Network modeling of the transcriptional effects of copy number aberrations in glioblastoma

DNA copy number aberrations (CNAs) are a characteristic feature of cancer genomes. In this work, Rebecka Jörnsten, Sven Nelander and colleagues combine network modeling and experimental methods to analyze the systems-level effects of CNAs in glioblastoma

Integrative network modeling of large multidimensional cancer datasets

Our ability to conduct detailed molecular investigations on tissue samples have, during the past decade, enabled the formation of databases containing measurements from thousands of cancer tumors. To harness the potential of the amassing data sets, we introduce new modeling techniques and generalise existing methods for large-scale integration of cancer data. These methods aim to construct network models that link genetic, epigenetic, transcriptional and phenotypic events, by combining genome-wide measurements of multiple kinds. In paper I we constructed a modeling framework, EPoC, for creating causal networks be- tween gene copy number levels and mRNA expression, and applied it to data from the brain tumor glioblastoma. Some of the predicted regulators were tested in four glioblastoma-derived cell lines and confirmed that the network model could be used to find unknown regulators of cell growth in glioblastoma. In paper II we used data integrative network modeling to identify novel genetic, epige- netic and transcriptional regulators of glioblastoma subtypes. In addition to confirming known regulators of gliomagenesis, the model also predicted that Annexin A2 (ANXA2) promoter methylation and mRNA expression were linked to the signature target genes of the clinically aggressive mesenchymal molecular subtype. Our findings were validated by knockdown of ANXA2 in glioblastoma-derived cell cultures. Paper III presents an extension of sparse inverse covariance selection (SICS), which is adapted and optimized for modeling of genetic, epigenetic, and transcriptional data across mul- tiple cancer types. To evaluate the potential of the method, we applied it to data from eight cancers available in The Cancer Genome Atlas and published the model online at cancerland- scapes.org for anyone to explore. The derived multi-cancer model detected known interactions and contained interesting predictions, including functionally coupled network structures shared between cancers. In summary, we use network modeling of cancer to identify possible drug targets, drivers of molecular subclasses, and reveal similarities and differences between cancer types. The developed tools for network construction can assist in further investigation of the cancer genome, potentially including other data sources and additional cancer diagnoses

c-Jun-N-terminal phosphorylation regulates DNMT1 expression and genome wide methylation in gliomas

High-grade gliomas (HGG) are the most common brain tumors, with an average survival time of 14 months. A glioma-CpG island methylator phenotype (G-CIMP), associated with better clinical outcome, has been described in low and high-grade gliomas. Mutation of IDH1 is known to drive the G-CIMP status. In some cases, however, the hypermethylation phenotype is independent of IDH1 mutation, suggesting the involvement of other mechanisms. Here, we demonstrate that DNMT1 expression is higher in low-grade gliomas compared to glioblastomas and correlates with phosphorylated c-Jun. We show that phospho-c-Jun binds to the DNMT1 promoter and causes DNA hypermethylation. Phospho-c-Jun activation by Anisomycin treatment in primary glioblastoma-derived cells attenuates the aggressive features of mesenchymal glioblastomas and leads to promoter methylation and downregulation of key mesenchymal genes (CD44, MMP9 and CHI3L1). Our findings suggest that phospho-c-Jun activates an important regulatory mechanism to control DNMT1 expression and regulate global DNA methylation in Glioblastoma

Searching for Synergies : Matrix Algebraic Approaches for Efficient Pair Screening

Functionally interacting perturbations, such as synergistic drugs pairs or synthetic lethal gene pairs, are of key interest in both pharmacology and functional genomics. However, to find such pairs by traditional screening methods is both time consuming and costly. We present a novel computational-experimental framework for efficient identification of synergistic target pairs, applicable for screening of systems with sizes on the order of current drug, small RNA or SGA (Synthetic Genetic Array) libraries (>1000 targets). This framework exploits the fact that the response of a drug pair in a given system, or a pair of genes' propensity to interact functionally, can be partly predicted by computational means from (i) a small set of experimentally determined target pairs, and (ii) pre-existing data (e.g. gene ontology, PPI) on the similarities between targets. Predictions are obtained by a novel matrix algebraic technique, based on cyclical projections onto convex sets. We demonstrate the efficiency of the proposed method using drug-drug interaction data from seven cancer cell lines and gene-gene interaction data from yeast SGA screens. Our protocol increases the rate of synergism discovery significantly over traditional screening, by up to 7-fold. Our method is easy to implement and could be applied to accelerate pair screening for both animal and microbial systems

Efficient exploration of pan-cancer networks by generalized covariance selection and interactive web content

Statistical network modeling techniques are increasingly important tools to analyze cancer genomics data. However, current tools and resources are not designed to work across multiple diagnoses and technical platforms, thus limiting their applicability to comprehensive pan-cancer datasets such as The Cancer Genome Atlas (TCGA). To address this, we describe a new data driven modeling method, based on generalized Sparse Inverse Covariance Selection (SICS). The method integrates genetic, epigenetic and transcriptional data from multiple cancers, to define links that are present in multiple cancers, a subset of cancers, or a single cancer. It is shown to be statistically robust and effective at detecting direct pathway links in data from TCGA. To facilitate interpretation of the results, we introduce a publicly accessible tool ( ext-link-type="uri" xlink:href="http://cancerlandscapes.org/">cancerlandscapes.org), in which the derived networks are explored as interactive web content, linked to several pathway and pharmacological databases. To evaluate the performance of the method, we constructed a model for eight TCGA cancers, using data from 3900 patients. The model rediscovered known mechanisms and contained interesting predictions. Possible applications include prediction of regulatory relationships, comparison of network modules across multiple forms of cancer and identification of drug targets

Efficient exploration of pan-cancer networks by generalized covariance selection and interactive web content

Statistical network modeling techniques are increasingly important tools to analyze cancer genomics data. However, current tools and resources are not designed to work across multiple diagnoses and technical platforms, thus limiting their applicability to comprehensive pan-cancer datasets such as The Cancer Genome Atlas (TCGA). To address this, we describe a new data driven modeling method, based on generalized Sparse Inverse Covariance Selection (SICS). The method integrates genetic, epigenetic and transcriptional data from multiple cancers, to define links that are present in multiple cancers, a subset of cancers, or a single cancer. It is shown to be statistically robust and effective at detecting direct pathway links in data from TCGA. To facilitate interpretation of the results, we introduce a publicly accessible tool ( ext-link-type="uri" xlink:href="http://cancerlandscapes.org/">cancerlandscapes.org), in which the derived networks are explored as interactive web content, linked to several pathway and pharmacological databases. To evaluate the performance of the method, we constructed a model for eight TCGA cancers, using data from 3900 patients. The model rediscovered known mechanisms and contained interesting predictions. Possible applications include prediction of regulatory relationships, comparison of network modules across multiple forms of cancer and identification of drug targets

System-scale network modeling of cancer using EPoC

One of the central problems of cancer systems biology is to understand the complex molecular changes of cancerous cells and tissues, and use this understanding to support the development of new targeted therapies. EPoC (Endogenous Perturbation analysis of Cancer) is a network modeling technique for tumor molecular profiles. EPoC models are constructed from combined copy number aberration (CNA) and mRNA data and aim to (1) identify genes whose copy number aberrations significantly affect target mRNA expression and (2) generate markers for long- and short-term survival of cancer patients. Models are constructed by a combination of regression and bootstrapping methods. Prognostic scores are obtained from a singular value decomposition of the networks. We have previously analyzed the performance of EPoC using glioblastoma data from The Cancer Genome Atlas (TCGA) consortium, and have shown that resulting network models contain both known and candidate disease-relevant genes as network hubs, as well as uncover predictors of patient survival. Here, we give a practical guide how to perform EPoC modeling in practice using R, and present a set of alternative modeling frameworks

System-scale network modeling of cancer using EPoC

One of the central problems of cancer systems biology is to understand the complex molecular changes of cancerous cells and tissues, and use this understanding to support the development of new targeted therapies. EPoC (Endogenous Perturbation analysis of Cancer) is a network modeling technique for tumor molecular profiles. EPoC models are constructed from combined copy number aberration (CNA) and mRNA data and aim to (1) identify genes whose copy number aberrations significantly affect target mRNA expression and (2) generate markers for long- and short-term survival of cancer patients. Models are constructed by a combination of regression and bootstrapping methods. Prognostic scores are obtained from a singular value decomposition of the networks. We have previously analyzed the performance of EPoC using glioblastoma data from The Cancer Genome Atlas (TCGA) consortium, and have shown that resulting network models contain both known and candidate disease-relevant genes as network hubs, as well as uncover predictors of patient survival. Here, we give a practical guide how to perform EPoC modeling in practice using R, and present a set of alternative modeling frameworks

Predicting synergism scores from highly incomplete data via cyclical set projection.

<p>A: To improve screening efficiency further, we introduce a projection-based predictor of synergism scores. An initial guess of a synergism score matrix is projected first onto the set , which corresponds to known interaction scores, then onto the set , which contains matrices of approximately low rank, and finally onto , holding the matrices consistent with known functional similarity. The projections are applied cyclically until convergence to a final prediction of is reached, which is guaranteed due to convexity of the three sets (here illustrating convergence in one iteration). B: Prediction accuracy in five glioblastoma cell lines and reference data sets. Comparison between our projection-based method and two state-of-the-art methods for interaction score imputation methods, LLS and EMDI. Generally, set based projections outperform the other methods (predictions correlate more with true values), especially when the screened fraction is small.</p

Improving screening efficiency by combining propensity-based sampling with interaction score prediction via matrix completion.

<p>A: We extend the simpler protocol (propensity-based sampling only, <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0068598#pone-0068598-g001" target="_blank">Figure 1C</a>), adding a projection-based predictor to choose likely synergistic pairs (steps 3 and 4). If the prediction-driven screening discovery rate is higher than the preceding propensity-based screening, a new prediction-driven screening cycle is started (step 5). We switch between propensity-based sampling and prediction to increase the fractional discovery rate. B: Fractional discovery rate across 9 data sets show marked improvement over brute-force screening. C: Estimates of the screening efficiency demonstrate that the full protocol (steps 1–5) gives better performance than propensity-based sampling only (steps 1–2). Yellow block: additional contribution by projecting onto in the largest yeast SGA screen.</p