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Conventional chemotherapy for acute myeloid leukemia regimens generally encompass an intensive induction phase, in order to achieve a morphological remission in terms of bone marrow blasts (&lt, 5%). The majority of cases are classified as Primary Induction Response (PIR), unfortunately, 15% of children do not achieve remission and are defined Primary Induction Failure (PIF). This study aims to characterize the gene expression profile of PIF in children with Acute Myeloid Leukemia (AML), in order to detect molecular pathways dysfunctions and identify potential biomarkers. Given that NUP98-rearrangements are enriched in PIF-AML patients, we investigated the association of NUP98-driven genes in primary chemoresistance. Therefore, 85 expression arrays, deposited on GEO database, and 358 RNAseq AML samples, from TARGET program, were analyzed for “Differentially Expressed Genes” (DEGs) between NUP98+ and NUP98-, identifying 110 highly confident NUP98/PIF-associated DEGs. We confirmed, by qRT-PCR, the overexpression of nine DEGs, selected on the bases of the diagnostic accuracy, in a local cohort of PIF patients: SPINK2, TMA7, SPCS2, CDCP1, CAPZA1, FGFR1OP2, MAN1A2, NT5C3A and SRP54. In conclusion, the integrated analysis of NUP98 mutational analysis and transcriptome profiles allowed the identification of novel putative biomarkers for the prediction of PIF in AML.

Derivative chromosome der(1, 16), isochromosome 1q, and deleted 16q—producing arm-level 1q-gain and/or 16q-loss—are recurrent cytogenetic abnormalities in breast cancer, but their exact role in determining the malignant phenotype is still largely unknown. We exploited The Cancer Genome Atlas (TCGA) data to generate and analyze groups of breast invasive carcinomas, called 1,16-chromogroups, that are characterized by a pattern of arm-level somatic copy number aberrations congruent with known cytogenetic aberrations of chromosome 1 and 16. Substantial differences were found among 1,16-chromogroups in terms of other chromosomal aberrations, aneuploidy scores, transcriptomic data, single-point mutations, histotypes, and molecular subtypes. Breast cancers with a co-occurrence of 1q-gain and 16q-loss can be distinguished in a “low aneuploidy score” group, congruent to der(1, 16), and a “high aneuploidy score” group, congruent to the co-occurrence of isochromosome 1q and deleted 16q. Another three groups are formed by cancers showing separately 1q-gain or 16q-loss or no aberrations of 1q and 16q. Transcriptome comparisons among the 1,16-chromogroups, integrated with functional pathway analysis, suggested the cooperation of overexpressed 1q genes and underexpressed 16q genes in the genesis of both ductal and lobular carcinomas, thus highlighting the putative role of genes encoding gamma-secretase subunits (APH1A, PSEN2, and NCSTN) and Wnt enhanceosome components (BCL9 and PYGO2) in 1q, and the glycoprotein E-cadherin (CDH1), the E3 ubiquitin-protein ligase WWP2, the deubiquitinating enzyme CYLD, and the transcription factor CBFB in 16q. The analysis of 1,16-chromogroups is a strategy with far-reaching implications for the selection of cancer cell models and novel experimental therapies.

Broad Copy Number Gains (BCNGs) are copy-number increases of chromosomes or large segments of chromosomal arms. Publicly-available single-nucleotide polymorphism (SNP) array and RNA-Seq data of colon adenocarcinoma (COAD) samples from The Cancer Genome Atlas (TCGA) consortium allowed us to design better control groups in order to identify changes in expression due to highly recurrent BCNGs (in chromosomes 20, 8, 7, 13). We identified: (1) Overexpressed Transcripts (OverT), transcripts whose expression increases in &ldquo, COAD groups bearing a specific BCNG&rdquo, in comparison to &ldquo, control COAD groups&rdquo, not bearing it, and (2) up-regulated/down-regulated transcripts, transcripts whose expression increases/decreases in COAD groups in comparison to normal colon tissue. An analysis of gene expression reveals a correlation between the density of up-regulated genes per selected chromosome and the recurrence rate of their BCNGs. We report an enrichment of gained enhancer activity and of cancer fitness genes among OverT genes. These results support the hypothesis that the chromosomal density of overexpressed cancer fitness genes might play a significant role in the selection of gained chromosomes during cancer evolution. Analysis of functional pathways associated with OverT suggest that some multi-subunit protein complexes (eIF2, eIF3, CSTF and CPSF) are candidate targets for silencing transcriptional therapy.

Obsessive-compulsive disorder (OCD) is a psychiatric condition characterized by intrusive and unwilling thoughts (obsessions) giving rise to anxiety. The patients feel obliged to perform a behavior (compulsions) induced by the obsessions. The World Health Organization ranks OCD as one of the 10 most disabling medical conditions. In the class of Anxiety Disorders, OCD is a pathology that shows an hereditary component. Consequently, an online resource collecting and integrating scientific discoveries and genetic evidence about OCD would be helpful to improve the current knowledge on this disorder. We have developed a manually curated database, OCD Database (OCDB), collecting the relations between candidate genes in OCD, microRNAs (miRNAs) involved in the pathophysiology of OCD and drugs used in its treatments. We have screened articles from PubMed and MEDLINE. For each gene, the bibliographic references with a brief description of the gene and the experimental conditions are shown. The database also lists the polymorphisms within genes and its chromosomal regions. OCDB data is enriched with both validated and predicted miRNA-target and drug-target information. The transcription factors regulations, which are also included, are taken from David and TransmiR. Moreover, a scoring function ranks the relevance of data in the OCDB context. The database is also integrated with the main online resources (PubMed, Entrez-gene, HGNC, dbSNP, DrugBank, miRBase, PubChem, Kegg, Disease-ontology and ChEBI). The web interface has been developed using phpMyAdmin and Bootstrap software. This allows (i) to browse data by category and (ii) to navigate in the database by searching genes, miRNAs, drugs, SNPs, regions, drug targets and articles. The data can be exported in textual format as well as the whole database in.sql or tabular format. OCDB is an essential resource to support genome-wide analysis, genetic and pharmacological studies. It also facilitates the evaluation of genetic data in OCD and the detection of alternative treatments. Database URL: http://alpha.dmi.unict.it/ocdb/