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Glioblastoma (GBM) is resistant to multimodality therapeutic approaches. A high burden of tumor-specific mutant peptides (neoantigens) correlates with better survival and response to immunotherapies in selected solid tumors but how neoantigens impact clinical outcome in GBM remains unclear. Here, we exploit the similarity between tumor neoantigens and infectious disease-derived immune epitopes and apply a neoantigen fitness model for identifying high-quality neoantigens in a human pan-glioma dataset. We find that the neoantigen quality fitness model stratifies GBM patients with more favorable clinical outcome and, together with CD8+ T lymphocytes tumor infiltration, identifies a GBM subgroup with the longest survival, which displays distinct genomic and transcriptomic features. Conversely, neither tumor neoantigen burden from a quantitative model nor the isolated enrichment of CD8+ T lymphocytes were able to predict survival of GBM patients. This approach may guide optimal stratification of GBM patients for maximum response to immunotherapy., Jing Zhang et al. analyze glioblastoma patient data with a computational model integrating mutation-derived neoantigens similar to pathogen-specific antigens with enrichment of signatures for individual immune cells. Using a fitness model for identifying high-quality neoantigens and T lymphocyte infiltrates in glioblastoma, they uncover a subset of patients with prolonged survival.

When administered to rats receiving a high-fat diet (HFD), 3,5-diiodo-L-thyronine (3,5-T2) [at a dose of 25 &mu, g/100 g body weight (BW)] is known to increase energy expenditure and to prevent HFD-induced adiposity. Here, we investigated which cellular and molecular processes in visceral white adipose tissue (VAT) contributed to the beneficial effect of 3,5-T2 over time (between 1 day and 4 weeks following administration). 3,5-T2 programmed the adipocyte for lipolysis by rapidly inducing hormone sensitive lipase (HSL) phosphorylation at the protein kinase A-responsive site Ser563, accompanied with glycerol release at the 1-week time-point, contributing to the partial normalization of adipocyte volume with respect to control (N) animals. After two weeks, when the adipocyte volumes of HFD-3,5-T2 rats were completely normalized to those of the controls (N), 3,5-T2 consistently induced HSL phosphorylation at Ser563, indicative of a combined effect of 3,5-T2-induced adipose lipolysis and increasing non-adipose oxidative metabolism. VAT proteome analysis after 4 weeks of treatment revealed that 3,5-T2 significantly altered the proteomic profile of HFD rats and produced a marked pro-angiogenic action. This was associated with a reduced representation of proteins involved in lipid storage or related to response to oxidative stress, and a normalization of the levels of those involved in lipogenesis-associated mitochondrial function. In conclusion, the prevention of VAT mass-gain by 3,5-T2 occurred through different molecular pathways that, together with the previously reported stimulation of resting metabolism and liver fatty acid oxidation, are associated with an anti adipogenic/lipogenic potential and positively impact on tissue health.

The progressive and physiological decline in ovarian function depends on the rate of follicular loss by atresia, contributing to the reduction in ovarian reserve. Genetics and environmental factors play important roles in ovarian senescence and in the onset of ovarian dysfunctions such as diminished ovarian reserve. A better understanding of the mechanisms underlying ovarian aging and their regulation by genetic and environmental factors is needed to evaluate ovarian reserve and to predict fertility potential by identification of more accurate and less invasive markers. We report transcriptomic data (i) implicating novel (e.g. EIF2 signalling) and well-known pathways (e.g. TGFβ signalling), and (ii) defining a unique set of non-coding RNA (ncRNA), both associated with ovarian function. The latter includes miRNAs (e.g. Mir143 and Mir145), snoRNAs (e.g. Snord16a and Snora34), and one lncRNA (Gas5), which are differentially expressed in middle-aged ovaries (12 months) vs young-aged (3 months) from CD1 mice. Experimental analysis confirms that ovary lifespan varies across genetic backgrounds in mice and, genetics influences the response to environmental perturbations such as diet. Moreover, the identified ncRNAs were verified in a model of reproductive dysfunction promoted by the environmental toxicant ethylenthiourea. We also report the increase of miRNA143 and miRNA145 in follicular fluid of women with diminished ovarian reserve. Their levels inversely correlate with the hormonal profile and with the number of the oocytes recruited upon hormonal stimulation. Overall, we report a transcriptomic signature for ovarian dysfunction in vivo that provides a valuable resource for translational research in human reproductive aging.

Highlights • We have identified 201 altered chromosomal bands and 3300 altered genes in human ovarian cancer samples. • The gene encoding for PDCD10 was selected for further studies. • PDCD10 was found to be over-expressed in primary cancer samples and in the corresponding metastatic lesions. • High PDCD10 expression correlates with grade, nodal involvement or advanced FIGO stage. • PDCD10 is involved in the control of cell growth and motility in vitro as well as tumorigenicity in vivo., Copy Number Alterations (CNAs) represent the most common genetic alterations identified in ovarian cancer cells, being responsible for the extensive genomic instability observed in this cancer. Here we report the identification of CNAs in a cohort of Italian patients affected by ovarian cancer performed by SNP-based array. Our analysis allowed the identification of 201 significantly altered chromosomal bands (70 copy number gains; 131 copy number losses). The 3300 genes subjected to CNA identified here were compared to those present in the TCGA dataset. The analysis allowed the identification of 11 genes with increased CN and mRNA expression (PDCD10, EBAG9, NUDCD1, ENY2, CSNK2A1, TBC1D20, ZCCHC3, STARD3, C19orf12, POP4, UQCRFS1). PDCD10 was selected for further studies because of the highest frequency of CNA. PDCD10 was found, by immunostaining of three different Tissue Micro Arrays, to be over-expressed in the majority of ovarian primary cancer samples and in metastatic lesions. Moreover, significant correlations were found in specific subsets of patients, between increased PDCD10 expression and grade (p

Background Breast carcinoma with extensive peritumoral vascular invasion (ePVI-BC) is a cancer with massive vascular invasion (>10) detected in more than one slide. This neoplasm shows clinic-pathological affinity with inflammatory breast carcinoma (IBC). In this paper we evaluate their biological relationship through the study of surrogate markers (β-catenin and NFAT5) of Canonical (cWnt) and non-canonical (nWnt) Wnt pathways activation. Methods By immunoistochemistry, we investigate β-catenin and NFAT5 in 39 IBC, 74 ePVI-BC and 84 control cases (CG-BC). Results cWnt was activated in 100 % of ePVI-BC, in 64 % of IBC and 10 % of CG-BC. nWnt was activated in 20 % of ePVI-BC, 50 % of IBC and 1% of CG-BC. The prognosis of carcinomas with nWnt activated was poor similar to IBC. The statistical analysis evidences as both the pathways are synergistic in malignant progression and survival time. β-catenin show an important association with prognostic factors and NFAT5 shows a relevant prognostic role on OS (p = 1.5*10−6) and DFS (P = 1,2*10-4). nWnt is associated with a worse prognosis independently of cWnt. cWnt is associated with adverse prognosis (DFS p = 0.0469; OS p = 0.004891) but its prognostic role is indifferent in carcinoma with nWnt activated. Conclusions Canonical Wnt pathway is involved in malignant progression with dominant role for vascular invasion whereas non canonical Wnt pathway plays an important role on survival time including the capacity to identify carcinomas with IBC-like prognosis. Furthermore ePVI may represent a “prodromal form of IBC” as demonstrated by its clinicopathological and biological similarity with IBC.

Simple Summary Laryngeal Squamous Cell Carcinoma (LSCC) is a leading cause of cancer-related death with a strong interest in identifying and developing new treatments. MicroRNAs (miRNAs) have emerged as one of the most important determinants of neoplastic transformation and progression. miRNA modulation causes significant antitumor effects both in vitro and in vivo and miRNA regulation by natural compounds, represents a promising approach in the field of cancer research. S-Adenosylmethionine (AdoMet), a natural compound and a nutritional supplement, is well known for its antiproliferative and pro-apoptotic effects in many kinds of human tumors. Here, we report that AdoMet induces ER-stress and autophagy paralleled by miR-888-5p downregulation and MYCBP and CDH1 increased expression in Laryngeal Squamous Cancer Cells (LSCC). This study contributes to understanding the mechanisms by which AdoMet exerts its effects in LSCC, suggesting the use of AdoMet as an attractive miRNA-mediated chemopreventive and therapeutic strategy against cancer. Abstract (1) Purpose: The methyl donor S-Adenosylmethionine (AdoMet) has been widely explored as a therapeutic compound, and its application-alone or in combination with other molecules-is emerging as a potential effective strategy for the treatment and chemoprevention of tumours. In this study, we investigated the antitumor activity of AdoMet in Laryngeal Squamous Cell Carcinoma (LSCC), exploring the underlying mechanisms. (2) Results: We demonstrated that AdoMet induced ROS generation and triggered autophagy with a consistent increase in LC3B-II autophagy-marker in JHU-SCC-011 and HNO210 LSCC cells. AdoMet induced ER-stress and activated UPR signaling through the upregulation of the spliced form of XBP1 and CHOP. To gain new insights into the molecular mechanisms underlying the antitumor activity of AdoMet, we evaluated the regulation of miRNA expression profile and we found a downregulation of miR-888-5p. We transfected LSCC cells with miR-888-5p inhibitor and exposed the cells to AdoMet for 48 and 72 h. The combination of AdoMet with miR-888-5p inhibitor synergistically induced both apoptosis and inhibited cell migration paralleled by the up-regulation of MYCBP and CDH1 genes and of their targets. (3) Conclusion: Overall, these data highlighted that epigenetic reprogramming of miRNAs by AdoMet play an important role in inhibiting apoptosis and migration in LSCC cell lines.

The balance between immune-stimulatory and immune-suppressive mechanisms in the tumour microenvironment is associated with tumour rejection and can predict the efficacy of immune checkpoint-inhibition therapies. We consider the observed differences between the transcriptional programmes associated with cancer types where the levels of immune infiltration predict a favourable prognosis versus those in which the immune infiltration predicts an unfavourable prognosis and defined a score named Mediators of Immune Response Against Cancer in soLid microEnvironments (MIRACLE). MIRACLE deconvolves T cell infiltration, from inhibitory mechanisms, such as TGFβ, EMT and PI3Kγ signatures. Our score outperforms current state-of-the-art immune signatures as a predictive marker of survival in TCGA (n = 9305, HR: 0.043, p value: 6.7 × 10−36). In a validation cohort (n = 7623), MIRACLE predicts better survival compared to other immune metrics (HR: 0.1985, p value: 2.73 × 10−38). MIRACLE also predicts response to checkpoint-inhibitor therapies (n = 333). The tumour-intrinsic factors inversely associated with the reported score such as EGFR, PRKAR1A and MAP3K1 are frequently associated with immune-suppressive phenotypes. The association of cancer outcome with the level of infiltrating immune cells is mediated by the balance of activatory and suppressive factors. MIRACLE accounts for this balance and predicts favourable cancer outcomes.

Correlative studies in humans have demonstrated that an active immune microenvironment characterized by the presence of a T-helper 1 immune response typifies a tumor phenotype associated with better outcome and increased responsiveness to immune manipulation. This phenotype also signifies the counter activation of immune-regulatory mechanisms. Variables modulating the development of an effective anti-tumor immune response are increasingly scrutinized as potential therapeutic targets. Genetic alterations of cancer cells that functionally influence intratumoral immune response include mutational load, specific mutations of genes involved in oncogenic pathways and copy number aberrations involving chemokine and cytokine genes. Inhibiting oncogenic pathways that prevent the development of the immune-favorable cancer phenotype may complement modern immunotherapeutic approaches.

Background Long non-coding RNAs (lncRNAs) represent a novel class of non-coding RNAs having a crucial role in many biological processes. The identification of long non-coding homologs among different species is essential to investigate such roles in model organisms as homologous genes tend to retain similar molecular and biological functions. Alignment–based metrics are able to effectively capture the conservation of transcribed coding sequences and then the homology of protein coding genes. However, unlike protein coding genes the poor sequence conservation of long non-coding genes makes the identification of their homologs a challenging task. Results In this study we compare alignment–based and alignment–free string similarity metrics and look at promoter regions as a possible source of conserved information. We show that promoter regions encode relevant information for the conservation of long non-coding genes across species and that such information is better captured by alignment–free metrics. We perform a genome wide test of this hypothesis in human, mouse, and zebrafish. Conclusions The obtained results persuaded us to postulate the new hypothesis that, unlike protein coding genes, long non-coding genes tend to preserve their regulatory machinery rather than their transcribed sequence. All datasets, scripts, and the prediction tools adopted in this study are available at https://github.com/bioinformatics-sannio/lncrna-homologs. Electronic supplementary material The online version of this article (10.1186/s12859-018-2441-6) contains supplementary material, which is available to authorized users.

Summary The molecular characterization of immune subsets is important for designing effective strategies to understand and treat diseases. We characterized 29 immune cell types within the peripheral blood mononuclear cell (PBMC) fraction of healthy donors using RNA-seq (RNA sequencing) and flow cytometry. Our dataset was used, first, to identify sets of genes that are specific, are co-expressed, and have housekeeping roles across the 29 cell types. Then, we examined differences in mRNA heterogeneity and mRNA abundance revealing cell type specificity. Last, we performed absolute deconvolution on a suitable set of immune cell types using transcriptomics signatures normalized by mRNA abundance. Absolute deconvolution is ready to use for PBMC transcriptomic data using our Shiny app (https://github.com/giannimonaco/ABIS). We benchmarked different deconvolution and normalization methods and validated the resources in independent cohorts. Our work has research, clinical, and diagnostic value by making it possible to effectively associate observations in bulk transcriptomics data to specific immune subsets., Graphical Abstract, Highlights • Characterization of 29 human immune cell type by RNA-seq and flow cytometry • Modules of specific, co-expressed, and housekeeping genes are defined • The mRNA heterogeneity and abundance are cell type specific • The proposed normalization approach enables absolute deconvolution, Monaco et al. generate an RNA-seq dataset on 29 immune cell types and identify modules of cell type-specific, co-expressed, and housekeeping genes. The mRNA heterogeneity and abundance of the different cell types were examined. Absolute deconvolution of PBMCs was obtained by taking into account mRNA abundance when normalizing the signature matrix.

Neurofibromatosis type 1 (NF1) is a common tumor predisposition syndrome in which glioma is one of the prevalent tumors. Gliomagenesis in NF1 results in a heterogeneous spectrum of low- to high-grade neoplasms occurring during the entire lifespan of patients. The pattern of genetic and epigenetic alterations of glioma that develops in NF1 patients and the similarities with sporadic glioma remain unknown. Here, we present the molecular landscape of low- and high-grade gliomas in patients affected by NF1 (NF1-glioma). We found that the predisposing germline mutation of the NF1 gene was frequently converted to homozygosity and the somatic mutational load of NF1-glioma was influenced by age and grade. High-grade tumors harbored genetic alterations of TP53 and CDKN2A, frequent mutations of ATRX associated with Alternative Lengthening of Telomere, and were enriched in genetic alterations of transcription/chromatin regulation and PI3 kinase pathways. Low-grade tumors exhibited fewer mutations that were over-represented in genes of the MAP kinase pathway. Approximately 50% of low-grade NF1-gliomas displayed an immune signature, T lymphocyte infiltrates, and increased neo-antigen load. DNA methylation assigned NF1-glioma to LGm6, a poorly defined Isocitrate Dehydrogenase 1 wild-type subgroup enriched with ATRX mutations. Thus, the profiling of NF1-glioma defined a distinct landscape that recapitulates a subset of sporadic tumors. An integrated analysis of glioma samples from patients with neurofibromatosis 1 annotates their mutational, epigenetic, transcriptional, and immunological features and uncovers similitudes with a subset of sporadic gliomas.

We examined how the immune microenvironment molds tumor evolution at different metastatic organs in a longitudinal dataset of colorectal cancer. Through multiplexed analyses, we showed that clonal evolution patterns during metastatic progression depend on the immune contexture at the metastatic site. Genetic evidence of neoantigen depletion was observed in the sites with high Immunoscore and spatial proximity between Ki67+ tumor cells and CD3+ cells. The immunoedited tumor clones were eliminated and did not recur, while progressing clones were immune privileged, despite the presence of tumor-infiltrating lymphocytes. Characterization of immune-privileged metastases revealed tumor-intrinsic and tumor-extrinsic mechanisms of escape. The lowest recurrence risk was associated with high Immunoscore, occurrence of immunoediting, and low tumor burden. We propose a parallel selection model of metastatic progression, where branched evolution could be traced back to immune-escaping clones. The findings could inform the understanding of cancer dissemination and the development of immunotherapeutics.

BackgroundDespite extensive molecular characterization, we lack a comprehensive understanding of lineage identity, differentiation, and proliferation in high-grade gliomas (HGGs). We sampled the cellular milieu of HGGs with massively-parallel single-cell RNA-Seq.ResultsWhile HGG cells can resemble glia or even immature neurons and form branched lineage structures, mesenchymal transformation results in unstructured populations. Glioma cells in a subset of mesenchymal tumors lose their neural lineage identity, express inflammatory genes, and co-exist with marked myeloid infiltration, reminiscent of molecular interactions between glioma and immune cells established in animal models. Additionally, we discovered a tight coupling between lineage resemblance and proliferation among malignantly transformed cells. Glioma cells that resemble oligodendrocyte progenitors, which proliferate in the brain, are often found in the cell cycle. Conversely, glioma cells that resemble astrocytes, neuroblasts, and oligodendrocytes, which are non-proliferative in the brain, are generally non-cycling in tumors.ConclusionsThese studies reveal a relationship between cellular identity and proliferation in HGG and distinct population structures that reflects the extent of neural and non-neural lineage resemblance among malignantly transformed cells.

DNAJC17 is a heat shock protein (HSP40) family member, identified in mouse as susceptibility gene for congenital hypothyroidism. DNAJC17 knockout mouse embryos die prior to implantation. In humans, germline homozygous mutations in DNAJC17 have been found in syndromic retinal dystrophy patients, while heterozygous mutations represent candidate pathogenic events for myeloproliferative disorders. Despite widespread expression and involvement in human diseases, DNAJC17 function is still poorly understood. Herein, we have investigated its function through high-throughput transcriptomic and proteomic approaches. DNAJC17-depleted cells transcriptome highlighted genes involved in general functional categories, mainly related to gene expression. Conversely, DNAJC17 interactome can be classified in very specific functional networks, with the most enriched one including proteins involved in splicing. Furthermore, several splicing-related interactors, were independently validated by co-immunoprecipitation and in vivo co-localization. Accordingly, co-localization of DNAJC17 with SC35, a marker of nuclear speckles, further supported its interaction with spliceosomal components. Lastly, DNAJC17 up-regulation enhanced splicing efficiency of minigene reporter in live cells, while its knockdown induced perturbations of splicing efficiency at whole genome level, as demonstrated by specific analysis of RNAseq data. In conclusion, our study strongly suggests a role of DNAJC17 in splicing-related processes and provides support to its recognized essential function in early development.

The molecular characterization of immune cell types is important for designing and developing effective strategies to understand and treat diseases. We characterized 29 different immune cell types within the peripheral blood mononuclear cell (PBMC) fraction of healthy donors using RNA sequencing (RNA-Seq) and flow cytometry. Our RNA-Seq resource has been used first to identify set of genes that are specific, co-expressed and have housekeeping role across the 29 immune cell types. Then, we analyzed the RNA composition for each immune cell type in terms of their relative mRNA heterogeneity and abundance. Lastly, we develop a novel method of gene expression normalization in order to perform absolute deconvolution at a fine resolution from both RNA-Seq and microarray datasets. The resources were validated in independent cohorts and benchmarked with different deconvolution and normalization methods. We believe that this work has clinical and diagnostic value by allowing us to attribute observations in bulk RNA data to specific immune cell populations.

Following publication of the original article [1], the author reported that an author name, Roberta Zappasodi, was missed in the authorship list. The correction has been implemented in the original article as well. The publisher apologized for any inconvenience this might has caused.

Developers' communication, as contained in emails, issue trackers, and forums, is a precious source of information to support the development process. For example, it can be used to capture knowledge about development practice or about a software project itself. Thus, extracting the content of developers' communication can be useful to support several software engineering tasks, such as program comprehension, source code analysis, and software analytics. However, automating the extraction process is challenging, due to the unstructured nature of free text, which mixes different coding languages (e.g., source code, stack dumps, and log traces) with natural language parts. We conduct an extensive evaluation of Irish (InfoRmation ISlands Hmm), an approach we proposed to extract islands of coded information from free text at token granularity, with respect to the state of art approaches based on island parsing or island parsing combined with machine learners. The evaluation considers a wide set of natural language documents (e.g., textbooks, forum discussions, and development emails) taken from different contexts and encompassing different coding languages. Results indicate an F-measure of Irish between 74% and 99%; this is in line with existing approaches which, differently from Irish , require specific expertise for the definition of regular expressions or grammars.

MotivationBiological networks unravel the inherent structure of molecular interactions which can lead to discovery of driver genes and meaningful pathways especially in cancer context. Often due to gene mutations, the gene expression undergoes changes and the corresponding gene regulatory network sustains some amount of localized re-wiring. The ability to identify significant changes in the interaction patterns caused by the progression of the disease can lead to the revelation of novel relevant signatures.MethodsThe task of identifying differential sub-networks in paired biological networks (A:control,B:case) can be re-phrased as one of finding dense communities in a single noisy differential topological (DT) graph constructed by taking absolute difference between the topological graphs of A and B. In this paper, we propose a fast two-stage approach, namely Differential Community Detection (DCD), to identify differential sub-networks as differential communities in a de-noised version of the DT graph. In the first stage, we iteratively re-order the nodes of the DT graph to determine approximate block diagonals present in the DT adjacency matrix using neighbourhood information of the nodes and Jaccard similarity. In the second stage, the ordered DT adjacency matrix is traversed along the diagonal to remove all the edges associated with a node, if that node has no immediate edges within a window. We then apply community detection methods on this de-noised DT graph to discover differential sub-networks as communities.ResultsOur proposed DCD approach can effectively locate differential sub-networks in several simulated paired random-geometric networks and various paired scale-free graphs with different power-law exponents. The DCD approach easily outperforms community detection methods applied on the original noisy DT graph and recent statistical techniques in simulation studies. We applied DCD method on two real datasets: a) Ovarian cancer dataset to discover differential DNA co-methylation sub-networks in patients and controls; b) Glioma cancer dataset to discover the difference between the regulatory networks of IDH-mutant and IDH-wild-type. We demonstrate the potential benefits of DCD for finding network-inferred bio-markers/pathways associated with a trait of interest.ConclusionThe proposed DCD approach overcomes the limitations of previous statistical techniques and the issues associated with identifying differential sub-networks by use of community detection methods on the noisy DT graph. This is reflected in the superior performance of the DCD method with respect to various metrics like Precision, Accuracy, Kappa and Specificity. The code implementing proposed DCD method is available at https://sites.google.com/site/ raghvendramallmlresearcher/codes.

The transcription factors (TF) which regulate gene expressions are key determinants of cellular phenotypes. Reconstructing large-scale genome-wide networks which capture the influence of TFs on target genes are essential for understanding and accurate modelling of living cells. We propose RGBM: a gene regulatory network (GRN) inference algorithm, which can handle data from heterogeneous information sources including dynamic time-series, gene knockout, gene knockdown, DNA microarrays and RNA-Seq expression profiles. RGBM allows to use an a priori mechanistic of active biding network consisting of TFs and corresponding target genes. RGBM is evaluated on the DREAM challenge datasets where it surpasses the winners of the competitions and other established methods for two evaluation metrics by about 10-15%.We use RGBM to identify the main regulators of the molecular subtypes of brain tumors. Our analysis reveals the identity and corresponding biological activities of the master regulators driving transformation of the G-CIMP-high into the G-CIMP-low subtype of glioma and PA-like into LGm6-GBM, thus, providing a clue to the yet undetermined nature of the transcriptional events driving the evolution among these novel glioma subtypes.RGBM is available for download on CRAN at https://cran.rproject.org/web/packages/RGBM/index.html

International audience; Cancer immunotherapy is revolutionizing the clinical management of several tumors, but has demonstrated limited activity in breast cancer. The development of more effective treatments is hindered by incomplete knowledge of the genetic determinant of immune responsiveness. To fill this gap, we mined copy number alteration, somatic mutation, and expression data from The Cancer Genome Atlas (TCGA). By using RNA-sequencing data from 1,004 breast cancers, we defined distinct immune phenotypes characterized by progressive expression of transcripts previously associated with immune-mediated rejection. The T helper 1 (Th-1) phenotype (ICR4), which also displays upregulation of immune-regulatory transcripts such as PDL1, PD1, FOXP3, IDO1, and CTLA4, was associated with prolonged patients' survival. We validated these findings in an independent meta-cohort of 1,954 breast cancer gene expression data. Chromosome segment 4q21, which includes genes encoding for the Th-1 chemokines CXCL9-11, was significantly amplified only in the immune favorable phenotype (ICR4). The mutation and neoantigen load progressively decreased from ICR4 to ICR1 but could not fully explain immune phenotypic differences. Mutations of TP53 were enriched in the immune favorable phenotype (ICR4). Conversely, the presence of MAP3K1 and MAP2K4 mutations were tightly associated with an immune-unfavorable phenotype (ICR1). Using both the TCGA and the validation dataset, the degree of MAPK deregulation segregates breast tumors according to their immune disposition. These findings suggest that mutation-driven perturbations of MAPK pathways are linked to the negative regulation of intratumoral immune response in breast cancer. Modulations of MAPK pathways could be experimentally tested to enhance breast cancer immune sensitivity.

Background:The GDC (Genomic Data Commons) data portal provides users with data from cancer genomics studies. Recently, we developed the R/Bioconductor TCGAbiolinks package, which allows users to search, download and prepare cancer genomics data for integrative data analysis. The use of this package requires users to have advanced knowledge of R thus limiting the number of users.Results:To overcome this obstacle and improve the accessibility of the package by a wider range of users, we developed TCGAbiolinksGUI that uses shiny graphical user interface (GUI) available through the R/Bioconductor package.Conclusion:The TCGAbiolinksGUI package is freely available within the Bioconductor project at http://bioconductor.org/packages/TCGAbiolinksGUI/. Links to the GitHub repository, a demo version of the tool, a docker image and PDF/video tutorials are available at http://bit.do/TCGAbiolinksDocs.

Objectives: Extensive peritumoral neoplastic lymphovascular invasion (ePVI) is a marker of aggressiveness in invasive breast carcinoma (BC). Methods: We explored the impact of ePVI on different BC subtypes. In a total of 2,116 BCs, 91 ePVI-BCs, 70 inflammatory breast carcinomas (IBCs), and 114 casual BCs as a control group (CG-BC) were recruited. Results: Patients affected by ePVI-BC were younger, had larger tumors, higher histologic grade, elevated Ki-67 score, Her2/neu overexpressed, and more lymph node metastases compared with CG-BC (P < .001). Interestingly, only younger mean age at diagnosis differentiated patients with ePVI-BC from patients affected by IBC. ePVI-BC showed a clinical outcome intermediate between the prognoses of IBC and CG-BC. Conclusions: Results suggest that ePVI-BC and IBC may share some pathologic processes, providing a novel perspective on the heterogeneity of BC. Epidemiologic data and molecular studies on gene expression features are needed to rationally classify these tumors into their identified subtypes.

Over the past decade, growing evidence indicates that the tumor microenvironment (TME) contributes with genomic/epigenomic aberrations of malignant cells to enhance cancer cells survival, invasion, and dissemination. Many factors, produced orde novosynthesized by immune, stromal, or malignant cells, acting in a paracrine and autocrine fashion, remodel TME and the adaptive immune response culminating in metastasis. Taking into account the recent accomplishments in the field of immune oncology and using metastatic colorectal cancer (mCRC) as a model, we propose that the evasion of the immune surveillance and metastatic spread can be achieved through a number of mechanisms that include (a) intrinsic plasticity and adaptability of immune and malignant cells to paracrine and autocrine stimuli or genotoxic stresses; (b) alteration of positional schemes of myeloid-lineage cells, produced by factors controlling the balance between tumour-suppressing and tumour-promoting activities; (c) acquisition by cancer cells of aberrant immune-phenotypic traits (NT5E/CD73, CD68, and CD163) that enhance the interactions among TME components through the production of immune-suppressive mediators. These properties may represent the driving force of metastatic progression and thus clinically exploitable for cancer prevention and therapy. In this review we summarize results and suggest new hypotheses that favour the growing impact of tumor-infiltrating immune cells on tumour progression, metastasis, and therapy resistance.

In vitro Omics analysis (i.e. transcriptome) is suggested to predict in vivo toxicity and adverse effects in humans, although the causal link between high-throughput data and effects in vivo is not easily established. Indeed, the chemical-organism interaction can involve processes, such as adaptation, not established in cell cultures. Starting from this consideration we investigate the transcriptomic response of immortalized thyrocytes to ethylenthiourea and chlorpyrifos. In vitro data revealed specific and common genes/mechanisms of toxicity, controlling the proliferation/survival of the thyrocytes and unrelated hematopoietic cell lineages. These results were phenotypically confirmed in vivo by the reduction of circulating T4 hormone and the development of pancytopenia after long exposure. Our data imply that in vitro toxicogenomics is a powerful tool in predicting adverse effects in vivo, experimentally confirming the vision described as Tox21c (Toxicity Testing in the 21st century) although not fully recapitulating the biocomplexity of a living animal.

Biotechnological advances in sequencing have led to an explosion of publicly available data via large international consortia such as The Cancer Genome Atlas (TCGA), The Encyclopedia of DNA Elements (ENCODE), and The NIH Roadmap Epigenomics Mapping Consortium (Roadmap). These projects have provided unprecedented opportunities to interrogate the epigenome of cultured cancer cell lines as well as normal and tumor tissues with high genomic resolution. The Bioconductor project offers more than 1,000 open-source software and statistical packages to analyze high-throughput genomic data. However, most packages are designed for specific data types (e.g. expression, epigenetics, genomics) and there is no one comprehensive tool that provides a complete integrative analysis of the resources and data provided by all three public projects. A need to create an integration of these different analyses was recently proposed. In this workflow, we provide a series of biologically focused integrative analyses of different molecular data. We describe how to download, process and prepare TCGA data and by harnessing several key Bioconductor packages, we describe how to extract biologically meaningful genomic and epigenomic data. Using Roadmap and ENCODE data, we provide a work plan to identify biologically relevant functional epigenomic elements associated with cancer. To illustrate our workflow, we analyzed two types of brain tumors: low-grade glioma (LGG) versus high-grade glioma (glioblastoma multiform or GBM). This workflow introduces the following Bioconductor packages: AnnotationHub, ChIPSeeker, ComplexHeatmap, pathview, ELMER, GAIA, MINET, RTCGAToolbox, TCGAbiolinks.

Cancer genome instability leads to accumulation of mutations which may result into tumor-specific mutated &ldquo, neoantigens&rdquo, not be affected by central T-cell tolerance. Such neoantigens are considered the optimal target for the patient&rsquo, s anti-tumor T cell immunity as well as for personalized cancer immunotherapy strategies. However, only a minor fraction of predicted neoantigens are relevant to the clinical outcome. In the present study, a prediction algorithm was applied using datasets of RNA sequencing from all 377 Hepatocellular carcinoma (HCC) patients available at The Cancer Genome Atlas (TCGA), to predict neoantigens to be presented by each patient's autologous HLA molecules. Correlation with patients&rsquo, survival was performed on the 115 samples for whom the exact date of death was known. A total of 30 samples were used for the training set, and 85 samples were used for the validation sets. Neither the somatic mutations nor the number nor the quality of the predicted neoantigens correlate as single parameter with survival of HCC patients who do not undergo immunotherapy treatment. Furthermore, the preferential presentation of such neoantigens in the context of one of the major histocompatibility complex MHC class I molecules does not have an impact on the survival. On the contrary, the expression of Granzyme A (GZMA) is significantly correlated with survival and, in the context of high GZMA, a direct correlation between number and quality of neoantigens with survival is observed. This is in striking contrast to results described in cancer patients undergoing immunotherapy, in which a strong correlation between Tumor Mutational Burden (TMB), number of predicted neoantigens and survival has been reported.

The inhibitory effect of the flavonoid naringenin on plant and human Two-Pore Channels (TPCs) was assessed by means of electrophysiological measurements. By acting on human TPC2, naringenin, was able to dampen intracellular calcium responses to VEGF in cultured human endothelial cells and to impair angiogenic activity in VEGF-containing matrigel plugs implanted in mice. Molecular docking predicts selective binding sites for naringenin in the TPC structure, thus suggesting a specific interaction between the flavonoid and the channel.

Predicted neoantigens can be relevant to the clinical outcome only when their affinity to MHC I molecule is significantly higher than the corresponding wild type peptide, and when they do not show any homology to unrelated self antigens (e.g. true predicted neoantigens - TPNAs). In addition, they may show homology to pathogen-derived antigens, representing a “super” neoantigen if the patient is immunologically primed. A novel algorithm for prediction of TPNAs presented by each patient's autologous HLA molecules was applied to datasets of all 377 HCC patients available at The Cancer Genome Atlas (TCGA). The number of neoantigens was highly variable between different HCC patients with an average of 14.5 per sample. However, considering those with the highest predicted antigenic quality, the average of neoantigens per samples with such parameters dropped down to 1.5. Results showed that patients’ survival did not correlate with the quantity nor the quality of neoantigens. However, only when the cytotoxic activity of tumor infiltrating lymphocytes was low, a correlation between neoantigens and survival was observed, suggesting that in this setting a higher number of neoantigens is required for a more pronounced efficacy on the patients’ survival. Predicted neoantigens with homology to unrelated self antigens or pathogen-derived antigens were identified. Nevertheless, a single neoantigen per patient with such a homology did not influence patients’ survival. The same algorithm was applied to a cohort of hepatocellular carcinoma (HCC) patients enrolled at our Institute, confirming the results obtained on the TCGA cohort. Interestingly, TPNAs with highest homology to pathogen’s antigens were found in the only HCC long-term survival patient. A pre-existing T cell immunity specific for these TPNAs was revealed in the patient, possibly explaining the favourable clinical outcome. In conclusion, the new algorithm allowed the identification of the very few TPNAs in HCC samples. The relevance of quantity and quality of neoantigens in the evolution of HCC without treatment with checkpoint inhibitors (CI) appears to be less central, unless the tumor cytotoxic lymphocyte infiltration is low. The identification in a single patient, of a primed immunity against a neoantigen homologous to a pathogen-derived antigen, can possibly explain the long term survival. Therefore, the identification of high quality neoantigens remains a priority for developing personalized active immunotherapies also in the HCC model characterized by a low mutational burden. Note: This abstract was not presented at the meeting. Citation Format: Maria Tagliamonte, Angela Mauriello, Roberta Zeuli, Annacarmen Petrizzo, Maria Lina Tornesello, Michele Ceccarelli, Franco M. Buonaguro, Luigi Buonaguro. Neoantigen load, tumor immune infiltration and prediction of survival in HCC patients [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr 1198.

Malignant glioma (glioblastoma, GBM) is a lethal cancer resistant to multimodality therapeutic approaches. A high burden of tumor-specific mutant peptides (neoantigens) correlates with a more favorable prognosis and response to immunotherapies in selected solid tumors but the role of neoantigens and how they impact clinical outcome in most tumors including GBM remains unclear. Here, we exploited the similarity between tumor neoantigens and infectious disease-derived immune epitopes and applied a neoantigen fitness model for the identification of high quality neoantigens in human GBM. The neoantigen quality fitness model stratified GBM patients with more favorable clinical outcome and, together with tumor infiltration of CD8 T lymphocytes, identified a subgroup of GBM patients with the longest survival, thus exhibiting optimal performance. Conversely, neither tumor neoantigen burden from a quantity model nor the isolated enrichment of CD8 T lymphocytes could predict better survival of GBM patients. The GBM subgroup that emerged from the neoantigen quality model displays distinct genomic and transcriptomic features that should guide patient stratification for immunotherapy and maximal benefit of GBM patients. Citation Format: Jing Zhang, Francesca P. Caruso, Jason K. Sa, Sune Justesen, Do-Hyun Nam, Peter Sims, Michele Ceccarelli, Anna Lasorella, Antonio Iavarone. A high quality neoantigen fitness model cooperates with T lymphocyte infiltrates to identify glioblastoma patients with the longest survival [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr LB-065.

O1 IL-15 primes an mTOR-regulated gene-expression program to prolong anti-tumor capacity of human natural killer cells #### Andreas Lundqvist1, Vincent van Hoef1, Xiaonan Zhang1, Erik Wennerberg2, Julie Lorent1, Kristina Witt1, Laia Masvidal Sanz1, Shuo Liang1, Shannon Murray3, Ola Larsson1

A novel two-step bioinformatics strategy was applied for identification of signatures with therapeutic implications in hepatitis-associated HCC. Transcriptional profiles from HBV- and HCV-associated HCC samples were compared with non-tumor liver controls. Resulting HCC modulated genes were subsequently compared with different non-tumor tissue samples. Two related signatures were identified, namely “HCC-associated” and “HCC-specific”. Expression data were validated by RNA-Seq analysis carried out on unrelated HCC samples and protein expression was confirmed according to The Human Protein Atlas" (http://proteinatlas.org/), a public repository of immunohistochemistry data. Among all, aldo-keto reductase family 1 member B10, and IGF2 mRNA-binding protein 3 were found strictly HCC-specific with no expression in 18/20 normal tissues. Target peptides for vaccine design were predicted for both proteins associated with the most prevalent HLA-class I and II alleles. The described novel strategy showed to be feasible for identification of HCC-specific proteins as highly potential target for HCC immunotherapy.

// Ilaria Guerriero 1 , Daniela D’Angelo 2 , Pierlorenzo Pallante 2 , Mafalda Santos 1 , Marianna Scrima 1 , Donatella Malanga 3 , Carmela De Marco 3 ,Maria Ravo 4 , Alessandro Weisz 4 , Carmelo Laudanna 3 , Michele Ceccarelli 1, 5 , Geppino Falco 1, 6 , Antonia Rizzuto 7 , Giuseppe Viglietto 1, 3 1 Biogems.c.ar.l., Istituto di Ricerche Genetiche “Gaetano Salvatore”, Ariano Irpino, Avellino, Italy 2 Istituto per l’Endocrinologia e l’Oncologia Sperimentale, IEOS-CNR, c/o Dipartimento di Medicina Molecolare e Biotecnologie Mediche, Universita “Federico II” of Napoli, Napoli, Italy 3 Dipartimento di Medicina Sperimentale e Clinica, Universita Magna Graecia of Catanzaro, Italy 4 Laboratorio di Medicina Molecolare e Genomica, Dipartimentodi Medicina e Chirurgia, Universita di Salerno, Baronissi, Italy 5 Department of Biological and Environmental Studies, Universita del Sannio, Benevento, Italy 6 Dipartimento di Biologia, Universita degli Studi di Napoli Federico II, Complesso Universitario Monte S.Angelo, Napoli, Italy 7 Dipartimento di Scienze Mediche e Chirurgiche, Universita Magna Graecia of Catanzaro, Italy Correspondence to: Giuseppe Viglietto, email: viglietto@unicz.it Keywords: NSCLC, PI3K/AKT, microRNA, miR-196a Received: January 11, 2016 Accepted: November 02, 2016 Published: November 17, 2016 ABSTRACT Hyperactivation of the PI3K/AKT pathway is observed in most human cancer including lung carcinomas. Here we have investigated the role of miRNAs as downstream targets of activated PI3K/AKT signaling in Non Small Cell Lung Cancer (NSCLC). To this aim, miRNA profiling was performed in human lung epithelial cells (BEAS-2B) expressing active AKT1 (BEAS-AKT1-E17K), active PI3KCA (BEAS-PIK3CA-E545K) or with silenced PTEN (BEAS-shPTEN). Twenty-four differentially expressed miRNAs common to BEAS-AKT1-E17K, BEAS-PIK3CA-E545K and BEAS-shPTEN cells were identified through this analysis, with miR-196a being the most consistently up-regulated miRNA. Interestingly, miR-196a was significantly overexpressed also in human NSCLC-derived cell lines (n=11) and primary lung cancer samples (n=28). By manipulating the expression of miR-196a in BEAS-2B and NCI-H460 cells, we obtained compelling evidence that this miRNA acts downstream the PI3K/AKT pathway, mediating some of the proliferative, pro-migratory and tumorigenic activity that this pathway exerts in lung epithelial cells, possibly through the regulation of FoxO1, CDKN1B (hereafter p27) and HOXA9.

Therapy development for adult diffuse glioma is hindered by incomplete knowledge of somatic glioma driving alterations and suboptimal disease classification. We defined the complete set of genes associated with 1,122 diffuse grade II-III-IV gliomas from The Cancer Genome Atlas and used molecular profiles to improve disease classification, identify molecular correlations, and provide insights into the progression from low- to high-grade disease. Whole-genome sequencing data analysis determined that ATRX but not TERT promoter mutations are associated with increased telomere length. Recent advances in glioma classification based on IDH mutation and 1p/19q co-deletion status were recapitulated through analysis of DNA methylation profiles, which identified clinically relevant molecular subsets. A subtype of IDH mutant glioma was associated with DNA demethylation and poor outcome; a group of IDH-wild-type diffuse glioma showed molecular similarity to pilocytic astrocytoma and relatively favorable survival. Understanding of cohesive disease groups may aid improved clinical outcomes.

Breast cancer is the most common type of tumor in women in the MENA region and it represents about 20% of the cancers diagnosed every year in Qatar. Although the implementation of cancer therapy has led to an improvement of patients' survival, metastatic breast cancer remains an incurable condition. Immunotherapy is emerging as an innovative therapeutic tool able to cure in some cases established metastatic tumors such as melanoma. There is a growing interest in exploring this fascinating approach in breast cancer. However, little is known about the immune biology of this aggressive disease. Studies from our and other groups have described intratumoral immune gene signatures associated with better responsiveness to immunotherapy and prolonged survival (Galon, Angell, Bedognetti and Marincola, Immunity 2013; PMID: 23890060). In general, these gene signatures imply the activation of interferon stimulated genes (e.g. IRF1 and STAT1), the recruitment of lymphocytes through CXCR3/CCR5 ligand chemokines, and the activation of immune-effector functions such as PRF1 and GZM1 (Wang, Bedognetti, Marincola, JCO 2013; PMID: 23715576). We refer to these genes as the Immunologic-Constant-of-Rejection (ICR) (Bedognetti, Wang, Sertoli, Marincola, Exp Rev Vacc, 2010; PMID: 20518712). The activation of the ICR pathways is accompanied by the counter-activation of immune-regulatory mechanisms such as the expression of PD-L1 and IDO1. Based on these observations, we hypothesize that tumors can be divided in two opposite phenotypes (Bedognetti, Hendrickx, Marincola, Miller, Curr Opin Onc 2015; PMID: 26418235). The first phenotype displays the activation of pro-inflammatory (ICR) and immune-regulatory mechanisms and is characterized by a favorable prognosis and responsiveness to immune manipulations. The second phenotype lacks these two characteristics and is associated by a poor prognosis and resistance to immune manipulations. Whether the development of such different immune phenotypes is influenced by the intrinsic genetics of the tumor cells is presently unclear. The understanding of genetic mechanisms associated with differential immune response can lead the development of targeted approaches. To answer this critical question we analyzed copy number variation, exome and RNA sequencing data from the The Cancer Genome Atlas (TCGA) breast cancer dataset. By using consensus clustering analysis based on RNA-seq data of more than 1000 breast cancer samples, we defined four immunophenotypes characterized by progressive expression of the ICR genes (ICR1

Epidemiological and experimental data highlighted the thyroid-disrupting activity of bisphenol A (BPA). Although pivotal to identify the mechanisms of toxicity, direct low-dose BPA effects on thyrocytes have not been assessed. Here, we report the results of microarray experiments revealing that the transcriptome reacts dynamically to low-dose BPA exposure, adapting the changes in gene expression to the exposure duration. The response involves many genes, enriching specific pathways and biological functions mainly cell death/proliferation or DNA repair. Their expression is only slightly altered but, since they enrich specific pathways, this results in major effects as shown here for transcripts involved in the DNA repair pathway. Indeed, even though no phenotypic changes are induced by the treatment, we show that the exposure to BPA impairs the cell response to further stressors. We experimentally verify that prolonged exposure to low doses of BPA results in a delayed response to UV-C-induced DNA damage, due to impairment of p21-Tp53 axis, with the BPA-treated cells more prone to cell death and DNA damage accumulation. The present findings shed light on a possible mechanism by which BPA, not able to directly cause genetic damage at environmental dose, may exert an indirect genotoxic activity.

Pluripotency confers Embryonic Stem Cells (ESCs) the ability to differentiate in ectoderm, endoderm, and mesoderm derivatives, producing the majority of cell types. Although the majority of ESCs divide without losing pluripotency, it has become evident that ESCs culture consists of multiple cell populations with different degrees of potency that are spontaneously induced in regular ESC culture conditions. Zscan4, a key pluripotency factor, marks ESC subpopulation that is referred to as high-level of pluripotency metastate. Here, we report that in ESC cultures treated with retinoic acid (RA), Zscan4 ESCs metastate is strongly enhanced. In particular, we found that induction of Zscan4 metastate is mediated via RA receptors (RAR-alpha, RAR-beta, and RAR-gamma), and it is dependent on phosphoinositide-3-kinase (PI3K) signaling. Remarkably, Zscan4 metastate induced by RA lacks canonical pluripotency genes Oct3/4 and Nanog but retained both self-renewal and pluripotency capabilities. Finally we demonstrated that the conditional ablation of Zscan4 subpopulation is dispensable for both endoderm and mesoderm but is required for ectoderm lineage. In conclusion, our research provides new insights about the role of RA signaling during ESCs high pluripotency metastate fluctuation.

Familiarity participates in the pathogenesis of hypertension, although only recently, whole genome studies have proposed regions of the human genome possibly involved in the transmission of the hypertensive phenotype. Although studies have mainly focused on autosome, hitherto the influence of sex on familial transmission of hypertension has not been considered. We analysed the database of the Campania Salute Network of Hypertension center of the Federico II University Hospital of Naples (Italy), using dichotomous variables for paternal and maternal familiarity and gender (male and female) of 12 504 hypertensive patients (6868 males and 5636 females) and 6352 controls (3484 males and 2868 females), totaling 18 856 subjects. In the hypertensive group, familiarity was present in 75% of cases with odds of 3.77 and in only 26% of the normotensives with odds of 0.94. The odds ratio (OR) indicated that familiarity increases the risk of developing hypertension by 2.91 (95% confidence interval (CI)=2.67–3.17, P

Oncogenic TACC-tics Human cancers exhibit many types of genomic rearrangements—including some that juxtapose sequences from two unrelated genes—thereby creating fusion proteins with oncogenic activity. Functional analysis of these fusion genes can provide mechanistic insights into tumorigenesis and potentially lead to effective drugs, as famously illustrated by the BCR-ABL gene in chronic myelogenous leukemia. Singh et al. (p. 1231 , published online 26 July) identify and characterize a fusion gene present in 3% of human glioblastomas, a deadly brain cancer. In the resultant fusion protein, the tyrosine kinase region of the fibroblast growth factor receptor (FGFR) is joined to a domain from a transforming acidic coiled-coil (TACC) protein. The TACC-FGFR protein is oncogenic, shows unregulated kinase activity, localizes to the mitotic spindle, and disrupts chromosome segregation. In mice, FGFR inhibitors slowed the growth of tumors driven by the TACC-FGFR gene, suggesting that a subset of glioblastoma patients may benefit from these types of drugs.

Motivation: Copy number alterations (CNAs) represent an important component of genetic variation and play a significant role in many human diseases. Development of array comparative genomic hybridization (aCGH) technology has made it possible to identify CNAs. Identification of recurrent CNAs represents the first fundamental step to provide a list of genomic regions which form the basis for further biological investigations. The main problem in recurrent CNAs discovery is related to the need to distinguish between functional changes and random events without pathological relevance. Within-sample homogeneity represents a common feature of copy number profile in cancer, so it can be used as additional source of information to increase the accuracy of the results. Although several algorithms aimed at the identification of recurrent CNAs have been proposed, no attempt of a comprehensive comparison of different approaches has yet been published. Results: We propose a new approach, called Genomic Analysis of Important Alterations (GAIA), to find recurrent CNAs where a statistical hypothesis framework is extended to take into account within-sample homogeneity. Statistical significance and within-sample homogeneity are combined into an iterative procedure to extract the regions that likely are involved in functional changes. Results show that GAIA represents a valid alternative to other proposed approaches. In addition, we perform an accurate comparison by using two real aCGH datasets and a carefully planned simulation study. Availability: GAIA has been implemented as R/Bioconductor package. It can be downloaded from the following page http://bioinformatics.biogem.it/download/gaia Contact: ceccarelli@unisannio.it; morganella@unisannio.it Supplementary Information: Supplementary data are available at Bioinformatics online.

This paper reports a novel method for nucleus and micronucleus segmentation. These biological structures are useful to biologists for relieving structural chromosome aberration. The adopted method consists of a pipeline of advanced computer vision algorithms, some of them were specifically tailored for the current segmentation problem. Starting from the weak hypotheses on size, shape, and color of micronucleus, it is possible to efficiently segment and measure the image features of interest by a com- puter vision approach. We report experimental results with a new flow cytometer architecture specifically developed to recognize and measure micronucleus of human lymphocyte. The robustness of the algorithm with respect to various kinds of noise is also reported.

Multivariate spatial statistics techniques can be efficiently applied to generate fine spatial patterns of climate data in presence of an appropriate multivariate spatial structure over ungauged mountainous basins. However, they can become unsuitable when the data available over complex regions are sparse and affected by discordant spatial scales in primary and (colocated)-auxiliary variables. This is the case of actual evapotranspiration (AET). Combining GIS and geoindicators (e.g., topographical and vegetational indices), we proposed an upscaling procedure to overcome this problem, transforming a preliminary-smoothed macro-scale pattern (AET grid-data), into a local-scale pattern. The procedure was applied to a cropland test site at Mediterranean sub-regional basin scale (Tammaro, South Italy) to develop a climatological baseline estimation of AET refined at slope scale. After the upscaling, the most frequent estimated AET values were about 550 mm yr−1 (with quasi-normal distribution), while underestimations were observed in the preliminary, smoothed map (positively skewed distribution with mean 460 mm yr−1). The upscaling allowed the influence of the topographic factor to emerge, with a wider range of values (about 300–900 mm yr−1) being estimated and substantially not visible in the smoothed pattern. A temporal climate pattern of soil water depletion in the growing season was also shown as reflected in the increase of AET flux in the period 1991–2008 in comparison to the precedent climate (1961–1990).

The GDC (Genomic Data Commons) data portal provides users with data from cancer genomics studies. Recently, we developed the R/Bioconductor TCGAbiolinks package, which allows users to search, download and prepare cancer genomics data for integrative data analysis. The use of this package requires users to have advanced knowledge of R thus limiting the number of users. To overcome this obstacle and improve the accessibility of the package by a wider range of users, we developed a graphical user interface (GUI) using Shiny available through the package TCGAbiolinksGUI. The TCGAbiolinksGUI package is freely available within the Bioconductor project at http://bioconductor.org/packages/TCGAbiolinksGUI/. Links to the GitHub repository, a demo version of the tool, a docker image and PDF/video tutorials are available from the TCGAbiolinksGUI site.

Disease processes are usually driven by several genes interacting in molecular modules or pathways leading to the disease. The identification of such modules in gene or protein networks is the core of computational methods in biomedical research. With this pretext, the Disease Module Identification (DMI) DREAM Challenge was initiated as an effort to systematically assess module identification methods on a panel of 6 diverse genomic networks. In this paper, we propose a generic refinement method based on ideas of merging and splitting the hierarchical tree obtained from any community detection technique for constrained DMI in biological networks. The only constraint was that size of community is in the range [3, 100]. We propose a novel model evaluation metric, called F-score, computed from several unsupervised quality metrics like modularity, conductance and connectivity to determine the quality of a graph partition at given level of hierarchy. We also propose a quality measure, namely Inverse Confidence, which ranks and prune insignificant modules to obtain a curated list of candidate disease modules (DM) for biological network. The predicted modules are evaluated on the basis of the total number of unique candidate modules that are associated with complex traits and diseases from over 200 genome-wide association study (GWAS) datasets. During the competition, we identified 42 modules, ranking 15th at the official false detection rate (FDR) cut-off of 0.05 for identifying statistically significant DM in the 6 benchmark networks. However, for stringent FDR cut-offs 0.025 and 0.01, the proposed method identified 31 (rank 9) and 16 DMIs (rank 10) respectively. From additional analysis, our proposed approach detected a total of 44 DM in the networks in comparison to 60 for the winner of DREAM Challenge. Interestingly, for several individual benchmark networks, our performance was better or competitive with the winner.

We propose a generic framework for gene regulatory network (GRN) inference approached as a feature selection problem. GRNs obtained using Machine Learning techniques are often dense, whereas real GRNs are rather sparse. We use a Tikonov regularization inspired optimal L-curve criterion that utilizes the edge weight distribution for a given target gene to determine the optimal set of TFs associated with it. Our proposed framework allows to incorporate a mechanistic active biding network based on cis-regulatory motif analysis. We evaluate our regularization framework in conjunction with two non-linear ML techniques, namely gradient boosting machines (GBM) and random-forests (GENIE), resulting in a regularized feature selection based method specifically called RGBM and RGENIE respectively. RGBM has been used to identify the main transcription factors that are causally involved as master regulators of the gene expression signature activated in the FGFR3-TACC3-positive glioblastoma. Here, we illustrate that RGBM identifies the main regulators of the molecular subtypes of brain tumors. Our analysis reveals the identity and corresponding biological activities of the master regulators characterizing the difference between G-CIMP-high and G-CIMP-low subtypes and between PA-like and LGm6-GBM, thus providing a clue to the yet undetermined nature of the transcriptional events among these subtypes.

Multiple Damaging Hydrological Events are rapidly developing into worldwide disasters with effects to the vi- able habitat for humankind and ecosystems. This research describes how data assimilation friendly models combining re- motely sensed and ground hydrological data could be used for developing a soft geovisual communication in order to re- duce the uncertainty in rainstorm hazard mapping. For this, a set of sequential GIScience rules was utilized for converting coding data of a Rainstorm Hazard Index (RHI) from point record to spatial information using TRMM-NASA satellite rain data as covariate. Examples of probability estimation for different precipitation durations, ranging from 3 to 48 hours and the quantification of hydrological hazard fields were used with probability maps of damaging rainstorms prone-areas for the test-region of Southern Italy. Results show that sub-regional rainstorm hazard modelling can provide probability maps for damaging events in Italy with a spatial variability resolution of around 20 km. Spatially finer estimates (e.g., at local-scale: < 10 km) can be ensured only with the availability of more accurate and detailed remote sensing rain data.

Motivation: Prediction of alternative splicing has been traditionally based on the study of expressed sequences, helped by homology considerations and the analysis of local discriminative features. More recently, machine learning algorithms have been developed that try avoid or reduce the use of a priori information, with partial success. Objective and method: With the aim of developing a fully automatic procedure of recognition of alternative splicing events based only on the genomic sequence, we first introduce a virtual genetic coding scheme to numerically modeling the information content of sequences in an effective way, then we use time series analysis to extract a fixed-length set of features from each sequence and finally we adopt a supervised learning method, namely the support vector machine, to predict alternative splicing events. Results: On the base of real C. elegans data, we show that it is possible within this purely numeric framework to obtain results better than the state of the art, without any explicit modeling of homology or positions in the splice site, nor any use of other local features. Conclusion: The virtual genetic coding together with time series analysis allows us to introduce an effective and powerful sequence coding scheme, that may be useful in various areas of genomics and transcriptomics.

Rain erosivity is difficult to reconstruct for past periods due to the few long detailed instrumental series. This paper deals with the potential of written historical documents to reconstruct yearly erosive rain anomalies (differences between erosivity values and long-term average). A detailed analysis was undertaken of the Calore River Basin (South Italy) erosive rainfall using data from 425-year-long series of both observations (1922–2004) and proxy-based reconstructions (1580–1921). Annual erosivity was computed for the observational time based on the Revised Universal Soil Loss Equation (RUSLE) scheme, whilst documentary descriptions described the course of rain events for the previous times. A method was proposed that uses a weather index to account for the abundance of weather anomalies (rainfall extremes) as registered in historical documents. A model was developed to provide annual rain-erosivity data comparable with the RUSLE scheme, using the weather index sum (June–October) and variance (January–December) and the annual precipitation amount as inputs. This model was applied to reconstruct annual rain-erosivity anomalies for the pre-modern instrumental time. A long continuous series of rain-erosivity anomalies was produced by combining (and homogenizing) model-reconstructed and RUSLE data, thus providing a range of natural variability in climate erosivity to detect the main changes from 1580 to 2004. The climate history of the Calore River Basin shows pronounced interdecadal variations, with multidecadal erosivity reflecting the mixed population of thermoconvective and cyclonic rainstorms with large anomalies. The so-called Little Ice Age (16th to mid-19th centuries) was identified as the stormiest period, with mixed rainstorm types and high frequency of floods and erosive rainfall. Important historical climate fluctuations were observed, but with a relevant smoothing in the last 150 years of the series. For the recent, warm period, investigations at higher time resolution are required to assess the seasonal changes of rain erosivity and their relationship to soil conservation. Copyright © 2008 John Wiley & Sons, Ltd.