Computational approaches for dissecting cancer pathways from insertional mutagenesis data

Advances in the field of molecular biology have resulted in a decent understanding of the causes for and mechanisms through which healthy cells can develop into cancer cells. It is, for instance, well established that cancer is caused by mutation of so-called cancer genes. That said, current knowledge on exactly which genes can function as a cancer gene is far from complete. Fur- thermore, the regulatory pathways through which cancer genes exhibit their malicious effect on healthy cell division remain largely elusive. To identify novel cancer genes, insertional mutagenesis screens can be employed. In these screens tumors are induced by viral mutations in the DNA of a mouse. Since these mutations are likely to be observed in the vicinity of cancer genes this is a fruitful method of cancer gene discovery. The computational approaches proposed in this thesis are primarily aimed at analyzing in- sertional mutagenesis data. To detect commonly mutated regions in the mouse genome, which point to novel cancer genes, we employ a kernel convolution framework. The major advan- tage of this method is that the data is analyzed in a scale-space, which allows the detection of regions of various widths. Furthermore, the probability of making an error is controlled inde- pendent of the width of a commonly mutated region, making the framework suitable for the analysis of large screens. This framework can also be applied to detect commonly co-occurring mutations, which reveal possible collaboration between cancer genes. We also elaborate on additional applications and generalizations of the scale-space framework for analyzing other types of biomolecular data. To delineate the pathways through which cancer genes act, we propose a mutational ge- nomics approach. To this end, the mutation data is complemented with gene expression data measured in the same samples. This enables the inference of associations between the presence or absence of an insertion and the gene expression. In this thesis we explore the use of Boolean association models that combine multiple mutated loci to predict gene expression levels. These models are also applied to a genetical genomics dataset. The discovered associations provide insight into how (cancer) genes are connected in cellular regulatory pathways.MediamaticsElectrical Engineering, Mathematics and Computer Scienc

FERAL: Network-based classifier with application to breast cancer outcome prediction

Motivation: Breast cancer outcome prediction based on gene expression profiles is an important strategy for personalize patient care. To improve performance and consistency of discovered markers of the initial molecular classifiers, network-based outcome prediction methods (NOPs) have been proposed. In spite of the initial claims, recent studies revealed that neither performance nor consistency can be improved using these methods. NOPs typically rely on the construction of meta-genes by averaging the expression of several genes connected in a network that encodes protein interactions or pathway information. In this article, we expose several fundamental issues in NOPs that impede on the prediction power, consistency of discovered markers and obscures biological interpretation. Results: To overcome these issues, we propose FERAL, a network-based classifier that hinges upon the Sparse Group Lasso which performs simultaneous selection of marker genes and training of the prediction model. An important feature of FERAL, and a significant departure from existing NOPs, is that it uses multiple operators to summarize genes into meta-genes. This gives the classifier the opportunity to select the most relevant meta-gene for each gene set. Extensive evaluation revealed that the discovered markers are markedly more stable across independent datasets. Moreover, interpretation of the marker genes detected by FERAL reveals valuable mechanistic insight into the etiology of breast cancer.Intelligent SystemsElectrical Engineering, Mathematics and Computer Scienc

Designerly Ways of Exploring Crowds

In this article, we present examples of using designerly ways to explore “crowd” phenomenon in a cross-disciplinary project named EWiDS. The phrase ‘designerly ways’ refers to visual communication methods such as drawings and videos, which are widely acknowledged as effective approaches to crossdisciplinary collaboration. This study started with designerly ways of exploring crowd experience and crowd management. Three crowd situations, public transportation, outdoor event, and indoor event, were selected as representative crowds that are distinguishable by crowd size, level of interaction, and emotional intensity. Current activities and problems in these crowds were visualized, and some possible solutions were presented as scenarios. These visualizations and scenarios were used as conversation stimulators in a plenary meeting of EWiDS and received positive feedback on their effectiveness in assisting project members in communication.Industrial DesignIndustrial Design Engineerin

Prognostic Molecular Classification of Breast Cancer Based on Features Extracted from a Scale Space

Breast cancer is one of the most prevalent cancers affecting females in the world. In recent years, many cancer researchers have been trying to determine molecular prognosis tools that predict cancer patient treatment response and/or chance of survival. In particular, the determination of gene expression signatures obtained by feature selection methods applied to large microarray datasets has shown potential. The main purpose of this study is to extend these gene signatures and molecular prognostic classifiers by investigating features constructed from a scale-space representation of the microarray data. Here, we construct a scale space by first mapping all genes to a one-dimensional functional space using protein family information. Next, we applied successive smoothing to the expression values resulting in one scale-space representation of the gene expression data from one sample. At the lowest scale, the scale space contains the original gene expression values, whereas at higher scales meta-features are formed, which are weighted sums of groups of genes. To test whether a scale-space representation is useful we performed feature selection and classification on a publicly available breast cancer expression dataset. We found that, instead of signatures consisting of single genes, meta-genes (i.e. groups of genes) that exist at higher scales were preferentially selected. We furthermore determined cross-validation errors using seven distinct classifiers (NMC, LDC, QDC, FISHERC, PARZENC, 3NNC, and LOGLC) and found that better performance is obtained using the scale-space representation than with the traditional representation of the gene expression data. As a result, we conclude that the scale-space analysis constitutes a potent way of selecting molecular signatures and is useful for prognostic classification.Pattern Recognition and BioinformaticsIntelligent SystemsElectrical Engineering, Mathematics and Computer Scienc

How Can Procurement Contribute to Network Performance? Streamlining Network, Project and Procurement Objectives

The core business of governmental organizations like Rijkswaterstaat in the Netherlands is the optimal management of road-end waterway networks. The coming years many maintenance, renewal and extension projects will be executed in these networks. Projects give a disturbance in functionality of the network. Network management is to keep this disturbance as low as possible and make functionality of the networks as high as possible. In reality however projects define their own objectives. To realize projects market involvement is necessary. Rijkswaterstaat has a procurement policy which aims at optimizing the transaction i.e. best quality for a competitive prize. However, through projects and transactions objectives seem to fade away from the core business of Rijkswaterstaat. Involvement of the market in public networks is about finding the right balance between keeping control on product and production processes and shifting freedom in design and related responsibility to the market. The more freedom is given to the market in the project transaction, the more difficult it is to manage the network on super project level. Dutch government policy tends to shift more and more freedom and responsibility to the market. A comparison with other types of networks shows that this is adverse to the policy in more business driven networks. Disturbances are kept as short and controlled as possible. The main question in this paper is how value optimization in projects and procurement can add value for network governance in public networks and at the same time leave enough design freedom to the market for the development of a more resource based construction industry in infrastructure. From the comparison of different networks potential instruments are discussed.Civil Engineering and Geoscience

Scale-space measures for graph topology link protein network architecture to function

MOTIVATION: The network architecture of physical protein interactions is an important determinant for the molecular functions that are carried out within each cell. To study this relation, the network architecture can be characterized by graph topological characteristics such as shortest paths and network hubs. These characteristics have an important shortcoming: they do not take into account that interactions occur across different scales. This is important because some cellular functions may involve a single direct protein interaction (small scale), whereas others require more and/or indirect interactions, such as protein complexes (medium scale) and interactions between large modules of proteins (large scale). RESULTS: In this work, we derive generalized scale-aware versions of known graph topological measures based on diffusion kernels. We apply these to characterize the topology of networks across all scales simultaneously, generating a so-called graph topological scale-space. The comprehensive physical interaction network in yeast is used to show that scale-space based measures consistently give superior performance when distinguishing protein functional categories and three major types of functional interactions-genetic interaction, co-expression and perturbation interactions. Moreover, we demonstrate that graph topological scale spaces capture biologically meaningful features that provide new insights into the link between function and protein network architecture.Intelligent SystemsElectrical Engineering, Mathematics and Computer Scienc

3D hotspots of recurrent retroviral insertions reveal long-range interactions with cancer genes

Genomically distal mutations can contribute to the deregulation of cancer genes by engaging in chromatin interactions. To study this, we overlay viral cancer-causing insertions obtained in a murine retroviral insertional mutagenesis screen with genome-wide chromatin conformation capture data. Here we find that insertions tend to cluster in 3D hotspots within the nucleus. The identified hotspots are significantly enriched for known cancer genes, and bear the expected characteristics of bona fide regulatory interactions, such as enrichment for transcription factor-binding sites. In addition, we observe a striking pattern of mutual exclusive integration. This is an indication that insertions in these loci target the same gene, either in their linear genomic vicinity or in their 3D spatial vicinity. Our findings shed new light on the repertoire of targets obtained from insertional mutagenesis screening and underline the importance of considering the genome as a 3D structure when studying effects of genomic perturbations.Intelligent SystemsElectrical Engineering, Mathematics and Computer Scienc

Presence: Where ar we? Editorial

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Integration of gene expression and DNA-methylation profiles improves molecular subtype classification in acute myeloid leukemia

Background Acute Myeloid Leukemia (AML) is characterized by various cytogenetic and molecular abnormalities. Detection of these abnormalities is important in the risk-classification of patients but requires laborious experimentation. Various studies showed that gene expression profiles (GEP), and the gene signatures derived from GEP, can be used for the prediction of subtypes in AML. Similarly, successful prediction was also achieved by exploiting DNA-methylation profiles (DMP). There are, however, no studies that compared classification accuracy and performance between GEP and DMP, neither are there studies that integrated both types of data to determine whether predictive power can be improved. Approach Here, we used 344 well-characterized AML samples for which both gene expression and DNA-methylation profiles are available. We created three different classification strategies including early, late and no integration of these datasets and used them to predict AML subtypes using a logistic regression model with Lasso regularization. Results We illustrate that both gene expression and DNA-methylation profiles contain distinct patterns that contribute to discriminating AML subtypes and that an integration strategy can exploit these patterns to achieve synergy between both data types. We show that concatenation of features from both data sets, i.e. early integration, improves the predictive power compared to classifiers trained on GEP or DMP alone. A more sophisticated strategy, i.e. the late integration strategy, employs a two-layer classifier which outperforms the early integration strategy. Conclusion We demonstrate that prediction of known cytogenetic and molecular abnormalities in AML can be further improved by integrating GEP and DMP profiles.Intelligent SystemsElectrical Engineering, Mathematics and Computer Scienc

Detecting recurrent gene mutation in interaction network context using multi-scale graph diffusion

Background Delineating the molecular drivers of cancer, i.e. determining cancer genes and the pathways which they deregulate, is an important challenge in cancer research. In this study, we aim to identify pathways of frequently mutated genes by exploiting their network neighborhood encoded in the protein-protein interaction network. To this end, we introduce a multi-scale diffusion kernel and apply it to a large collection of murine retroviral insertional mutagenesis data. The diffusion strength plays the role of scale parameter, determining the size of the network neighborhood that is taken into account. As a result, in addition to detecting genes with frequent mutations in their genomic vicinity, we find genes that harbor frequent mutations in their interaction network context. Results We identify densely connected components of known and putatively novel cancer genes and demonstrate that they are strongly enriched for cancer related pathways across the diffusion scales. Moreover, the mutations in the clusters exhibit a significant pattern of mutual exclusion, supporting the conjecture that such genes are functionally linked. Using multi-scale diffusion kernel, various infrequently mutated genes are found to harbor significant numbers of mutations in their interaction network neighborhood. Many of them are well-known cancer genes. Conclusions The results demonstrate the importance of defining recurrent mutations while taking into account the interaction network context. Importantly, the putative cancer genes and networks detected in this study are found to be significant at different diffusion scales, confirming the necessity of a multi-scale analysis.Intelligent SystemsElectrical Engineering, Mathematics and Computer Scienc