (GBM) are the most aggressive tumors originating in the brain. Histopathologic features include circuitous, disorganized, and highly permeable blood vessels with intermittent blood flow. These features contribute to the inability to direct therapeutic agents to tumor cells. Known targets for anti-angiogenic therapies provide minimal or no effect in overall survival of 12-15 months following diagnosis. Identification of novel targets therefore remains an important goal for effective treatment of highly vascularized tumors such as GBM. We previously demonstrated in zebrafish that a balanced level of expression of the transmembrane protein TMEM230/C20ORF30 was required to maintain normal blood vessel structural integrity and promote proper vessel network formation. To investigate whether TMEM230 has a role in the pathogenesis of GBM, we analyzed its prognostic value in patient tumor gene expression datasets and performed cell functional analysis. TMEM230 was found necessary for growth of U87-MG cells, a model of human GBM. Downregulation of TMEM230 resulted in loss of U87 migration, substratum adhesion, and re-passaging capacity. Conditioned media from U87 expressing endogenous TMEM230 induced sprouting and tubule-like structure formation of HUVECs. Moreover, TMEM230 promoted vascular mimicry-like behavior of U87 cells. Gene expression analysis of 702 patients identified that TMEM230 expression levels distinguished high from low grade gliomas. Transcriptomic analysis of patients with gliomas revealed molecular pathways consistent with properties observed in U87 cell assays. Within low grade gliomas, elevated TMEM230 expression levels correlated with reduced overall survival independent from tumor subtype. Highest level of TMEM230 correlated with glioblastoma and ATP-dependent microtubule kinesin motor activity, providing a direction for future therapeutic intervention. Our studies support that TMEM230 has both glial tumor and endothelial cell intracellular and extracellular functions. Elevated levels of TMEM230 promote glial tumor cell migration, extracellular scaffold remodeling, and hypervascularization and abnormal formation of blood vessels. Downregulation of TMEM230 expression may inhibit both low grade glioma and glioblastoma tumor progression and promote normalization of abnormally formed blood vessels. TMEM230 therefore is both a promising anticancer and antiangiogenic therapeutic target for inhibiting GBM tumor cells and tumor-driven angiogenesis.

Classification and Personalized Prognostic Assessment on the Basis of Clinical and Genomic Features in Myelodysplastic Syndromes.

PURPOSE: Recurrently mutated genes and chromosomal abnormalities have been identified in myelodysplastic syndromes (MDS). We aim to integrate these genomic features into disease classification and prognostication. METHODS: We retrospectively enrolled 2,043 patients. Using Bayesian networks and Dirichlet processes, we combined mutations in 47 genes with cytogenetic abnormalities to identify genetic associations and subgroups. Random-effects Cox proportional hazards multistate modeling was used for developing prognostic models. An independent validation on 318 cases was performed. RESULTS: We identify eight MDS groups (clusters) according to specific genomic features. In five groups, dominant genomic features include splicing gene mutations (SF3B1, SRSF2, and U2AF1) that occur early in disease history, determine specific phenotypes, and drive disease evolution. These groups display different prognosis (groups with SF3B1 mutations being associated with better survival). Specific co-mutation patterns account for clinical heterogeneity within SF3B1- and SRSF2-related MDS. MDS with complex karyotype and/or TP53 gene abnormalities and MDS with acute leukemia-like mutations show poorest prognosis. MDS with 5q deletion are clustered into two distinct groups according to the number of mutated genes and/or presence of TP53 mutations. By integrating 63 clinical and genomic variables, we define a novel prognostic model that generates personally tailored predictions of survival. The predicted and observed outcomes correlate well in internal cross-validation and in an independent external cohort. This model substantially improves predictive accuracy of currently available prognostic tools. We have created a Web portal that allows outcome predictions to be generated for user-defined constellations of genomic and clinical features. CONCLUSION: Genomic landscape in MDS reveals distinct subgroups associated with specific clinical features and discrete patterns of evolution, providing a proof of concept for next-generation disease classification and prognosis.

Overlapping genes may control reprogramming of mouse somatic cells into induced pluripotent stem cells (iPSCs) and breast cancer stem cells.

Recent findings suggest the possibility that tumors originate from cancer cells with stem cell properties. The cancer stem cell (CSC) hypothesis provides an explanation for why existing cancer therapies often fail in eradicating highly malignant tumors and end with tumor recurrence. Although normal stem cells and CSCs both share the capacity for self-renewal and multi-lineage differentiation, suggesting that CSC may be derived from normal SCs, the cellular origin of transformation of CSCs is debatable. Research suggests that the tightly controlled balance of self-renewal and differentiation that characterizes normal stem cell function is dis-regulated in cancer. Additionally, recent evidence has linked an embryonic stem cell (ESC)-like gene signature with poorly differentiated high-grade tumors, suggesting that regulatory pathways controlling pluripotency may in part contribute to the somatic CSC phenotype. Here, we introduce expression profile bioinformatic analyses of mouse breast cells with CSC properties, mouse embryonic stem (mES) and induced pluripotent stem (iPS) cells with an emphasis on how study of pluripotent stem cells may contribute to the identification of genes and pathways that facilitate events associated with oncogenesis. Global gene expression analysis from CSCs and induced pluripotent stem cell lines represent an ideal model to study cancer initiation and progression and provide insight into the origin cancer stem cells. Additionally, insight into the genetic and epigenomic mechanisms regulating the balance between self-renewal and differentiation of somatic stem cells and cancer may help to determine whether different strategies used to generate iPSCs are potentially safe for therapeutic use.