Quantitative Approaches to the Genomics of Clonal Evolution

Many problems in the biological sciences reduce to questions of genetic evolution. Entire classes of medical pathology, such as malignant neoplasia or infectious disease, can be viewed in the light of Darwinian competition of genomes. With the benefit of today's maturing sequencing technologies we can observe and quantify genetic evolution with nucleotide resolution. This provides a molecular view of genetic material that has adapted, or is in the process of adapting, to its local selection pressures. A series of problems will be discussed in this thesis, all involving the mathematical modeling of genomic data derived from clonally evolving populations. We use a variety of computational approaches to characterize over-represented features in the data, with the underlying hypothesis that we may be detecting fitness-conferring features of the biology. In Part I we consider the cross-sectional sampling of human tumors via RNA-sequencing, and devise computational pipelines for detecting oncogenic gene fusions and oncovirus infections. Genomic translocation and oncovirus infection can each be a highly penetrant alteration in a tumor's evolutionary history, with famous examples of both populating the cancer biology literature. In order to exert a transforming influence over the host cell, gene fusions and viral genetic programs need to be expressed and thus can be detected via whole transcriptome sequencing of a malignant cell population. We describe our approaches to predicting oncogenic gene fusions (Chapter 2) and quantifying host-viral interactions (Chapter 3) in large panels of human tumor tissue. The alterations that we characterize prompt the larger question of how the genetics of tumors and viruses might vary in time, leading us to the study of serially sampled populations. In Part II we consider longitudinal sampling of a clonally evolving population. Phylogenetic trees are the standard representation of a clonal process, an evolutionary picture as old as Darwin's voyages on the Beagle. Chapter 4 first reviews phylogenetic inference and then introduces a certain phylogenetic tree space that forms the starting point of our work on the topic. Specifically, Chapter 4 describes the construction of our projective tree space along with an explicit implementation for visualizing point clouds of rescaled trees. The Chapter finishes by defining a method for stable dimensionality reduction of large phylogenies, which is useful for analyzing long genomic time series. In Chapter 5 we consider medically relevant instances of clonal evolution and the longitudinal genetic data sets to which they give rise. We analyze data from (i) the sequencing of cancers along their therapeutic course, (ii) the passaging of a xenografted tumor through a mouse model, and (iii) the seasonal surveillance of H3N2 influenza's hemagglutinin segment. A novel approach to predicting influenza vaccine effectiveness is demonstrated using statistics of point clouds in tree spaces. Our investigations into clonal processes may be extended beyond naturally occurring genomes. In Part III we focus on the directed clonal evolution of populations of synthetic RNAs in vitro. Analogous to the selection pressures exerted upon malignant cells or viral particles, these synthetic RNA genomes can be evolved against a desired fitness objective. We investigate fitness objectives related to reprogramming ribosomal translation. Chapter 6 identifies high fitness RNA pseudoknot geometries capable of inducing ribosomal frameshift, while Chapter 7 takes an unbiased approach to evolving sequence and structural elements that promote stop codon readthrough

Tissues as networks of cells : towards generative rules of complex organ development

Network analysis is a well-known and powerful tool in molecular biology. More recently, it has been introduced in developmental biology. Tissues can be readily translated into spatial networks such that cells are represented by nodes and intercellular connections by edges. This discretization of cellular organization enables mathematical approaches rooted in network science to be applied towards the understanding of tissue structure and function. Here, we describe how such tissue abstractions can enable the principles that underpin tissue formation and function to be uncovered. We provide an introduction into biologically relevant network measures, then present an overview of different areas of developmental biology where these approaches have been applied. We then summarize the general developmental rules underpinning tissue topology generation. Finally, we discuss how generative models can help to link the developmental rule back to the tissue topologies. Our collection of results points at general mechanisms as to how local developmental rules can give rise to observed topological properties in multicellular systems

Computational approaches to drug design and treatment optimization in genetic diseases

Tesis Doctoral inédita leída en la Universidad Autónoma de Madrid, Facultad de Ciencias, Departamento de Biología Molecular. Fecha de lectura: 30-06-2017Esta tesis tiene embargado el acceso al texto completo hasta el 30-12-201

Genome-wide Predictive Simulation on the Effect of Perturbation and the Cause of Phenotypic variations with Network Biology Approach

Thanks to modern high-throughput technologies such as microarray-based gene expression profiling, a large amount of molecular profile data have been generated in several disease related contexts. Despite the fact that these data likely contain systems-level information about disease regulation, revealing the underlying dynamics between genes and mechanisms of gene regulation in genome wide way remains a major challenge. Understanding these mechanisms in genome-wide fashion and the resulting dynamical behavior is a key goal of the nascent field of systems biology. One approach to dissect the logic of the cell, is to use reverse engineering algorithms that infer regulatory interactions form molecular profile data. In this context, use of information theoretic approaches has been very successful: for instance, the ARACNe algorithm has been able to successfully infer transcriptional interactions between transcription factors and their target genes; similarly, the MINDy algorithm has identified post-translational modulators of transcription factor activity by multivariate analysis of large gene expression profile datasets. Many methods have been proposed to improve ARACNe both from a computational efficiency perspective and in terms of increasing the accuracy of the predicted interactions. Yet, the main core of ARACNe, i.e., the data processing inequality (DPI), has remained virtually unaffected even though modern information theory has extended the DPI theorem into higher-order interactions. First, we introduce an improvement of ARACNe, hARACNe, which recursively applies a higher-order DPI analysis. We show that the new algorithm successfully detects false positive feed-forward loops involving more than three genes. Second, we extend the MINDy algorithm using co-information as a novel metric, thus replacing the conditional mutual information and significantly improving the algorithm"™s predictions. Largely, two ultimate goals of systems perturbation studies are to reveal how human diseases are connected with the genes, and to find regulatory mechanism that determine disease cell behavior. However, these goals remain daunting: even the most talented researchers still have to rely on laborious genetic screens and very simplified hypotheses about effects of given perturbation have been experimentally validated and roughly analyzed with very limited regulatory sub-network such as pathway. To overcome these limitations, use of gene regulatory network is explored in this thesis research. Specifically, we propose creation of a new algorithm that can accurately predict cell state in genome-wide fashion following perturbation of individual genes, such as from silencing or ectopic expression experiments. Furthermore, experimentally validated methods to predict genome-wide changes in a cellular system following a genetic perturbation (e.g., gene silencing or ectopic expression) are still unavailable, and even though phenotypic variations are experimentally profiled and gene signatures are selected by being statistically tested, finding the exact regulator which systematically causes significant variations of gene signature is still quite challenging. In this research, I introduce and experimentally validate a probabilistic Bayesian method to simulate the propagation of genetic perturbations on integrated gene regulatory networks inferred by the hARACNe and coMINDy algorithms from human B cell data. With the same predictive framework, we also computationally predict the master driver (regulator) that is most likely to have produced the observed variations in gene expression levels; these studies as a systematized pre-screening process before genetic manipulation. I predict in silico the effect of silencing of several genes as well as the cause of phenotypic variations. Performance analysis, tested by Gene Set Enrichment Analysis (GSEA), shows that the new methods are highly predictive, thus providing an initial step toward building predictive probabilistic regulatory models, which may be applicable as pre-screening steps in perturbation studies

Computational Integrative Analysis of Biological Networks in Cancer

Cancer is one of the most lethal diseases. By 2030, deaths caused by cancers are estimated to reach 13 million per year worldwide. Cancer is a collection of related diseases distinguished by uncontrolled cell division that is driven by genomic alterations. Cancer is heterogeneous and shows an extraordinary genomic diversity between patients with transcriptionally and histologically similar cancer subtypes, and even between tumors from the same anatomical position. The heterogeneity poses great challenges in understanding cancer mechanisms and drug resistance; this understanding is critical for precise prognosis and improved treatments. Emergence of high-throughput technologies, such as microarrays and next-generation sequencing, has motivated the investigation of cancer cells on a genome-wide scale. Over the last decade, an unprecedented amount of high-throughput data has been generated. The challenge is to turn such a vast amount of raw data into clinically valuable information to benefit cancer patients. Single omics data have failed to fully uncover mechanisms behind cancer phenotypes. Accordingly, integrative approaches have been introduced to systematically analyze and interpret multi-omics data, among which network-based integrative approaches have achieved substantial advances in basic biological studies and cancer treatments. In this thesis, the development and application of network-based integrative methods are included to address challenges in analyzing cancer samples. Two novel methods are introduced to integrate disparate omics data and biological networks at the single-patient level: PerPAS, which takes pathway topology into account and integrates gene expression and clinical data with pathway information; and DERA, which elevates gene expression analysis to the network level and identifies network-based biomarkers that provide functional interpretation. The performance of both methods was demonstrated using biological experiment data, and the results were validated in independent cohorts. The application part of this thesis focuses on understanding cancer mechanisms and identifying clinical biomarkers in breast cancer and diffuse large B-cell lymphoma using PerPAS, DERA, and an existing method SPIA. Our experimental results provided insights into underlying cancer mechanisms and potential prognostic biomarkers for breast cancer, and identified therapeutic targets for diffuse large B-cell lymphoma. The potential of the therapeutic targets was verified in in vitro experiments.癌症是一种复杂的疾病,也是现今最致命的疾病之一。据推算未来二十年后, 在世界范围内, 每年将有一千三百万人死于癌症。癌症是异质性疾病,表现出极大的基因组多样性。取自不同病人但属于相似亚组的基因组样品呈现出显著的差异性, 甚至取自同一个病人同一个位置的基因组样品也是具有差异性。理解癌症致病机理和发展过程才能更好地提供精确诊断及治疗。 高通量技术的出现激发了系统分析学和计算工具的发展。但是单一平台的数据不足以全面揭示癌症机理, 导致理解癌症机理一直是个极大的挑战。基于网络的整合方法的出现促进了基础生物的研究和病人的诊治。这篇论文包括两个部分: 整合方法的开发与应用。在开发新的整合方法方面, 我们研发了新的整合方法来应对整合数据的挑战并回答癌症研究中的问题。两个新开发的整合方法有: 1) PerPAS, 是一个体化治疗分析工具, 支持单个病人样品的分析, 并且能整合信号通路和基因表达数据。2) DERA, 是一个整合细胞网络和基因表达数据的工具。它能把基因表达数据的分析提升到网络层面并能进行单个样品的分析。这两种新型方法的可用性已经在生物数据应用中得以展示, 并且用独立数据验证了发现的结果。 整合方法的应用部分集中在全面整合分析mRNA, miRNA, 信号通路数据, 并在弥漫大B细胞淋巴瘤中识别出新的治疗靶点。在此方法的应用下, 我们发现了几个调控重要的临床存活的细胞通路的靶点。并且这些靶点的可靠性已经被实验验证

Genomic studies on the impact of host/virus interaction in EBV infection using massively parallel high throughput sequencing

Epstein-Barr virus is one of the most common viral infections in humans and, once acquired, persists within its host throughout their life. EBV therefore represents an ex- tremely successful virus, having evolved complex strategies to evade the host’s innate and adaptive immune response during both initial and persistent stages of infection. While infection is mostly harmless in the majority of cases, EBV has the ability to be oncogenic in some individuals, and is associated with a wide range of malignancies as well as non-cancerous diseases. To generate new and useful insights into the evolution of EBV interactions with its host, a hybridization-based target enrichment methodology was optimised to enable whole genome sequencing of EBV directly from clinical samples. This allowed the gen- eration of whole genome sequences of EBV directly from blood for the first time. This methodology was subsequently applied to a number of distinct EBV sample col- lections and the resulting data used to investigate the intra- and inter-host variation in various clinical settings, such as infectious mononucleosis and immunosuppression with chronic EBV infection. Additionally, the number of available whole genomes from East Asia is expanded by eleven (unique) novel genomes from primary infection from a NPC- non-endemic area. These sequences were used for a comparative analysis between NPC- and non-NPC-derived EBV genomes and a number of sites were determined differenti- ating these two groups. Finally, comparative genomic analyses of world-wide EBV strain diversity were per- formed using genome sequences generated here in conjunction with a large number of publicly available EBV genome sequences. The comprehensive data sets generated, which included measures of diversity, selection, and linkage, were used to identify poten- tial targets of T cell immunity. In addition, the population structure of EBV was analysed to better understand the forces that have shaped the evolution of EBV

Optimization of logical networks for the modelling of cancer signalling pathways

Cancer is one of the main causes of death throughout the world. The survival of patients diagnosed with various cancer types remains low despite the numerous progresses of the last decades. Some of the reasons for this unmet clinical need are the high heterogeneity between patients, the differentiation of cancer cells within a single tumor, the persistence of cancer stem cells, and the high number of possible clinical phenotypes arising from the combination of the genetic and epigenetic insults that confer to cells the functional characteristics enabling them to proliferate, evade the immune system and programmed cell death, and give rise to neoplasms. To identify new therapeutic options, a better understanding of the mechanisms that generate and maintain these functional characteristics is needed. As many of the alterations that characterize cancerous lesions relate to the signaling pathways that ensure the adequacy of cellular behavior in a specific micro-environment and in response to molecular cues, it is likely that increased knowledge about these signaling pathways will result in the identification of new pharmacological targets towards which new drugs can be designed. As such, the modeling of the cellular regulatory networks can play a prominent role in this understanding, as computational modeling allows the integration of large quantities of data and the simulation of large systems. Logical modeling is well adapted to the large-scale modeling of regulatory networks. Different types of logical network modeling have been used successfully to study cancer signaling pathways and investigate specific hypotheses. In this work we propose a Dynamic Bayesian Network framework to contextualize network models of signaling pathways. We implemented FALCON, a Matlab toolbox to formulate the parametrization of a prior-knowledge interaction network given a set of biological measurements under different experimental conditions. The FALCON toolbox allows a systems-level analysis of the model with the aim of identifying the most sensitive nodes and interactions of the inferred regulatory network and point to possible ways to modify its functional properties. The resulting hypotheses can be tested in the form of virtual knock-out experiments. We also propose a series of regularization schemes, materializing biological assumptions, to incorporate relevant research questions in the optimization procedure. These questions include the detection of the active signaling pathways in a specific context, the identification of the most important differences within a group of cell lines, or the time-frame of network rewiring. We used the toolbox and its extensions on a series of toy models and biological examples. We showed that our pipeline is able to identify cell type-specific parameters that are predictive of drug sensitivity, using a regularization scheme based on local parameter densities in the parameter space. We applied FALCON to the analysis of the resistance mechanism in A375 melanoma cells adapted to low doses of a TNFR agonist, and we accurately predict the re-sensitization and successful induction of apoptosis in the adapted cells via the silencing of XIAP and the down-regulation of NFkB. We further point to specific drug combinations that could be applied in the clinics. Overall, we demonstrate that our approach is able to identify the most relevant changes between sensitive and resistant cancer clones

Characterization of the novel role of parkin in Gliomagenesis

Ph.DDOCTOR OF PHILOSOPH