66 research outputs found

    Recent Developments in Cancer Systems Biology

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    This ebook includes original research articles and reviews to update readers on the state of the art systems approach to not only discover novel diagnostic and prognostic biomarkers for several cancer types, but also evaluate methodologies to map out important genomic signatures. In addition, therapeutic targets and drug repurposing have been emphasized for a variety of cancer types. In particular, new and established researchers who desire to learn about cancer systems biology and why it is possibly the leading front to a personalized medicine approach will enjoy reading this book

    Novel quantitative proteomic approaches to reveal new driving mechanisms and biomarkers of tumorigenesis

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    Understanding the biological processes and pathways important to healthy and disease states requires the probing of systems at the protein level. While genetic alterations may predict the likelihood of disease, it is the phenotypic changes which characterize disease onset. The growth of mass spectrometry-based proteomics over the past two decades has been essential for our ability to perform systems-level interrogations of both healthy and disease states. Here, we have employed quantitative proteomic techniques to investigate breast cancer and glioma biology. Chapter 1 provides background on breast cancer heterogeneity and the challenges in classifying breast tumors in a clinically relevant manner. It additionally presents an overview of mass-spectrometry based proteomics and methods of proteomic quantitation. Chapter 2 presents a novel workflow for the identification of new liquid biopsy biomarkers for breast cancer prognosis. We developed a multi-omic approach linking oncogenic secreted proteins to patient-specific transcriptomic data and clinical outcomes. Kaplan-Meier analysis of genes having a secretion correlated expression pattern was used to identify new liquid biopsy biomarkers for predicting individualized prognosis. Chapter 3 explores the role of spliceosome protein SNRPD1 in breast cancer. SNRPD1 is often overexpressed in breast cancer and plays a seemingly important, but understudied, role in tumor aggressiveness. We have performed the first systems-level study of SNRPD1 function in breast cancer cells and identify previously unreported signaling pathways and biological functions impacted by SNRPD1. Chapter 4 introduces a novel approach to the study of secreted proteins termed outside-in proteomics. We first perform an unbiased screen of the secretome to identify phenotypic-relevant markers and use those results to guide our investigation of the intracellular proteome. We applied this approach to analyze the role of EZH2 in the aggressive phenotype of triple negative breast cancer. Chapter 5 presents a quantitative proteomic comparison of IDH1 mutant and wild type glioma cells. We performed a proteomic screen of IDH1 mutant versus wild type gliomas, and we report several new pathways and processes which may contribute to IDH1 mutant glioma pathogenesis or progression. Overall, this work demonstrates both novel and traditional mass spectrometric proteomic techniques for the investigation of human disease.Doctor of Philosoph

    Topics in cancer genomics

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    Large-scale projects such as the The Cancer Genome Atlas (TCGA) have generated extensive exome libraries across several disease types and populations. Detection of somatic changes in HLA genes by whole-exome sequencing (WES) has been complicated by the highly polymorphic nature of these loci. We developed a method POLYSOLVER (POLYmorphic loci reSOLVER) for accurate inference of class I HLA-A, -B and -C alleles from WES data, and achieved 97% accuracy at protein level resolution when this was applied to 133 HapMap samples of known HLA type. By applying POLYSOLVER in conjunction with somatic change detection tools to 2688 tumor/normal pairs TCGA that were previously analyzed by conventional approaches, we re-discovered 37 of 56 (66%) HLA mutations, while further identifying 23 new events. An analysis of WES data from a larger set of 3768 tumor/normal pairs by POLYSOLVER revealed 131 class I mutations with an enrichment for potentially loss-of-function events. 3% of samples had at least one HLA event with 95 of 131 mutations in the T cell interacting and peptide binding domains. Recurrent hotspot sites of missense, nonsense and splice site mutations were discovered that suggest positive selection, and support immune evasion as an important pathway in cancer. Exome sequencing has also revealed a large number of shared and personal somatic mutations across human cancers. In principle, any genetic alteration affecting a protein-coding region has the potential to generate mutated peptides that are presented by surface HLA class I proteins that might be recognized by cytotoxic T cells. Utilizing POLYSOLVER in conjunction with knowledge of mutations in other genetic loci inferred from exome data, we developed a pipeline for the prediction and validation of such neoantigens derived from individual tumors and presented by patient-specific alleles of the HLA proteins. We applied our computational pipeline to 91 chronic lymphocytic leukemias (CLL) that had undergone whole-exome sequencing. We predicted ~22 mutated HLA-binding peptides per leukemia (derived from ~16 missense mutations), and experimentally confirmed HLA binding for ~55% of such peptides. Finally, we computationally predicted HLA-binding peptides with missense or frameshift mutations for several cancer types and predicted dozens to thousands of neoantigens per individual tumor, suggesting that neoantigens are frequent in most tumors. The neoantigen prediction pipeline can also elucidate the neoantigens unique to a particular cancer patient and help in the design of personalized immune vaccines. MicroRNAs (miRs) are a class of non-coding small RNAs that regulate gene expression by promoting mRNA degradation or by inhibiting mRNA translation. Context Likelihood of Relatedness (CLR) is genetic network reconstruction method that considers the local network context in assessing the significance of connections while also allowing for detection of non-linear associations. Leveraging TCGA multidimensional data in glioblastoma, we inferred the putative regulatory network between microRNA and mRNA using the CLR algorithm. Interrogation of the network in context of defined molecular subtypes identified 8 microRNAs with a strong discriminatory potential between proneural and mesenchymal subtypes. Integrative in silico analyses, a functional genetic screen, and experimental validation identified miR-34a as a tumor suppressor in proneural subtype glioblastoma. Mechanistically, in addition to its direct regulation of platelet-derived growth factor receptor-alpha (PDGFRA), promoter enrichment analysis of CLR-inferred mRNA nodes established miR-34a as a novel regulator of a SMAD4 transcriptional network. Clinically, miR-34a expression level is shown to be prognostic, where miR-34a low-expressing glioblastomas exhibited better overall survival. This work illustrates the potential of comprehensive multidimensional cancer genomic data combined with computational and experimental models to enable mechanistic exploration of relationships among different genetic elements across the genome space in cancer

    Development of methods for Omics Network inference and analysis and their application to disease modeling

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    With the advent of Next Generation Sequencing (NGS) technologies and the emergence of large publicly available genomics data comes an unprecedented opportunity to model biological networks through a holistic lens using a systems-based approach. Networks provide a mathematical framework for representing biological phenomena that go beyond standard one-gene-at-a-time analyses. Networks can model system-level patterns and the molecular rewiring (i.e. changes in connectivity) occurring in response to perturbations or between distinct phenotypic groups or cell types. This in turn supports the identification of putative mechanisms of actions of the biological processes under study, and thus have the potential to advance prevention and therapy. However, there are major challenges faced by researchers. Inference of biological network structures is often performed on high-dimensional data, yet is hindered by the limited sample size of high throughput omics data. Furthermore, modeling biological networks involves complex analyses capable of integrating multiple sources of omics layers and summarizing large amounts of information. My dissertation aims to address these challenges by presenting new approaches for high-dimensional network inference with limit sample sizes as well as methods and tools for integrated network analysis applied to multiple research domains in cancer genomics. First, I introduce a novel method for reconstructing gene regulatory networks called SHINE (Structure Learning for Hierarchical Networks) and present an evaluation on simulated and real datasets including a Pan-Cancer analysis using The Cancer Genome Atlas (TCGA) data. Next, I summarize the challenges with executing and managing data processing workflows for large omics datasets on high performance computing environments and present multiple strategies for using Nextflow for reproducible scientific workflows including shine-nf - a collection of Nextflow modules for structure learning. Lastly, I introduce the methods, objects, and tools developed for the analysis of biological networks used throughout my dissertation work. Together - these contributions were used in focused analyses of understanding the molecular mechanisms of tumor maintenance and progression in subtype networks of Breast Cancer and Head and Neck Squamous Cell Carcinoma

    Modules, networks and systems medicine for understanding disease and aiding diagnosis

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    Many common diseases, such as asthma, diabetes or obesity, involve altered interactions between thousands of genes. High-throughput techniques (omics) allow identification of such genes and their products, but functional understanding is a formidable challenge. Network-based analyses of omics data have identified modules of disease-associated genes that have been used to obtain both a systems level and a molecular understanding of disease mechanisms. For example, in allergy a module was used to find a novel candidate gene that was validated by functional and clinical studies. Such analyses play important roles in systems medicine. This is an emerging discipline that aims to gain a translational understanding of the complex mechanisms underlying common diseases. In this review, we will explain and provide examples of how network-based analyses of omics data, in combination with functional and clinical studies, are aiding our understanding of disease, as well as helping to prioritize diagnostic markers or therapeutic candidate genes. Such analyses involve significant problems and limitations, which will be discussed. We also highlight the steps needed for clinical implementation

    Molecular Targets of CNS Tumors

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    Molecular Targets of CNS Tumors is a selected review of Central Nervous System (CNS) tumors with particular emphasis on signaling pathway of the most common CNS tumor types. To develop drugs which specifically attack the cancer cells requires an understanding of the distinct characteristics of those cells. Additional detailed information is provided on selected signal pathways in CNS tumors

    Bioinformatics approaches for cancer research

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    Cancer is the consequence of genetic alterations that influence the behavior of affected cells. While the phenotypic effects of cancer like infinite proliferation are common hallmarks of this complex class of diseases, the connections between the genetic alterations and these effects are not always evident. The growth of information generated by experimental high-throughput techniques makes it possible to combine heterogeneous data from different sources to gain new insights into these complex molecular processes. The demand on computational biology to develop tools and methods to facilitate the evaluation of such data has increased accordingly. To this end, we developed new approaches and bioinformatics tools for the analysis of high-throughput data. Additionally, we integrated these new approaches into our comprehensive C++ framework GeneTrail. GeneTrail presents a powerful package that combines information retrieval, statistical evaluation of gene sets, result presentation, and data exchange. To make GeneTrail';s capabilities available to the research community, we implemented a graphical user interface in PHP and set up a webserver that is world-wide accessible. In this thesis, we discuss newly integrated algorithms and extensions of GeneTrail, as well as some comprehensive studies that have been performed with GeneTrail in the context of cancer research. We applied GeneTrail to analyze properties of tumor-associated antigens to elucidate the mechanisms of antigen candidate selection. Furthermore, we performed an extensive analysis of miRNAs and their putative target pathways and networks in cancer. In the field of differential network analysis, we employed a combination of expression values and topological data to identify patterns of deregulated subnetworks and putative key players for the deregulation. Signatures of deregulated subnetworks may help to predict the sensitivity of tumor subtypes to therapeutic agents and, hence, may be used in the future to guide the selection of optimal agents. Furthermore, the identified putative key players may represent oncogenes, tumor suppressor genes, or other genes that contribute to crucial changes of regulatory and signaling processes in cancer cells and may serve as potential targets for an individualized tumor therapy. With these applications, we demonstrate the usefulness of our GeneTrail package and hope that our work will contribute to a better understanding of cancer.Krebs ist eine Folge von tiefgreifenden genetischen VerĂ€nderungen, die das Verhalten der betroffenen Zellen beeinflussen. WĂ€hrend phĂ€notypische Effekte wie unaufhörliches Wachstum augenscheinliche Merkmale dieser komplexen Klasse von Krankheiten sind, sind die ZusammenhĂ€nge zwischen genetischen VerĂ€nderungen und diesen Effekten oftmals weit weniger offensichtlich. Mit der stetigen Zunahme an Daten, die aus Hochdurchsatz-Verfahren stammen, ist es möglich geworden, heterogene Daten aus verschiedenen Quellen zu kombinieren und neue Erkenntnisse ĂŒber diese ZusammenhĂ€nge zu gewinnen. Dementsprechend sind auch die Anforderungen an die Bioinformatik gewachsen, geeignete Applikationen und Verfahren zu entwickeln, um die Auswertung solcher Daten zu vereinfachen. Zu diesem Zweck haben wir neue AnsĂ€tze und bioinformatische Werkzeuge fĂŒr die Analyse von entsprechenden Daten fĂŒr die Krebsforschung entwickelt, welche wir in unser umfangreiches C++ System GeneTrail integriert haben. GeneTrail stellt ein mĂ€chtiges Softwarepaket dar, das Informationsgewinnung, statistische Auswertung von Gen Mengen, visuelle Darstellung der Resultate und Datenaustausch kombiniert. Um GeneTrail';s FĂ€higkeiten der Forschungsgemeinschaft zugĂ€nglich zu machen, haben wir eine graphische Benutzerschnittstelle in PHP implementiert und einen Webserver aufgesetzt, auf den weltweit zugegriffen werden kann. In der vorliegenden Arbeit diskutieren wir neu integrierte Algorithmen und Erweiterungen von GeneTrail, sowie umfangreiche Untersuchungen im Bereich Krebsforschung, die mit GeneTrail durchgefĂŒhrt wurden. Wir haben GeneTrail angewendet, um Eigenschaften von Tumorantigenen zu untersuchen, um aufzuklĂ€ren, welche dieser Eigenschaften zur Selektion dieser Proteine als Antigene beitragen. Des Weiteren haben wir eine umfangreiche Analyse von miRNAs und deren potentiellen Zielpfaden und -netzen in verschiedenen Krebsarten durchgefĂŒhrt. Im Bereich differentieller Netzwerkanalyse kombinierten wir Expressionswerte und topologische Netzwerkdaten, um Muster deregulierter Teilnetzwerke und mögliche SchlĂŒsselgene fĂŒr die Deregulation zu identifizieren. Signaturen deregulierter Teilnetzwerke können helfen die SensitivitĂ€t verschiedener Tumorarten gegenĂŒber Therapeutika vorherzusagen und damit zukĂŒnftig eine optimal angepasste Therapie zu ermöglichen. Außerdem können die identifizierten potentiellen SchlĂŒsselgene Oncogene, Tumorsuppressorgene, oder andere Gene darstellen, die zu wichtigen Änderungen von regulatorischen Prozessen in Krebszellen beitragen, und damit auch als potentielle Ziele fĂŒr eine individuelle Tumortherapie in Frage kommen. Mit diesen Anwendungen untermauern wir den Nutzen von GeneTrail und hoffen, dass unsere Arbeit in Zukunft zu einem besseren VerstĂ€ndnis von Krebs beitrĂ€gt
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