SETBP1 induces transcription of a network of development genes by acting as an epigenetic hub

SETBP1 variants occur as somatic mutations in several hematological malignancies such as atypical chronic myeloid leukemia and as de novo germline mutations in the Schinzel-Giedion syndrome. Here we show that SETBP1 binds to gDNA in AT-rich promoter regions, causing activation of gene expression through recruitment of a HCF1/KMT2A/PHF8 epigenetic complex. Deletion of two AT-hooks abrogates the binding of SETBP1 to gDNA and impairs target gene upregulation. Genes controlled by SETBP1 such as MECOM are significantly upregulated in leukemias containing SETBP1 mutations. Gene ontology analysis of deregulated SETBP1 target genes indicates that they are also key controllers of visceral organ development and brain morphogenesis. In line with these findings, in utero brain electroporation of mutated SETBP1 causes impairment of mouse neurogenesis with a profound delay in neuronal migration. In summary, this work unveils a SETBP1 function that directly affects gene transcription and clarifies the mechanism operating in myeloid malignancies and in the Schinzel- Giedion syndrome caused by SETBP1 mutations.Peer reviewe

De novo UBE2A mutations are recurrently acquired during chronic myeloid leukemia progression and interfere with myeloid differentiation pathways

Despite the advent of tyrosine kinase inhibitors, a proportion of chronic myeloid leukemia patients in chronic phase fails to respond to Imatinib or to second generation inhibitors and progress to blast crisis. Limited improvements in the understanding of the molecular mechanisms responsible for chronic myeloid leukemia transformation from chronic phase to the aggressive blast crisis were achieved until now. We present here a massive parallel sequencing analysis of 10 blast crisis samples and of the corresponding autologous chronic phase controls which reveals, for the first time, recurrent mutations affecting the ubiquitin-conjugating enzyme E2A gene (UBE2A, formerly RAD6A). Additional analyses on a cohort of 24 blast crisis, 41 chronic phase as well as 40 acute myeloid leukemia and 38 atypical chronic myeloid leukemia patients at onset confirmed that UBE2A mutations are specifically acquired during chronic myeloid leukemia progression with a frequency of 16.7% in advanced phases. In vitro studies show that the mutations here described cause a decrease in UBE2A activity, leading to an impairment of myeloid differentiation in chronic myeloid leukemia cells

Integrated Genomic, Functional, and Prognostic Characterization of Atypical Chronic Myeloid Leukemia

Atypical chronic myeloid leukemia (aCML) is a BCR-ABL1-negative clonal disorder, which belongs to the myelodysplastic/myeloproliferative group. This disease is characterized by recurrent somatic mutations in SETBP1, ASXL1 and ETNK1 genes, as well as high genetic heterogeneity, thus posing a great therapeutic challenge. To provide a comprehensive genomic characterization of aCML we applied a high-throughput sequencing strategy to 43 aCML samples, including both whole-exome and RNA-sequencing data. Our dataset identifies ASXL1, SETBP1, and ETNK1 as the most frequently mutated genes with a total of 43.2%, 29.7 and 16.2%, respectively. We characterized the clonal architecture of 7 aCML patients by means of colony assays and targeted resequencing. The results indicate that ETNK1 variants occur early in the clonal evolution history of aCML, while SETBP1 mutations often represent a late event. The presence of actionable mutations conferred both ex vivo and in vivo sensitivity to specific inhibitors with evidence of strong in vitro synergism in case of multiple targeting. In one patient, a clinical response was obtained. Stratification based on RNA-sequencing identified two different populations in terms of overall survival, and differential gene expression analysis identified 38 significantly overexpressed genes in the worse outcome group. Three genes correctly classified patients for overall survival

ERG Deregulation Induces PIM1 Over-Expression and Aneuploidy in Prostate Epithelial Cells

The ERG gene belongs to the ETS family of transcription factors and has been found to be involved in atypical chromosomal rearrangements in several cancers. To gain insight into the oncogenic activity of ERG, we compared the gene expression profile of NIH-3T3 cells stably expressing the coding regions of the three main ERG oncogenic fusions: TMPRSS2/ERG (tERG), EWS/ERG and FUS/ERG. We found that all three ERG fusions significantly up-regulate PIM1 expression in the NIH-3T3 cell line. PIM1 is a serine/threonine kinase frequently over-expressed in cancers of haematological and epithelial origin. We show here that tERG expression induces PIM1 in the non-malignant prostate cell line RWPE-1, strengthening the relation between tERG and PIM1 up-regulation in the initial stages of prostate carcinogenesis. Silencing of tERG reversed PIM1 induction. A significant association between ERG and PIM1 expression in clinical prostate carcinoma specimens was found, suggesting that such a mechanism may be relevant in vivo. Chromatin Immunoprecipitation experiments showed that tERG directly binds to PIM1 promoter in the RWPE-1 prostate cell line, suggesting that tERG could be a direct regulator of PIM1 expression. The up-regulation of PIM1 induced by tERG over-expression significantly modified Cyclin B1 levels and increased the percentage of aneuploid cells in the RWPE-1 cell line after taxane-based treatment. Here we provide the first evidence for an ERG-mediated PIM1 up-regulation in prostate cells in vitro and in vivo, suggesting a direct effect of ERG transcriptional activity in the alteration of genetic stability

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

Identification of novel point mutations in splicing sites integrating whole‐exome and RNA

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), ABCC3 (NM_003786.3:c.1783-1G>A), KLHDC1 (NM_172193.1:c.568-2A>G), HOOK1 (NM_015888.4:c.1662-1G>A), SMAD9 (NM_001127217.2:c.1004-1C>T), and DNAH9 (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, ABCC3, HOOK1. In ABCC3 and HOOK1, 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 HOOK1. 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

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), ABCC3 (NM_003786.3:c.1783-1G>A), KLHDC1 (NM_172193.1:c.568-2A>G), HOOK1 (NM_015888.4:c.1662-1G>A), SMAD9 (NM_001127217.2:c.1004-1C>T), and DNAH9 (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, ABCC3, HOOK1. In ABCC3 and HOOK1, 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 HOOK1. 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