RTCGAToolbox: A New Tool for Exporting TCGA Firehose Data

Background & Objective Managing data from large-scale projects (such as The Cancer Genome Atlas (TCGA)) for further analysis is an important and time consuming step for research projects. Several efforts, such as the Firehose project, make TCGA pre-processed data publicly available via web services and data portals, but this information must be managed, downloaded and prepared for subsequent steps. We have developed an open source and extensible R based data client for pre-processed data from the Firehouse, and demonstrate its use with sample case studies. Results show that our RTCGAToolbox can facilitate data management for researchers interested in working with TCGA data. The RTCGAToolbox can also be integrated with other analysis pipelines for further data processing. Availability and implementation The RTCGAToolbox is open-source and licensed under the GNU General Public License Version 2.0. All documentation and source code for RTCGAToolbox is freely available at http://mksamur.github.io/RTCGAToolbox/ for Linux and Mac OS X operating systems

Leadership Styles and Technology: Leadership Competency Level of Educational Leaders

AbstractResearchers have studied the leadership styles of educational leaders in connection with their level of computer use and success in integration of ICT. This study aims to reveal if the leadership style can be a predictor of competent technology leaders. The importance of this study is to investigate the leaders’ competency as technology leaders rather than level of perceived use of technology, using Technology Leadership Competency Scale for School Administrators (TELÖY) (Hacıfazlıoğlu, Karadeniz, & Dalgıç, 2011) which is adapted from International Society for Technology and Education (ISTE) standards for school administrators. Fifty educators, who take leadership or administrative roles in educational institutions from the Eastern part of Turkey, completed Multi factor leadership questionnaire (MLQ) developed by Bass (1985) and translated and modified for the Turkish leaders by Demir and Okan (2008) and TELÖY. The results indicate moderate correlation between both transactional and transformational leadership styles. It is concluded that leadership style is not a predictor of competency level of technology leadership. The study contributes into literature discussing the effects of cultural differences in different countries on desired leadership styles, which in result may effect the level of technology leadership competency. In addition it also argues that leadership style characteristics cannot be used as a method to transform education and schools

The Relationship between Perceived Overqualification and Counterproductive Work Behaviors: Moderating Role of Perceived Distributive Justice

Employee behaviors can be classified into two basic groups as positive and negative organizational behaviors. One of the negative organizational behaviors is counterproductive work behaviours. It is aimed to reveal the effects of perceived overqualification on counterproductive work behaviours and moderating role of distributive justice through an empirical study. In this respect, the data obtained from 398 employees in hospitality enterprises was analyzed by means of structural equation modelling (SEM). It is found that there is a positive relationship between perceived overqualification and counterproductive work behaviours, and perceived distributive justice moderates the relationship between perceived overqualification and counterproductive work behaviours towards colleagues. Some theoretical and managerial implications are offered about the variables. Distributive justice is effective in reducing counterproductive work behaviours which emerged from perceived overqualification. Managers need to control the factors that lead to perceived overqualification and implement strategies that can activate catalyst variables, lessening or eliminating its negative consequences. In addition, limitations of the study and suggestions for future studies are provided

canEvolve: A Web Portal for Integrative Oncogenomics

Background & objective: Genome-wide profiles of tumors obtained using functional genomics platforms are being deposited to the public repositories at an astronomical scale, as a result of focused efforts by individual laboratories and large projects such as the Cancer Genome Atlas (TCGA) and the International Cancer Genome Consortium. Consequently, there is an urgent need for reliable tools that integrate and interpret these data in light of current knowledge and disseminate results to biomedical researchers in a user-friendly manner. We have built the canEvolve web portal to meet this need. Results: canEvolve query functionalities are designed to fulfill most frequent analysis needs of cancer researchers with a view to generate novel hypotheses. canEvolve stores gene, microRNA (miRNA) and protein expression profiles, copy number alterations for multiple cancer types, and protein-protein interaction information. canEvolve allows querying of results of primary analysis, integrative analysis and network analysis of oncogenomics data. The querying for primary analysis includes differential gene and miRNA expression as well as changes in gene copy number measured with SNP microarrays. canEvolve provides results of integrative analysis of gene expression profiles with copy number alterations and with miRNA profiles as well as generalized integrative analysis using gene set enrichment analysis. The network analysis capability includes storage and visualization of gene co-expression, inferred gene regulatory networks and protein-protein interaction information. Finally, canEvolve provides correlations between gene expression and clinical outcomes in terms of univariate survival analysis. Conclusion: At present canEvolve provides different types of information extracted from 90 cancer genomics studies comprising of more than 10,000 patients. The presence of multiple data types, novel integrative analysis for identifying regulators of oncogenesis, network analysis and ability to query gene lists/pathways are distinctive features of canEvolve. canEvolve will facilitate integrative and meta-analysis of oncogenomics datasets

In-depth Analysis of Alternative Splicing Landscape in Multiple Myeloma and Potential Role of Dysregulated Splicing Factors

Splicing changes are common in cancer and are associated with dysregulated splicing factors. Here, we analyzed RNA-seq data from 323 newly diagnosed multiple myeloma (MM) patients and described the alternative splicing (AS) landscape. We observed a large number of splicing pattern changes in MM cells compared to normal plasma cells (NPC). The most common events were alterations of mutually exclusive exons and exon skipping. Most of these events were observed in the absence of overall changes in gene expression and often impacted the coding potential of the alternatively spliced genes. To understand the molecular mechanisms driving frequent aberrant AS, we investigated 115 splicing factors (SFs) and associated them with the AS events in MM. We observed that ~40% of SFs were dysregulated in MM cells compared to NPC and found a significant enrichment of SRSF1, SRSF9, and PCB1 binding motifs around AS events. Importantly, SRSF1 overexpression was linked with shorter survival in two independent MM datasets and was correlated with the number of AS events, impacting tumor cell proliferation. Together with the observation that MM cells are vulnerable to splicing inhibition, our results may lay the foundation for developing new therapeutic strategies for MM. We have developed a web portal that allows custom alternative splicing event queries by using gene symbols and visualizes AS events in MM and subgroups. Our portals can be accessed at http://rconnect.dfci.harvard.edu/mmsplicing/ and https://rconnect.dfci.harvard.edu/mmleafcutter/

The shaping and functional consequences of the dosage effect landscape in multiple myeloma

Background: Multiple myeloma (MM) is a malignant proliferation of plasma B cells. Based on recurrent aneuploidy such as copy number alterations (CNAs), myeloma is divided into two subtypes with different CNA patterns and patient survival outcomes. How aneuploidy events arise, and whether they contribute to cancer cell evolution are actively studied. The large amount of transcriptomic changes resultant of CNAs (dosage effect) pose big challenges for identifying functional consequences of CNAs in myeloma in terms of specific driver genes and pathways. In this study, we hypothesize that gene-wise dosage effect varies as a result from complex regulatory networks that translate the impact of CNAs to gene expression, and studying this variation can provide insights into functional effects of CNAs. Results: We propose gene-wise dosage effect score and genome-wide karyotype plot as tools to measure and visualize concordant copy number and expression changes across cancer samples. We find that dosage effect in myeloma is widespread yet variable, and it is correlated with gene expression level and CNA frequencies in different chromosomes. Our analysis suggests that despite the enrichment of differentially expressed genes between hyperdiploid MM and non-hyperdiploid MM in the trisomy chromosomes, the chromosomal proportion of dosage sensitive genes is higher in the non-trisomy chromosomes. Dosage-sensitive genes are enriched by genes with protein translation and localization functions, and dosage resistant genes are enriched by apoptosis genes. These results point to future studies on differential dosage sensitivity and resistance of pro- and anti-proliferation pathways and their variation across patients as therapeutic targets and prognosis markers. Conclusions: Our findings support the hypothesis that recurrent CNAs in myeloma are selected by their functional consequences. The novel dosage effect score defined in this work will facilitate integration of copy number and expression data for identifying driver genes in cancer genomics studies. The accompanying R code is available at http://www.canevolve.org/dosageEffect/

Integrative analysis of gene and miRNA expression profiles with transcription factor–miRNA feed-forward loops identifies regulators in human cancers

We describe here a novel method for integrating gene and miRNA expression profiles in cancer using feed-forward loops (FFLs) consisting of transcription factors (TFs), miRNAs and their common target genes. The dChip-GemiNI (Gene and miRNA Network-based Integration) method statistically ranks computationally predicted FFLs by their explanatory power to account for differential gene and miRNA expression between two biological conditions such as normal and cancer. GemiNI integrates not only gene and miRNA expression data but also computationally derived information about TF–target gene and miRNA–mRNA interactions. Literature validation shows that the integrated modeling of expression data and FFLs better identifies cancer-related TFs and miRNAs compared to existing approaches. We have utilized GemiNI for analyzing six data sets of solid cancers (liver, kidney, prostate, lung and germ cell) and found that top-ranked FFLs account for ∼20% of transcriptome changes between normal and cancer. We have identified common FFL regulators across multiple cancer types, such as known FFLs consisting of MYC and miR-15/miR-17 families, and novel FFLs consisting of ARNT, CREB1 and their miRNA partners. The results and analysis web server are available at http://www.canevolve.org/dChip-GemiNi

Widespread intronic polyadenylation diversifies immune cell transcriptomes

Alternative cleavage and polyadenylation (ApA) is known to alter untranslated region (3ʹUTR) length but can also recognize intronic polyadenylation (IpA) signals to generate transcripts that lose part or all of the coding region. We analyzed 46 3ʹ-seq and RNA-seq profiles from normal human tissues, primary immune cells, and multiple myeloma (MM) samples and created an atlas of 4927 high-confidence IpA events represented in these cell types. IpA isoforms are widely expressed in immune cells, differentially used during B-cell development or in different cellular environments, and can generate truncated proteins lacking C-terminal functional domains. This can mimic ectodomain shedding through loss of transmembrane domains or alter the binding specificity of proteins with DNA-binding or protein–protein interaction domains. MM cells display a striking loss of IpA isoforms expressed in plasma cells, associated with shorter progression-free survival and impacting key genes in MM biology and response to lenalidomide

Comprehensive genomic analysis of refractory multiple myeloma reveals a complex mutational landscape associated with drug resistance and novel therapeutic vulnerabilities

The outcomes of patients with multiple myeloma (MM) refractory to immunomodulatory agents (IMiDs) and proteasome inhibitors (PIs) remain poor. In this study, we performed whole genome and transcriptome sequencing of 39 heavily pretreated relapsed/refractory MM (RRMM) patients to identify mechanisms of resistance and potential therapeutic targets. We observed a high mutational load and indications of increased genomic instability. Recurrently mutated genes in RRMM, which had not been previously reported or only observed at a lower frequency in newly diagnosed MM, included NRAS, BRAF, TP53, SLC4A7, MLLT4, EWSR1, HCFC2, and COPS3. We found multiple genomic regions with bi-allelic events affecting tumor suppressor genes and demonstrated a significant adverse impact of bi-allelic TP53 alterations on survival. With regard to potentially resistance conferring mutations, recurrently mutated gene networks included genes with relevance for PI and IMiD activity; the latter particularly affecting members of the Cereblon and the COP9 signalosome complex. We observed a major impact of signatures associated with exposure to melphalan or impaired DNA double-strand break homologous recombination repair in RRMM. The latter coincided with mutations in genes associated with PARP inhibitor sensitivity in 49% of RRMM patients; a finding with potential therapeutic implications. In conclusion, this comprehensive genomic characterization revealed a complex mutational and structural landscape in RRMM and highlights potential implications for therapeutic strategies

Targeting histone deacetylase 3 (HDAC3) in the bone marrow microenvironment inhibits multiple myeloma proliferation by modulating exosomes and IL-6 trans-signaling

Multiple myeloma (MM) is an incurable cancer that derives pro-survival/proliferative signals from the bone marrow (BM) niche. Novel agents targeting not only cancer cells, but also the BM-niche have shown the greatest activity in MM. Histone deacetylases (HDACs) are therapeutic targets in MM and we previously showed that HDAC3 inhibition decreases MM proliferation both alone and in co-culture with bone marrow stromal cells (BMSC). In this study, we investigate the effects of HDAC3 targeting in BMSCs. Using both BMSC lines as well as patient-derived BMSCs, we show that HDAC3 expression in BMSCs can be induced by co-culture with MM cells. Knock-out (KO), knock-down (KD), and pharmacologic inhibition of HDAC3 in BMSCs results in decreased MM cell proliferation; including in autologous cultures of patient MM cells with BMSCs. We identified both quantitative and qualitative changes in exosomes and exosomal miRNA, as well as inhibition of IL-6 trans-signaling, as molecular mechanisms mediating anti-MM activity. Furthermore, we show that HDAC3-KD in BM endothelial cells decreases neoangiogenesis, consistent with a broad effect of HDAC3 targeting in the BM-niche. Our results therefore support the clinical development of HDAC3 inhibitors based not only on their direct anti-MM effects, but also their modulation of the BM microenvironment