INTEGRATIVE ANALYSIS OF OMICS DATA IN ADULT GLIOMA AND OTHER TCGA CANCERS TO GUIDE PRECISION MEDICINE

Transcriptomic profiling and gene expression signatures have been widely applied as effective approaches for enhancing the molecular classification, diagnosis, prognosis or prediction of therapeutic response towards personalized therapy for cancer patients. Thanks to modern genome-wide profiling technology, scientists are able to build engines leveraging massive genomic variations and integrating with clinical data to identify “at risk” individuals for the sake of prevention, diagnosis and therapeutic interventions. In my graduate work for my Ph.D. thesis, I have investigated genomic sequencing data mining to comprehensively characterise molecular classifications and aberrant genomic events associated with clinical prognosis and treatment response, through applying high-dimensional omics genomic data to promote the understanding of gene signatures and somatic molecular alterations contributing to cancer progression and clinical outcomes. Following this motivation, my dissertation has been focused on the following three topics in translational genomics. 1) Characterization of transcriptomic plasticity and its association with the tumor microenvironment in glioblastoma (GBM). I have integrated transcriptomic, genomic, protein and clinical data to increase the accuracy of GBM classification, and identify the association between the GBM mesenchymal subtype and reduced tumorpurity, accompanied with increased presence of tumor-associated microglia. Then I have tackled the sole source of microglial as intrinsic tumor bulk but not their corresponding neurosphere cells through both transcriptional and protein level analysis using a panel of sphere-forming glioma cultures and their parent GBM samples.FurthermoreI have demonstrated my hypothesis through longitudinal analysis of paired primary and recurrent GBM samples that the phenotypic alterations of GBM subtypes are not due to intrinsic proneural-to-mesenchymal transition in tumor cells, rather it is intertwined with increased level of microglia upon disease recurrence. Collectively I have elucidated the critical role of tumor microenvironment (Microglia and macrophages from central nervous system) contributing to the intra-tumor heterogeneity and accurate classification of GBM patients based on transcriptomic profiling, which will not only significantly impact on clinical perspective but also pave the way for preclinical cancer research. 2) Identification of prognostic gene signatures that stratify adult diffuse glioma patientsharboring1p/19q co-deletions. I have compared multiple statistical methods and derived a gene signature significantly associated with survival by applying a machine learning algorithm. Then I have identified inflammatory response and acetylation activity that associated with malignant progression of 1p/19q co-deleted glioma. In addition, I showed this signature translates to other types of adult diffuse glioma, suggesting its universality in the pathobiology of other subset gliomas. My efforts on integrative data analysis of this highly curated data set usingoptimizedstatistical models will reflect the pending update to WHO classification system oftumorsin the central nervous system (CNS). 3) Comprehensive characterization of somatic fusion transcripts in Pan-Cancers. I have identified a panel of novel fusion transcripts across all of TCGA cancer types through transcriptomic profiling. Then I have predicted fusion proteins with kinase activity and hub function of pathway network based on the annotation of genetically mobile domains and functional domain architectures. I have evaluated a panel of in -frame gene fusions as potential driver mutations based on network fusion centrality hypothesis. I have also characterised the emerging complexity of genetic architecture in fusion transcripts through integrating genomic structure and somatic variants and delineating the distinct genomic patterns of fusion events across different cancer types. Overall my exploration of the pathogenetic impact and clinical relevance of candidate gene fusions have provided fundamental insights into the management of a subset of cancer patients by predicting the oncogenic signalling and specific drug targets encoded by these fusion genes. Taken together, the translational genomic research I have conducted during my Ph.D. study will shed new light on precision medicine and contribute to the cancer research community. The novel classification concept, gene signature and fusion transcripts I have identified will address several hotly debated issues in translational genomics, such as complex interactions between tumor bulks and their adjacent microenvironments, prognostic markers for clinical diagnostics and personalized therapy, distinct patterns of genomic structure alterations and oncogenic events in different cancer types, therefore facilitating our understanding of genomic alterations and moving us towards the development of precision medicine

Identifying Regulators from Multiple Types of Biological Data in Cancer

Cancer genomes accumulate alterations that promote cancer cell proliferation and survival. Structural, genetic and epigenetic alterations that have a selective advantage for tumorigenesis affect key regulatory genes and microRNAs that in turn regulate the expression of many target genes. The goal of this dissertation is to leverage the alteration-rich landscape of cancer genomes to detect key regulatory genes and microRNAs. To this end, we designed a feature selection algorithm to identify DNA methylation signals around a gene that would highly predict its expression. We found that genes whose expression could be predicted by DNA methylation accurately were enriched in Gene Ontology terms related to the regulation of various biological processes. This suggests that genes controlled by DNA methylation are regulatory genes. We also developed two tools that infer relationships between regulatory genes and target genes leveraging structural and epigenetic data. The first tool, ProcessDriver integrates copy number alteration and gene expression datasets to identify copy number cancer driver genes, target genes of these drivers and the disrupted biological processes. Our results showed that driver genes selected by ProcessDriver are enriched in known cancer genes. Using survival analysis, we showed that drivers are linked to new tumor events after initial treatment. The second tool was developed to leverage structural and epigenetic data to infer interactions between regulatory genes and targets on a network-level. Our canonical correlation analysis-based approach utilized the DNA methylation or copy number states of potential regulators and the expression states of potential targets to score regulatory interactions. We then incorporated these regulatory interaction scores as prior knowledge in a dynamic Bayesian framework utilizing time series gene expression data. Our results indicated that the canonical correlation analysis-based scores reflect the true interactions between genes with high accuracy, and the accuracy can be further increased by using the scores as a prior in the dynamic Bayesian framework. Finally, we are developing an algorithm to detect cancer-related microRNAs, associated targets and disrupted biological processes. Our preliminary results suggest that the modules of miRNAs and target genes identified in this approach are enriched in known microRNA-gene interactions

Computational mapping of regulatory domains of human genes

Das menschliche Genom enthält Millionen von regulatorischen Elementen - Enhancern -, die die Genexpression quantitativ regulieren. Trotz des enormen Fortschritts beim Verständnis, wie Enhancer die Genexpression steuern, fehlt es in diesem Bereich immer noch an einem systematischen, integrativen und zugänglichen Ansatz zur Entdeckung und Dokumentation von cis-regulatorischen Beziehungen im gesamten Genom. Wir haben eine neuartige Methode - reg2gene - entwickelt, die Genexpression~Enhancer-Aktivität modelliert und integriert. reg2gene besteht aus drei Hauptschritten: 1) Datenquantifizierung, 2) Datenmodellierung und Signifikanzbewertung und 3) Datenintegration, die in dem R-Paket reg2gene zusammengefasst sind. Als Ergebnis haben wir zwei Sätze von Enhancer-Gen-Assoziationen (EGAs) identifiziert: den flexiblen Satz von ~230K EGAs (flexibleC) und den stringenten Satz von ~60K EGAs (stringentC). Wir haben große Unterschiede zwischen den bisher veröffentlichten Berechnungsmodellen für Enhancer-Gene-Assoziationen festgestellt, vor allem in Bezug auf die Lage, die Anzahl und die Eigenschaften der definierten Enhancer-Regionen und EGAs. Wir führten ein detailliertes Benchmarking von sieben Sets von rechnerisch modellierten EGAs durch, zeigten jedoch, dass keiner der derzeit verfügbaren Benchmark-Datensätze als "goldener Standard" verwendet werden kann. Wir definierten einen zusätzlichen Benchmark-Datensatz mit positiven und negativen EGAs, mit dem wir zeigten, dass das stringentC-Modell den höchsten positiven Vorhersagewert (PPV) hatte. Wir haben das Potenzial von EGAs zur Identifizierung von Genzielen von nicht-kodierenden SNP-Gene-Assoziationen nachgewiesen. Schließlich führten wir eine funktionelle Analyse durch, um neue Genziele, Enhancer-Pleiotropie und Mechanismen der Enhancer-Aktivität zu ermitteln. Insgesamt bringt diese Arbeit unser Verständnis der durch Enhancer vermittelten Regulierung der Genexpression in Gesundheit und Krankheit voran.Human genome contains millions of regulatory elements - enhancers - that quantitatively regulate gene expression. Multiple experimental and computational approaches were developed to associate enhancers with their gene targets. Despite the tremendous progress in understanding how enhancers tune gene expression, the field still lacks an approach that is systematic, integrative and accessible for discovering and documenting cis-regulatory relationships across the genome. We developed a novel computational approach - reg2gene- that models and integrates gene expression ~ enhancer activity. reg2gene consists of three main steps: 1) data quantification, 2) data modelling and significance assessment, and 3) data integration gathered in the reg2gene R package. As a result we identified two sets of enhancer-gene associations (EGAs): the flexible set of ~230K EGAs (flexibleC), and the stringent set of ~60K EGAs (stringentC). We identified major differences across previously published computational models of enhancer-gene associations; mostly in the location, number and properties of defined enhancer regions and EGAs. We performed detailed benchmarking of seven sets of computationally modelled EGAs, but showed that none of the currently available benchmark datasets could be used as a “golden-standard” benchmark dataset. To account for that observation, we defined an additional benchmark set of positive and negative EGAs with which we showed that the stringentC model had the highest positive predictive value (PPV) across all analyzed computational models. We reviewed the influence of EGA sets on the functional analysis of risk SNPs and demonstrated the potential of EGAs to identify gene targets of non-coding SNP-gene associations. Lastly, we performed a functional analysis to detect novel gene targets, enhancer pleiotropy, and mechanisms of enhancer activity. Altogether, this work advances our understanding of enhancer-mediated gene expression regulation in health and disease.Ljudski genom sadrži milijune regulatornih elemenata - enhancera - koji kvantitativno reguliraju ekspresiju gena. Unatoč ogromnom napretku u razumijevanju načina na koji enhanceri reguliraju ekspresiju gena, području još uvijek nedostaje pristup koji je sustavan, integrativan i dostupan za otkrivanje i dokumentiranje cis-regulatornih odnosa u cijelom genomu. Razvili smo novu računalnu metodu - reg2gene - koja modelira i integrira aktivnost enhancera~ekspresije gena. reg2gene sastoji se od tri glavna koraka: 1) kvantifikacija podataka, 2) modeliranje podataka i procjena značaja, i 3) integracija podataka prikupljenih u reg2gene R paketu. Kao rezultat toga, identificirali smo dva skupa enhancer-gen interakcija (EGA): fleksibilni skup od ~ 230K EGA (flexibleC) i strogi skup od ~ 60K EGA (stringentC). Utvrdili smo velike razlike u prethodno objavljenim računalnim modelima enhancer-gen interakcija; uglavnom u lokaciji, broju i svojstvima definiranih enhancera i EGA. Izveli smo detaljno mjerenje performansi sedam skupova računalno modeliranih EGA-a, ali smo pokazali da se niti jedan od trenutno dostupnih skupova referentnih podataka ne može koristiti kao referentni skup podataka "zlatnI standard". Definirali smo dodatni referentni skup pozitivnih i negativnih EGA -a pomoću kojih smo pokazali da stringentC ima najveću pozitivnu prediktivnu vrijednost (PPV). Pokazali smo potencijal EGA-a za identifikaciju genskih meta nekodirajucih SNP-ova. Proveli smo funkcionalnu analizu kako bismo otkrili nove genske mete, pleiotropiju enhancera i mehanizme aktivnosti enhancera. Ovaj rad poboljšava naše razumijevanje regulacije ekspresije gena posredovane enhancerima

Functional effects of variation in transcription factor binding highlight long-range gene regulation by epromoters.

Identifying DNA cis-regulatory modules (CRMs) that control the expression of specific genes is crucial for deciphering the logic of transcriptional control. Natural genetic variation can point to the possible gene regulatory function of specific sequences through their allelic associations with gene expression. However, comprehensive identification of causal regulatory sequences in brute-force association testing without incorporating prior knowledge is challenging due to limited statistical power and effects of linkage disequilibrium. Sequence variants affecting transcription factor (TF) binding at CRMs have a strong potential to influence gene regulatory function, which provides a motivation for prioritizing such variants in association testing. Here, we generate an atlas of CRMs showing predicted allelic variation in TF binding affinity in human lymphoblastoid cell lines and test their association with the expression of their putative target genes inferred from Promoter Capture Hi-C and immediate linear proximity. We reveal >1300 CRM TF-binding variants associated with target gene expression, the majority of them undetected with standard association testing. A large proportion of CRMs showing associations with the expression of genes they contact in 3D localize to the promoter regions of other genes, supporting the notion of 'epromoters': dual-action CRMs with promoter and distal enhancer activity

Heritability enrichment of immunoglobulin G N-glycosylation in specific tissues

Genome-wide association studies (GWAS) have identified over 60 genetic loci associated with immunoglobulin G (IgG) N-glycosylation; however, the causal genes and their abundance in relevant tissues are uncertain. Leveraging data from GWAS summary statistics for 8,090 Europeans, and large-scale expression quantitative trait loci (eQTL) data from the genotype-tissue expression of 53 types of tissues (GTEx v7), we derived a linkage disequilibrium score for the specific expression of genes (LDSC-SEG) and conducted a transcriptome-wide association study (TWAS). We identified 55 gene associations whose predicted levels of expression were significantly associated with IgG N-glycosylation in 14 tissues. Three working scenarios, i.e., tissue-specific, pleiotropic, and coassociated, were observed for candidate genetic predisposition affecting IgG N-glycosylation traits. Furthermore, pathway enrichment showed several IgG N-glycosylation-related pathways, such as asparagine N-linked glycosylation, N-glycan biosynthesis and transport to the Golgi and subsequent modification. Through phenome-wide association studies (PheWAS), most genetic variants underlying TWAS hits were found to be correlated with health measures (height, waist-hip ratio, systolic blood pressure) and diseases, such as systemic lupus erythematosus, inflammatory bowel disease, and Parkinson’s disease, which are related to IgG N-glycosylation. Our study provides an atlas of genetic regulatory loci and their target genes within functionally relevant tissues, for further studies on the mechanisms of IgG N-glycosylation and its related diseases

Mapping and Functional Analysis of cis-Regulatory Elements in Mouse Photoreceptors

Photoreceptors are light-sensitive neurons that mediate vision, and they are the most commonly affected cell type in genetic forms of blindness. In mice, there are two basic types of photoreceptors, rods and cones, which mediate vision in dim and bright environments, respectively. The transcription factors (TFs) that control rod and cone development have been studied in detail, but the cis-regulatory elements (CREs) through which these TFs act are less well understood. To comprehensively identify photoreceptor CREs in mice and to understand their relationship with gene expression, we performed open chromatin (ATAC-seq) and transcriptome (RNA-seq) profiling of FACS-purified rods and cones. We find that rods have significantly fewer regions of open chromatin than cones (as well as \u3e60 additional cell types and tissues), and we demonstrate that this uniquely closed chromatin architecture depends on the rod master regulator Nrl. Finally, we find that regions of rod- and cone-specific open chromatin are enriched for distinct sets of TF binding sites, providing insight into the cis-regulatory grammar of these cell types. We also sought to understand how the regulatory activity of rod and cone open chromatin regions is encoded in DNA sequence. Cone-rod homeobox (CRX) is a paired-like homeodomain TF and master regulator of both rod and cone development, and CRX binding sites are by far the most enriched TF binding sites in photoreceptor CREs. The in vitro DNA binding preferences of CRX have been extensively characterized, but how well in vitro models of TF binding site affinity predict in vivo regulatory activity is not known. In addition, paired-class homeodomain TFs bind DNA as both monomers and dimers, but whether monomeric and dimeric CRX binding sites have distinct regulatory activities is not known. To address these questions, we used a massively parallel reporter assay to quantify the activity of thousands native and mutant CRX binding sites in explanted mouse retinas. These data reveal that dimeric CRX binding sites encode stronger enhancers than monomeric CRX binding sites. Moreover, the activity of half-sites within dimeric CRX binding sites is cooperative and spacing-dependent. In addition, saturating mutagenesis of 195 CRX binding sites reveals that, while TF binding site affinity and activity are moderately correlated across mutations within individual CREs, they are poorly correlated across mutations from distinct CREs. Accordingly, we show that accounting for baseline CRE activity improves the prediction of the effects of mutations in regulatory DNA from sequence-based models. Taken together, these data demonstrate that the activity of CRX binding sites depends on multiple layers of sequence context, providing insight into photoreceptor gene regulation and illustrating functional principles of homeodomain TF binding sites

Strategies for increasing the applicability of biological network inference

The manipulation of cellular state has many promising applications, including stem cell biology and regenerative medicine, biofuel production, and stress resistant crop development. The construction of interaction maps promises to enhance our ability to engineer cellular behavior. Within the last 15 years, many methods have been developed to infer the structure of the gene regulatory interaction map from gene abundance snapshots provided by high-throughput experimental data. However, relatively little research has focused on using gene regulatory network models for the prediction and manipulation of cellular behavior. This dissertation examines and applies strategies to utilize the predictive power of gene network models to guide experimentation and engineering efforts. First, we developed methods to improve gene network models by integrating interaction evidence sources, in order to utilize the full predictive power of the models. Next, we explored the power of networks models to guide experimental efforts through inference and analysis of a regulatory network in the pathogenic fungus Cryptococcus neoformans. Finally, we develop a novel, network-guided algorithm to select genetic interventions for engineering transcriptional state. We apply this method to select intervention strains for improving biofuel production in a mixed glucose-xylose environment. The contributions in this dissertation provide the first thorough examination, systematic application, and quantitative evaluation of the utilization of network models for guiding cellular engineering

Network reconstruction for trans acting genetic loci using multi-omics data and prior information

BACKGROUND: Molecular measurements of the genome, the transcriptome, and the epigenome, often termed multi-omics data, provide an in-depth view on biological systems and their integration is crucial for gaining insights in complex regulatory processes. These data can be used to explain disease related genetic variants by linking them to intermediate molecular traits (quantitative trait loci, QTL). Molecular networks regulating cellular processes leave footprints in QTL results as so-called trans-QTL hotspots. Reconstructing these networks is a complex endeavor and use of biological prior information can improve network inference. However, previous efforts were limited in the types of priors used or have only been applied to model systems. In this study, we reconstruct the regulatory networks underlying trans-QTL hotspots using human cohort data and data-driven prior information. METHODS: We devised a new strategy to integrate QTL with human population scale multi-omics data. State-of-the art network inference methods including BDgraph and glasso were applied to these data. Comprehensive prior information to guide network inference was manually curated from large-scale biological databases. The inference approach was extensively benchmarked using simulated data and cross-cohort replication analyses. Best performing methods were subsequently applied to real-world human cohort data. RESULTS: Our benchmarks showed that prior-based strategies outperform methods without prior information in simulated data and show better replication across datasets. Application of our approach to human cohort data highlighted two novel regulatory networks related to schizophrenia and lean body mass for which we generated novel functional hypotheses. CONCLUSIONS: We demonstrate that existing biological knowledge can improve the integrative analysis of networks underlying trans associations and generate novel hypotheses about regulatory mechanisms

Learning a Prior on Regulatory Potential from eQTL Data

Genome-wide RNA expression data provide a detailed view of an organism's biological state; hence, a dataset measuring expression variation between genetically diverse individuals (eQTL data) may provide important insights into the genetics of complex traits. However, with data from a relatively small number of individuals, it is difficult to distinguish true causal polymorphisms from the large number of possibilities. The problem is particularly challenging in populations with significant linkage disequilibrium, where traits are often linked to large chromosomal regions containing many genes. Here, we present a novel method, Lirnet, that automatically learns a regulatory potential for each sequence polymorphism, estimating how likely it is to have a significant effect on gene expression. This regulatory potential is defined in terms of “regulatory features”—including the function of the gene and the conservation, type, and position of genetic polymorphisms—that are available for any organism. The extent to which the different features influence the regulatory potential is learned automatically, making Lirnet readily applicable to different datasets, organisms, and feature sets. We apply Lirnet both to the human HapMap eQTL dataset and to a yeast eQTL dataset and provide statistical and biological results demonstrating that Lirnet produces significantly better regulatory programs than other recent approaches. We demonstrate in the yeast data that Lirnet can correctly suggest a specific causal sequence variation within a large, linked chromosomal region. In one example, Lirnet uncovered a novel, experimentally validated connection between Puf3—a sequence-specific RNA binding protein—and P-bodies—cytoplasmic structures that regulate translation and RNA stability—as well as the particular causative polymorphism, a SNP in Mkt1, that induces the variation in the pathway