The whole-genome landscape of medulloblastoma subtypes.

Current therapies for medulloblastoma, a highly malignant childhood brain tumour, impose debilitating effects on the developing child, and highlight the need for molecularly targeted treatments with reduced toxicity. Previous studies have been unable to identify the full spectrum of driver genes and molecular processes that operate in medulloblastoma subgroups. Here we analyse the somatic landscape across 491 sequenced medulloblastoma samples and the molecular heterogeneity among 1,256 epigenetically analysed cases, and identify subgroup-specific driver alterations that include previously undiscovered actionable targets. Driver mutations were confidently assigned to most patients belonging to Group 3 and Group 4 medulloblastoma subgroups, greatly enhancing previous knowledge. New molecular subtypes were differentially enriched for specific driver events, including hotspot in-frame insertions that target KBTBD4 and 'enhancer hijacking' events that activate PRDM6. Thus, the application of integrative genomics to an extensive cohort of clinical samples derived from a single childhood cancer entity revealed a series of cancer genes and biologically relevant subtype diversity that represent attractive therapeutic targets for the treatment of patients with medulloblastoma.

A genome-scale in vivo loss-of-function screen identifies Phf6 as a lineage-specific regulator of leukemia cell growth.

We performed a genome-scale shRNA screen for modulators of B-cell leukemia progression in vivo. Results from this work revealed dramatic distinctions between the relative effects of shRNAs on the growth of tumor cells in culture versus in their native microenvironment. Specifically, we identified many "context-specific" regulators of leukemia development. These included the gene encoding the zinc finger protein Phf6. While inactivating mutations in PHF6 are commonly observed in human myeloid and T-cell malignancies, we found that Phf6 suppression in B-cell malignancies impairs tumor progression. Thus, Phf6 is a "lineage-specific" cancer gene that plays opposing roles in developmentally distinct hematopoietic malignancies.

Early epigenomic and transcriptional changes reveal Elk-1 transcription factor as a therapeutic target in Huntington's disease.

Huntington's disease (HD) is a chronic neurodegenerative disorder characterized by a late clinical onset despite ubiquitous expression of the mutant Huntingtin gene (HTT) from birth. Transcriptional dysregulation is a pivotal feature of HD. Yet, the genes that are altered in the prodromal period and their regulators, which present opportunities for therapeutic intervention, remain to be elucidated. Using transcriptional and chromatin profiling, we found aberrant transcription and changes in histone H3K27acetylation in the striatum of R6/1 mice during the presymptomatic disease stages. Integrating these data, we identified the Elk-1 transcription factor as a candidate regulator of prodromal changes in HD. Exogenous expression of Elk-1 exerted beneficial effects in a primary striatal cell culture model of HD, and adeno-associated virus-mediated Elk-1 overexpression alleviated transcriptional dysregulation in R6/1 mice. Collectively, our work demonstrates that aberrant gene expression precedes overt disease onset in HD, identifies the Elk-1 transcription factor as a key regulator linked to early epigenetic and transcriptional changes in HD, and presents evidence for Elk-1 as a target for alleviating molecular pathology in HD.

Extending the limits of quantitative proteome profiling with data-independent acquisition and application to acetaminophen-treated three-dimensional liver microtissues.

The data-independent acquisition (DIA) approach has recently been introduced as a novel mass spectrometric method that promises to combine the high content aspect of shotgun proteomics with the reproducibility and precision of selected reaction monitoring. Here, we evaluate, whether SWATH-MS type DIA effectively translates into a better protein profiling as compared with the established shotgun proteomics. We implemented a novel DIA method on the widely used Orbitrap platform and used retention-time-normalized (iRT) spectral libraries for targeted data extraction using Spectronaut. We call this combination hyper reaction monitoring (HRM). Using a controlled sample set, we show that HRM outperformed shotgun proteomics both in the number of consistently identified peptides across multiple measurements and quantification of differentially abundant proteins. The reproducibility of HRM in peptide detection was above 98%, resulting in quasi complete data sets compared with 49% of shotgun proteomics. Utilizing HRM, we profiled acetaminophen (APAP)(1)-treated three-dimensional human liver microtissues. An early onset of relevant proteome changes was revealed at subtoxic doses of APAP. Further, we detected and quantified for the first time human NAPQI-protein adducts that might be relevant for the toxicity of APAP. The adducts were identified on four mitochondrial oxidative stress related proteins (GATM, PARK7, PRDX6, and VDAC2) and two other proteins (ANXA2 and FTCD). Our findings imply that DIA should be the preferred method for quantitative protein profiling.

D-Light on promoters: a client-server system for the analysis and visualization of cis-regulatory elements.

BACKGROUND: The binding of transcription factors to DNA plays an essential role in the regulation of gene expression. Numerous experiments elucidated binding sequences which subsequently have been used to derive statistical models for predicting potential transcription factor binding sites (TFBS). The rapidly increasing number of genome sequence data requires sophisticated computational approaches to manage and query experimental and predicted TFBS data in the context of other epigenetic factors and across different organisms. RESULTS: We have developed D-Light, a novel client-server software package to store and query large amounts of TFBS data for any number of genomes. Users can add small-scale data to the server database and query them in a large scale, genome-wide promoter context. The client is implemented in Java and provides simple graphical user interfaces and data visualization. Here we also performed a statistical analysis showing what a user can expect for certain parameter settings and we illustrate the usage of D-Light with the help of a microarray data set. CONCLUSIONS: D-Light is an easy to use software tool to integrate, store and query annotation data for promoters. A public D-Light server, the client and server software for local installation and the source code under GNU GPL license are available at http://biwww.che.sbg.ac.at/dlight.

Proteomics, Post-translational Modifications, and Integrative Analyses Reveal Molecular Heterogeneity within Medulloblastoma Subgroups.

There is a pressing need to identify therapeutic targets in tumors with low mutation rates such as the malignant pediatric brain tumor medulloblastoma. To address this challenge, we quantitatively profiled global proteomes and phospho-proteomes of 45 medulloblastoma samples. Integrated analyses revealed that tumors with similar RNA expression vary extensively at the post-transcriptional and post-translational levels. We identified distinct pathways associated with two subsets of SHH tumors, and found post-translational modifications of MYC that are associated with poor outcomes in group 3 tumors. We found kinases associated with subtypes and showed that inhibiting PRKDC sensitizes MYC-driven cells to radiation. Our study shows that proteomics enables a more comprehensive, functional readout, providing a foundation for future therapeutic strategies.

DiSCoVERing Innovative Therapies for Rare Tumors: Combining Genetically Accurate Disease Models with In Silico Analysis to Identify Novel Therapeutic Targets.

PURPOSE: We used human stem and progenitor cells to develop a genetically accurate novel model of MYC-driven Group 3 medulloblastoma. We also developed a new informatics method, Disease-model Signature versus Compound-Variety Enriched Response ("DiSCoVER"), to identify novel therapeutics that target this specific disease subtype. EXPERIMENTAL DESIGN: Human neural stem and progenitor cells derived from the cerebellar anlage were transduced with oncogenic elements associated with aggressive medulloblastoma. An in silico analysis method for screening drug sensitivity databases (DiSCoVER) was used in multiple drug sensitivity datasets. We validated the top hits from this analysis in vitro and in vivo RESULTS: Human neural stem and progenitor cells transformed with c-MYC, dominant-negative p53, constitutively active AKT and hTERT formed tumors in mice that recapitulated Group 3 medulloblastoma in terms of pathology and expression profile. DiSCoVER analysis predicted that aggressive MYC-driven Group 3 medulloblastoma would be sensitive to cyclin-dependent kinase (CDK) inhibitors. The CDK 4/6 inhibitor palbociclib decreased proliferation, increased apoptosis, and significantly extended the survival of mice with orthotopic medulloblastoma xenografts. CONCLUSIONS: We present a new method to generate genetically accurate models of rare tumors, and a companion computational methodology to find therapeutic interventions that target them. We validated our human neural stem cell model of MYC-driven Group 3 medulloblastoma and showed that CDK 4/6 inhibitors are active against this subgroup. Our results suggest that palbociclib is a potential effective treatment for poor prognosis MYC-driven Group 3 medulloblastoma tumors in carefully selected patients. Clin Cancer Res; 22(15); 3903-14. ©2016 AACR.

Computational prediction of protein-protein interactions.

The prediction of protein-protein interactions and kinase-specific phosphorylation sites on individual proteins is critical for correctly placing proteins within signaling pathways and networks. The importance of this type of annotation continues to increase with the continued explosion of genomic and proteomic data, particularly with emerging data categorizing posttranslational modifications on a large scale. A variety of computational tools are available for this purpose. In this chapter, we review the general methodologies for these types of computational predictions and present a detailed user-focused tutorial of one such method and computational tool, Scansite, which is freely available to the entire scientific community over the Internet.

A Multivariate Computational Method to Analyze High-Content RNAi Screening Data.

High-content screening (HCS) using RNA interference (RNAi) in combination with automated microscopy is a powerful investigative tool to explore complex biological processes. However, despite the plethora of data generated from these screens, little progress has been made in analyzing HC data using multivariate methods that exploit the full richness of multidimensional data. We developed a novel multivariate method for HCS, multivariate robust analysis method (M-RAM), integrating image feature selection with ranking of perturbations for hit identification, and applied this method to an HC RNAi screen to discover novel components of the DNA damage response in an osteosarcoma cell line. M-RAM automatically selects the most informative phenotypic readouts and time points to facilitate the more efficient design of follow-up experiments and enhance biological understanding. Our method outperforms univariate hit identification and identifies relevant genes that these approaches would have missed. We found that statistical cell-to-cell variation in phenotypic responses is an important predictor of hits in RNAi-directed image-based screens. Genes that we identified as modulators of DNA damage signaling in U2OS cells include B-Raf, a cancer driver gene in multiple tumor types, whose role in DNA damage signaling we confirm experimentally, and multiple subunits of protein kinase A.

Phosphorylation of ETS1 by Src family kinases prevents its recognition by the COP1 tumor suppressor.

Oncoproteins and tumor suppressors antagonistically converge on critical nodes governing neoplastic growth, invasion, and metastasis. We discovered that phosphorylation of the ETS1 and ETS2 transcriptional oncoproteins at specific serine or threonine residues creates binding sites for the COP1 tumor suppressor protein, which is an ubiquitin ligase component, leading to their destruction. In the case of ETS1, however, phosphorylation of a neighboring tyrosine residue by Src family kinases disrupts COP1 binding, thereby stabilizing ETS1. Src-dependent accumulation of ETS1 in breast cancer cells promotes anchorage-independent growth in vitro and tumor growth in vivo. These findings expand the list of potential COP1 substrates to include proteins whose COP1-binding sites are subject to regulatory phosphorylation and provide insights into transformation by Src family kinases.

Spatial exclusivity combined with positive and negative selection of phosphorylation motifs is the basis for context-dependent mitotic signaling.

The timing and localization of events during mitosis are controlled by the regulated phosphorylation of proteins by the mitotic kinases, which include Aurora A, Aurora B, Nek2 (never in mitosis kinase 2), Plk1 (Polo-like kinase 1), and the cyclin-dependent kinase complex Cdk1/cyclin B. Although mitotic kinases can have overlapping subcellular localizations, each kinase appears to phosphorylate its substrates on distinct sites. To gain insight into the relative importance of local sequence context in kinase selectivity, identify previously unknown substrates of these five mitotic kinases, and explore potential mechanisms for substrate discrimination, we determined the optimal substrate motifs of these major mitotic kinases by positional scanning oriented peptide library screening (PS-OPLS). We verified individual motifs with in vitro peptide kinetic studies and used structural modeling to rationalize the kinase-specific selection of key motif-determining residues at the molecular level. Cross comparisons among the phosphorylation site selectivity motifs of these kinases revealed an evolutionarily conserved mutual exclusion mechanism in which the positively and negatively selected portions of the phosphorylation motifs of mitotic kinases, together with their subcellular localizations, result in proper substrate targeting in a coordinated manner during mitosis.