Candidate Genes and MiRNAs Linked to the Inverse Relationship Between Cancer and Alzheimer's Disease: Insights From Data Mining and Enrichment Analysis.

The incidence of cancer and Alzheimer's disease (AD) increases exponentially with age. A growing body of epidemiological evidence and molecular investigations inspired the hypothesis of an inverse relationship between these two pathologies. It has been proposed that the two diseases might utilize the same proteins and pathways that are, however, modulated differently and sometimes in opposite directions. Investigation of the common processes underlying these diseases may enhance the understanding of their pathogenesis and may also guide novel therapeutic strategies. Starting from a text-mining approach, our in silico study integrated the dispersed biological evidence by combining data mining, gene set enrichment, and protein-protein interaction (PPI) analyses while searching for common biological hallmarks linked to AD and cancer. We retrieved 138 genes (ALZCAN gene set), computed a significant number of enriched gene ontology clusters, and identified four PPI modules. The investigation confirmed the relevance of autophagy, ubiquitin proteasome system, and cell death as common biological hallmarks shared by cancer and AD. Then, from a closer investigation of the PPI modules and of the miRNAs enrichment data, several genes (SQSTM1, UCHL1, STUB1, BECN1, CDKN2A, TP53, EGFR, GSK3B, and HSPA9) and miRNAs (miR-146a-5p, MiR-34a-5p, miR-21-5p, miR-9-5p, and miR-16-5p) emerged as promising candidates. The integrative approach uncovered novel miRNA-gene networks (e.g., miR-146 and miR-34 regulating p62 and Beclin1 in autophagy) that might give new insights into the complex regulatory mechanisms of gene expression in AD and cancer.

Erratum: OncoScore: a novel, Internet-based tool to assess the oncogenic potential of genes.

This corrects the article DOI: 10.1038/srep46290.

OncoScore: a novel, Internet-based tool to assess the oncogenic potential of genes.

The complicated, evolving landscape of cancer mutations poses a formidable challenge to identify cancer genes among the large lists of mutations typically generated in NGS experiments. The ability to prioritize these variants is therefore of paramount importance. To address this issue we developed OncoScore, a text-mining tool that ranks genes according to their association with cancer, based on available biomedical literature. Receiver operating characteristic curve and the area under the curve (AUC) metrics on manually curated datasets confirmed the excellent discriminating capability of OncoScore (OncoScore cut-off threshold = 21.09; AUC = 90.3%, 95% CI: 88.1-92.5%), indicating that OncoScore provides useful results in cases where an efficient prioritization of cancer-associated genes is needed.

Read-through transcripts in normal human lung parenchyma are down-regulated in lung adenocarcinoma.

Read-through transcripts result from the continuous transcription of adjacent, similarly oriented genes, with the splicing out of the intergenic region. They have been found in several neoplastic and normal tissues, but their pathophysiological significance is unclear. We used high-throughput sequencing of cDNA fragments (RNA-Seq) to identify read-through transcripts in the non-involved lung tissue of 64 surgically treated lung adenocarcinoma patients. A total of 52 distinct read-through species was identified, with 24 patients having at least one read-through event, up to a maximum of 17 such transcripts in one patient. Sanger sequencing validated 28 of these transcripts and identified an additional 15, for a total of 43 distinct read-through events involving 35 gene pairs. Expression levels of 10 validated read-through transcripts were measured by quantitative PCR in pairs of matched non-involved lung tissue and lung adenocarcinoma tissue from 45 patients. Higher expression levels were observed in normal lung tissue than in the tumor counterpart, with median relative quantification ratios between normal and tumor varying from 1.90 to 7.78; the difference was statistically significant (P < 0.001, Wilcoxon's signed-rank test for paired samples) for eight transcripts: ELAVL1-TIMM44, FAM162B-ZUFSP, IFNAR2-IL10RB, INMT-FAM188B, KIAA1841-C2orf74, NFATC3-PLA2G15, SIRPB1-SIRPD, and SHANK3-ACR. This report documents the presence of read-through transcripts in apparently normal lung tissue, with inter-individual differences in patterns and abundance. It also shows their down-regulation in tumors, suggesting that these chimeric transcripts may function as tumor suppressors in lung tissue.

Recurrent ETNK1 mutations in atypical chronic myeloid leukemia.

Despite the recent identification of recurrent SETBP1 mutations in atypical chronic myeloid leukemia (aCML), a complete description of the somatic lesions responsible for the onset of this disorder is still lacking. To find additional somatic abnormalities in aCML, we performed whole-exome sequencing on 15 aCML cases. In 2 cases (13.3%), we identified somatic missense mutations in the ETNK1 gene. Targeted resequencing on 515 hematological clonal disorders revealed the presence of ETNK1 variants in 6 (8.8%) of 68 aCML and 2 (2.6%) of 77 chronic myelomonocytic leukemia samples. These mutations clustered in a small region of the kinase domain, encoding for H243Y and N244S (1/8 H243Y; 7/8 N244S). They were all heterozygous and present in the dominant clone. The intracellular phosphoethanolamine/phosphocholine ratio was, on average, 5.2-fold lower in ETNK1-mutated samples (P < .05). Similar results were obtained using myeloid TF1 cells transduced with ETNK1 wild type, ETNK1-N244S, and ETNK1-H243Y, where the intracellular phosphoethanolamine/phosphocholine ratio was significantly lower in ETNK1-N244S (0.76 ± 0.07) and ETNK1-H243Y (0.37 ± 0.02) than in ETNK1-WT (1.37 ± 0.32; P = .01 and P = .0008, respectively), suggesting that ETNK1 mutations may inhibit the catalytic activity of the enzyme. In summary, our study shows for the first time the evidence of recurrent somatic ETNK1 mutations in the context of myeloproliferative/myelodysplastic disorders.

CEQer: a graphical tool for copy number and allelic imbalance detection from whole-exome sequencing data.

Copy number alterations (CNA) are common events occurring in leukaemias and solid tumors. Comparative Genome Hybridization (CGH) is actually the gold standard technique to analyze CNAs; however, CGH analysis requires dedicated instruments and is able to perform only low resolution Loss of Heterozygosity (LOH) analyses. Here we present CEQer (Comparative Exome Quantification analyzer), a new graphical, event-driven tool for CNA/allelic-imbalance (AI) coupled analysis of exome sequencing data. By using case-control matched exome data, CEQer performs a comparative digital exonic quantification to generate CNA data and couples this information with exome-wide LOH and allelic imbalance detection. This data is used to build mixed statistical/heuristic models allowing the identification of CNA/AI events. To test our tool, we initially used in silico generated data, then we performed whole-exome sequencing from 20 leukemic specimens and corresponding matched controls and we analyzed the results using CEQer. Taken globally, these analyses showed that the combined use of comparative digital exon quantification and LOH/AI allows generating very accurate CNA data. Therefore, we propose CEQer as an efficient, robust and user-friendly graphical tool for the identification of CNA/AI in the context of whole-exome sequencing data.

Gene expression signature of non-involved lung tissue associated with survival in lung adenocarcinoma patients.

Lung adenocarcinoma patients of similar clinical stage and undergoing the same treatments often have marked interindividual variations in prognosis. These clinical discrepancies may be due to the genetic background modulating an individual's predisposition to fighting cancer. Herein, we hypothesized that the lung microenvironment, as reflected by its expression profile, may affect lung adenocarcinoma patients' survival. The transcriptome of non-involved lung tissue, excised from a discovery series of 204 lung adenocarcinoma patients, was evaluated using whole-genome expression microarrays (with probes corresponding to 28 688 well-annotated coding sequences). Genes associated with survival status at 60 months were identified by Cox regression analysis (adjusted for gender, age and clinical stage) and retested in a validation series of 78 additional cases. RNA-Seq analysis from non-involved lung tissue of 12 patients was performed to characterize the different isoforms of candidate genes. Ten genes for which the loge-transformed hazard ratios expressed the same direction of effect in the discovery (P < 1.0 × 10(-3)) and validation series comprised the gene expression signature associated with survival: CNTNAP1, PKNOX1, FAM156A, FRMD8, GALNTL1, TXNDC12, SNTB1, PPP3R1, SNX10 and SERPINH1. RNA sequencing highlighted the complex expression pattern of these genes in non-involved lung tissue from different patients and permitted the detection of a read-through gene fusion between PPP3R1 and the flanking gene (CNRIP1) as well as a novel isoform of CNTNAP1. Our findings support the hypothesis that individual genetic characteristics, evidenced by the expression pattern of non-involved tissue, influence the outcome of lung adenocarcinoma patients.

Whole genome SNP genotyping and exome sequencing reveal novel genetic variants and putative causative genes in congenital hyperinsulinism.

Congenital hyperinsulinism of infancy (CHI) is a rare disorder characterized by severe hypoglycemia due to inappropriate insulin secretion. The genetic causes of CHI have been found in genes regulating insulin secretion from pancreatic ß-cells; recessive inactivating mutations in the ABCC8 and KCNJ11 genes represent the most common events. Despite the advances in understanding the molecular pathogenesis of CHI, specific genetic determinants in about 50 % of the CHI patients remain unknown, suggesting additional locus heterogeneity. In order to search for novel loci contributing to the pathogenesis of CHI, we combined a family-based association study, using the transmission disequilibrium test on 17 CHI patients lacking mutations in ABCC8/KCNJ11, with a whole-exome sequencing analysis performed on 10 probands. This strategy allowed the identification of the potential causative mutations in genes implicated in the regulation of insulin secretion such as transmembrane proteins (CACNA1A, KCNH6, KCNJ10, NOTCH2, RYR3, SCN8A, TRPV3, TRPC5), cytosolic (ACACB, CAMK2D, CDKAL1, GNAS, NOS2, PDE4C, PIK3R3) and mitochondrial enzymes (PC, SLC24A6), and in four genes (CSMD1, SLC37A3, SULF1, TLL1) suggested by TDT family-based association study. Moreover, the exome-sequencing approach resulted to be an efficient diagnostic tool for CHI, allowing the identification of mutations in three causative CHI genes (ABCC8, GLUD1, and HNF1A) in four out of 10 patients. Overall, the present study should be considered as a starting point to design further investigations: our results might indeed contribute to meta-analysis studies, aimed at the identification/confirmation of novel causative or modifier genes.

Identification of novel point mutations in splicing sites integrating whole-exome and RNA-seq data in myeloproliferative diseases.

Point mutations in intronic regions near mRNA splice junctions can affect the splicing process. To identify novel splicing variants from exome sequencing data, we developed a bioinformatics splice-site prediction procedure to analyze next-generation sequencing (NGS) data (SpliceFinder). SpliceFinder integrates two functional annotation tools for NGS, ANNOVAR and MutationTaster and two canonical splice site prediction programs for single mutation analysis, SSPNN and NetGene2. By SpliceFinder, we identified somatic mutations affecting RNA splicing in a colon cancer sample, in eight atypical chronic myeloid leukemia (aCML), and eight CML patients. A novel homozygous splicing mutation was found in APC (NM_000038.4:c.1312+5G>A) and six heterozygous in GNAQ (NM_002072.2:c.735+1C>T), ABCC 3 (NM_003786.3:c.1783-1G>A), KLHDC 1 (NM_172193.1:c.568-2A>G), HOOK 1 (NM_015888.4:c.1662-1G>A), SMAD 9 (NM_001127217.2:c.1004-1C>T), and DNAH 9 (NM_001372.3:c.10242+5G>A). Integrating whole-exome and RNA sequencing in aCML and CML, we assessed the phenotypic effect of mutations on mRNA splicing for GNAQ, ABCC 3, HOOK 1. In ABCC 3 and HOOK 1, RNA-Seq showed the presence of aberrant transcripts with activation of a cryptic splice site or intron retention, validated by the reverse transcription-polymerase chain reaction (RT-PCR) in the case of HOOK 1. In GNAQ, RNA-Seq showed 22% of wild-type transcript and 78% of mRNA skipping exon 5, resulting in a 4-6 frameshift fusion confirmed by RT-PCR. The pipeline can be useful to identify intronic variants affecting RNA sequence by complementing conventional exome analysis.

Recurrent SETBP1 mutations in atypical chronic myeloid leukemia.

Atypical chronic myeloid leukemia (aCML) shares clinical and laboratory features with CML, but it lacks the BCR-ABL1 fusion. We performed exome sequencing of eight aCMLs and identified somatic alterations of SETBP1 (encoding a p.Gly870Ser alteration) in two cases. Targeted resequencing of 70 aCMLs, 574 diverse hematological malignancies and 344 cancer cell lines identified SETBP1 mutations in 24 cases, including 17 of 70 aCMLs (24.3%; 95% confidence interval (CI) = 16-35%). Most mutations (92%) were located between codons 858 and 871 and were identical to changes seen in individuals with Schinzel-Giedion syndrome. Individuals with mutations had higher white blood cell counts (P = 0.008) and worse prognosis (P = 0.01). The p.Gly870Ser alteration abrogated a site for ubiquitination, and cells exogenously expressing this mutant exhibited higher amounts of SETBP1 and SET protein, lower PP2A activity and higher proliferation rates relative to those expressing the wild-type protein. In summary, mutated SETBP1 represents a newly discovered oncogene present in aCML and closely related diseases.

FusionAnalyser: a new graphical, event-driven tool for fusion rearrangements discovery.

Gene fusions are common driver events in leukaemias and solid tumours; here we present FusionAnalyser, a tool dedicated to the identification of driver fusion rearrangements in human cancer through the analysis of paired-end high-throughput transcriptome sequencing data. We initially tested FusionAnalyser by using a set of in silico randomly generated sequencing data from 20 known human translocations occurring in cancer and subsequently using transcriptome data from three chronic and three acute myeloid leukaemia samples. in all the cases our tool was invariably able to detect the presence of the correct driver fusion event(s) with high specificity. In one of the acute myeloid leukaemia samples, FusionAnalyser identified a novel, cryptic, in-frame ETS2-ERG fusion. A fully event-driven graphical interface and a flexible filtering system allow complex analyses to be run in the absence of any a priori programming or scripting knowledge. Therefore, we propose FusionAnalyser as an efficient and robust graphical tool for the identification of functional rearrangements in the context of high-throughput transcriptome sequencing data.

A computational procedure to identify significant overlap of differentially expressed and genomic imbalanced regions in cancer datasets.

The integration of high-throughput genomic data represents an opportunity for deciphering the interplay between structural and functional organization of genomes and for discovering novel biomarkers. However, the development of integrative approaches to complement gene expression (GE) data with other types of gene information, such as copy number (CN) and chromosomal localization, still represents a computational challenge in the genomic arena. This work presents a computational procedure that directly integrates CN and GE profiles at genome-wide level. When applied to DNA/RNA paired data, this approach leads to the identification of Significant Overlaps of Differentially Expressed and Genomic Imbalanced Regions (SODEGIR). This goal is accomplished in three steps. The first step extends to CN a method for detecting regional imbalances in GE. The second part provides the integration of CN and GE data and identifies chromosomal regions with concordantly altered genomic and transcriptional status in a tumor sample. The last step elevates the single-sample analysis to an entire dataset of tumor specimens. When applied to study chromosomal aberrations in a collection of astrocytoma and renal carcinoma samples, the procedure proved to be effective in identifying discrete chromosomal regions of coordinated CN alterations and changes in transcriptional levels.

Genome-wide screening of copy number alterations and LOH events in renal cell carcinomas and integration with gene expression profile.

BACKGROUND: Clear cell renal carcinoma (RCC) is the most common and invasive adult renal cancer. For the purpose of identifying RCC biomarkers, we investigated chromosomal regions and individual genes modulated in RCC pathology. We applied the dual strategy of assessing and integrating genomic and transcriptomic data, today considered the most effective approach for understanding genetic mechanisms of cancer and the most sensitive for identifying cancer-related genes. RESULTS: We performed the first integrated analysis of DNA and RNA profiles of RCC samples using Affymetrix technology. Using 100K SNP mapping arrays, we assembled a genome-wide map of DNA copy number alterations and LOH areas. We thus confirmed the typical genetic signature of RCC but also identified other amplified regions (e.g. on chr. 4, 11, 12), deleted regions (chr. 1, 9, 22) and LOH areas (chr. 1, 2, 9, 13). Simultaneously, using HG-U133 Plus 2.0 arrays, we identified differentially expressed genes (DEGs) in tumor vs. normal samples. Combining genomic and transcriptomic data, we identified 71 DEGs in aberrant chromosomal regions and observed, in amplified regions, a predominance of up-regulated genes (27 of 37 DEGs) and a trend to clustering. Functional annotation of these genes revealed some already implicated in RCC pathology and other cancers, as well as others that may be novel tumor biomarkers. CONCLUSION: By combining genomic and transcriptomic profiles from a collection of RCC samples, we identified specific genomic regions with concordant alterations in DNA and RNA profiles and focused on regions with increased DNA copy number. Since the transcriptional modulation of up-regulated genes in amplified regions may be attributed to the genomic alterations characteristic of RCC, these genes may encode novel RCC biomarkers actively involved in tumor initiation and progression and useful in clinical applications.

[Combined vaccine against measles, mumps, rubella and varicella and recommendations for varicella immunization in Italy?]. / Il vaccino quadruplo contro morbillo, parotite, rosolia e varicella modificherà le indicazioni per la profilassi dell'infezione da virus varicella zoster in italia?

In Italy, the whole population is affected by chickenpox especially in infancy with an important burden of disease. Vaccination is recommended for susceptible adolescents and adults, but the offer is different in different regions. The availability of a combined vaccine against measles, mumps, rubella and varicella could allow an easier introduction of VZV prophylaxis in infants immunization schedules without increasing the number of injections and of accesses to Health Services.