Roberta Spinelli

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.

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.

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.

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.

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.

Erratum: 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.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.

OncoScore: an R package to measure the oncogenic potential of genes.

The increasing availability of sequencing data of cancer samples is fueling the development of algorithmic strategies to investigate tumor heterogeneity and infer reliable models of cancer evolution. We here build up on previous works on cancer progression inference from genomic alteration data, to deliver two distinct Cytoscape-based applications, which allow to produce, visualize and manipulate cancer evolution models, also by interacting with public genomic and proteomics databases. In particular, we here introduce cyTRON, a stand-alone Cytoscape app, and cyTRON/JS, a web application which employs the functionalities of Cytoscape/JS. cyTRON was developed in Java; the code is available at https://github.com/BIMIB-DISCo/cyTRON and on the Cytoscape App Store http://apps.cytoscape.org/apps/cytron. cyTRON/JS was developed in JavaScript and R; the source code of the tool is available at https://github.com/BIMIB-DISCo/cyTRON-js and the tool is accessible from https://bimib.disco.unimib.it/cytronjs/welcome.Motivation: We here present OncoScore, an open-source tool and R package that implements a novel text-mining method capable of ranking genes according to their association to cancer, based on available biomedical literature on PubMed. OncoScore can scan the biomedical literature with dynamically updatable web queries and measure the association to cancer of each gene by considering their citations. The output of the tool is a score that is measuring the strength of the association of the genes to cancer at the time of the analysis. Availability and Implementation: OncoScore is available on GitHub and as an R package on bioconductor.org. Furthermore, the queries to OncoScore can also be performed at http://www.galseq.com/oncoscore.html Contact: daniele.ramazzotti@stanford.edu or l.desano@campus.unimib.it Supplementary information: Supplementary data are available at Bioinformatics online.

Whole-Exome Sequencing Data - Identifying Somatic Mutations

The use of next-generation sequencing instruments to study hematological malignancies generates a tremendous amount of sequencing data. This leads to a challenging bioinformatics problem to store, manage, and analyze terabytes of sequencing data, often generated from extremely different data sources. Our project is mainly focused on sequence analysis of human cancer genomes, in order to identify the genetic lesions underlying the development of tumors. However, the automated detection procedure of somatic mutations and the statistical testing procedure to identify genetic lesions are still an open problem. Therefore, we propose a computational procedure to handle large-scale sequencing data in order to detect exonic somatic mutations in a tumor sample. The proposed pipeline includes several steps based on open-source software and the R language: alignment, detection of mutations, annotation, functional classification, and visualization of results. We analyzed Illumina whole-exome sequencing data from five leukemic patients and five paired controls plus one colon cancer sample and paired control. The results were validated by Sanger sequencing.

A Bioinformatics Procedure to Identify and Annotate Somatic Mutations in Whole-Exome Sequencing Data

The application of next-generation sequencing instruments generates a tremendous amount of sequencing data. This leads to a challenging bioinformatics problem to store, manage and analyze terabytes of sequencing data often generated from extremely different data-sources. Our project is mainly focused on the sequence analysis of human cancer genomes, in order to identify the genetic lesions underlying the development of tumors. However, the automated detection procedure of somatic mutations and a statistical based testing procedure to identify genetic lesions are still an open problem. Therefore, we propose a computational procedure to manage large scale sequencing data in order to detect exonic somatic mutations in a tumor sample. The proposed pipeline includes several steps based on open-source softwares and R language: alignment, detection of mutations, annotation, functional classification and visualization of results. We analyzed whole exome sequencing data from 3 leukemic patients and 3 paired controls plus 1 colon cancer sample and paired control. The results were validated by Sanger sequencing.