Predictive genomics: A cancer hallmark network framework for predicting tumor clinical phenotypes using genome sequencing data

We discuss a cancer hallmark network framework for modelling genome-sequencing data to predict cancer clonal evolution and associated clinical phenotypes. Strategies of using this framework in conjunction with genome sequencing data in an attempt to predict personalized drug targets, drug resistance, and metastasis for a cancer patient, as well as cancer risks for a healthy individual are discussed. Accurate prediction of cancer clonal evolution and clinical phenotypes will have substantial impact on timely diagnosis, personalized management and prevention of cancer.Comment: 5 figs, related papers, visit lab homepage: http://www.cancer-systemsbiology.org, Seminar in Cancer Biology, 201

AutoSOME: a clustering method for identifying gene expression modules without prior knowledge of cluster number

<p>Abstract</p> <p>Background</p> <p>Clustering the information content of large high-dimensional gene expression datasets has widespread application in "omics" biology. Unfortunately, the underlying structure of these natural datasets is often fuzzy, and the computational identification of data clusters generally requires knowledge about cluster number and geometry.</p> <p>Results</p> <p>We integrated strategies from machine learning, cartography, and graph theory into a new informatics method for automatically clustering self-organizing map ensembles of high-dimensional data. Our new method, called AutoSOME, readily identifies discrete and fuzzy data clusters without prior knowledge of cluster number or structure in diverse datasets including whole genome microarray data. Visualization of AutoSOME output using network diagrams and differential heat maps reveals unexpected variation among well-characterized cancer cell lines. Co-expression analysis of data from human embryonic and induced pluripotent stem cells using AutoSOME identifies >3400 up-regulated genes associated with pluripotency, and indicates that a recently identified protein-protein interaction network characterizing pluripotency was underestimated by a factor of four.</p> <p>Conclusions</p> <p>By effectively extracting important information from high-dimensional microarray data without prior knowledge or the need for data filtration, AutoSOME can yield systems-level insights from whole genome microarray expression studies. Due to its generality, this new method should also have practical utility for a variety of data-intensive applications, including the results of deep sequencing experiments. AutoSOME is available for download at <url>http://jimcooperlab.mcdb.ucsb.edu/autosome</url>.</p

An ectomycorrhizal fungus alters sensitivity to jasmonate, salicylate, gibberellin, and ethylene in host roots.

The phytohormones jasmonate, gibberellin, salicylate, and ethylene regulate an interconnected reprogramming network integrating root development with plant responses against microbes. The establishment of mutualistic ectomycorrhizal symbiosis requires the suppression of plant defense responses against fungi as well as the modification of root architecture and cortical cell wall properties. Here, we investigated the contribution of phytohormones and their crosstalk to the ontogenesis of ectomycorrhizae (ECM) between grey poplar (Populus tremula x alba) roots and the fungus Laccaria bicolor. To obtain the hormonal blueprint of developing ECM, we quantified the concentrations of jasmonates, gibberellins, and salicylate via liquid chromatography-tandem mass spectrometry. Subsequently, we assessed root architecture, mycorrhizal morphology, and gene expression levels (RNA sequencing) in phytohormone-treated poplar lateral roots in the presence or absence of L. bicolor. Salicylic acid accumulated in mid-stage ECM. Exogenous phytohormone treatment affected the fungal colonization rate and/or frequency of Hartig net formation. Colonized lateral roots displayed diminished responsiveness to jasmonate but regulated some genes, implicated in defense and cell wall remodelling, that were specifically differentially expressed after jasmonate treatment. Responses to salicylate, gibberellin, and ethylene were enhanced in ECM. The dynamics of phytohormone accumulation and response suggest that jasmonate, gibberellin, salicylate, and ethylene signalling play multifaceted roles in poplar L. bicolor ectomycorrhizal development

Order and disorder: abnormal 3D chromatin organization in human disease

A precise three-dimensional (3D) organization of chromatin is central to achieve the intricate transcriptional patterns that are required to form complex organisms. Growing evidence supports an important role of 3D chromatin architecture in development and delineates its alterations as prominent causes of disease. In this review, we discuss emerging concepts on the fundamental forces shaping genomes in space and on how their disruption can lead to pathogenic phenotypes. We describe the molecular mechanisms underlying a wide range of diseases, from the systemic effects of coding mutations on 3D architectural factors, to the more tissue-specific phenotypes resulting from genetic and epigenetic modifications at specific loci. Understanding the connection between the 3D organization of the genome and its underlying biological function will allow a better interpretation of human pathogenesis

An organogenesis network-based comparative transcriptome analysis for understanding early human development in vivo and in vitro

<p>Abstract</p> <p>Background</p> <p>Integrated networks hold great promise in a variety of contexts. In a recent study, we have combined expression and interaction data to identify a putative network underlying early human organogenesis that contains two modules, the stemness-relevant module (hStemModule) and the differentiation-relevant module (hDiffModule). However, owing to its hypothetical nature, it remains unclear whether this network allows for comparative transcriptome analysis to advance our understanding of early human development, both <it>in vivo </it>and <it>in vitro</it>.</p> <p>Results</p> <p>Based on this integrated network, we here report comparisons with the context-dependent transcriptome data from a variety of sources. By viewing the network and its two modules as gene sets and conducting gene set enrichment analysis, we demonstrate the network's utility as a quantitative monitor of the stem potential <it>versus </it>the differentiation potential. During early human organogenesis, the hStemModule reflects the generality of a gradual loss of the stem potential. The hDiffModule indicates the stage-specific differentiation potential and is therefore not suitable for depicting an extended developmental window. Processing of cultured cells of different types further revealed that the hStemModule is a general indicator that distinguishes different cell types in terms of their stem potential. In contrast, the hDiffModule cannot distinguish between differentiated cells of different types but is able to predict differences in the differentiation potential of pluripotent cells of different origins. We also observed a significant positive correlation between each of these two modules and early embryoid bodies (EBs), which are used as <it>in vitro </it>differentiation models. Despite this, the network-oriented comparisons showed considerable differences between the developing embryos and the EBs that were cultured <it>in vitro </it>over time to try to mimic <it>in vivo </it>processes.</p> <p>Conclusions</p> <p>We strongly recommend the use of these two modules either when pluripotent cell types of different origins are involved or when the comparisons made are constrained to the in <it>vivo </it>embryos during early human organogenesis (and an equivalent <it>in vitro </it>differentiation models). Network-based comparative transcriptome analysis will contribute to an increase in knowledge about human embryogenesis, particularly when only transcriptome data are currently available. These advances will add an extra dimension to network applications.</p

Resistance is Futile: Physical Science, Systems Biology and Single-Cell Analysis to Understanding the Plastic and Heterogeneous Nature of Melanoma and Their Role in Non-Genetic Drug Resistance

Melanoma is the most deadly form of skin cancer due to its great metastatic potential. Targeted therapy that inhibits the BRAF-V600E driver mutation has shown impressive initial responses in melanoma patients. However, drug resistance, as the universal phenomenon for any cancer therapy, always limits treatment efficacy and compromises outcomes. As the early-step of resistance development, non-genetic mechanisms enable cancer cells to transition into a drug-resistant state in as early as a few days after drug treatment without alteration of the genome. This early mechanism is, to a large extent, due to the heterogeneous and highly plastic nature of tumor cells. Therefore, it imperative to understand the plastic and heterogeneous nature of the melanoma cells in order to identify combination therapies that can overcome resistance. In this thesis, we investigate these two fundamental natures of non-genetic drug resistance using BRAF inhibition of BRAF-mutant melanomas as the model system. These melanoma cells undergo multi-step, reversible drug-induced cell-state transitions from the original sensitive phenotype to a drug-resistant one. We first conducted bulk analysis to characterize the detailed kinetics of the entire transition from drug-sensitive state towards drug-resistant state, revealing expression changes of thousands of genes and extensive chromatin remodeling. A 3-step computational biology approach greatly simplified the complexity and revealed that the whole cell-state transition was controlled by a gene module activated within just the first three days of drug treatment, with the RelA transcription factor driving chromatin remodeling to establish an epigenetic program encoding long-term phenotype changes towards resistance. From there, a detailed mechanism connecting tumor epigenetic plasticity with non-genetic drug resistance was resolved through in-depth molecular biology experiments. The mechanism was validated in clinical patient samples. We further investigated heterogeneity by moving from bulk cellular studies to single-cell analysis. The single-cell view further revealed that two driving forces from both cell-state interconversions and phenotype-specific drug selection control the cell-state transition dynamics. The single-cell studies also pinpointed the signaling network hub, RelA, as the driver molecule of the initiation of the adaptive transition. These two competing driving forces were further quantitatively modeled via a thermodynamic-inspired surprisal analysis and a modified Fokker-Planck-type kinetic model. Finally, using integrated single-cell proteomic and metabolic technology I developed to characterize the early-stage signaling and metabolic changes upon initial drug responses, we further identified two distinct paths connecting drug-sensitive and drug-tolerant states. Melanoma cells exclusively traverse one of the two paths depending on the level of MITF in the drug-naïve cells. The two trajectories are associated with distinct signaling and metabolic susceptibilities and are independently druggable. In total, this thesis combines and synergizes various physical science and systems biology approaches together with several unique single-cell technologies and analysis to obtain a deep and comprehensive understanding of non-genetic drug resistance in cancer. The findings from this thesis provide several novel insights into the rational design of effective combination therapy for overcoming the development of resistance in response to cancer treatments.</p

Developmental biology: an array of new possibilities

Microarrays offer biologists comprehensive and powerful tools to analyze the involvement of genes in developmental processes at an unprecedented scale. Microarrays that employ defined sequences will permit us to elucidate genetic relationships and responses, while those that employ undefined DNA sequences (ESTs, cDNA, or genomic libraries) will help us to discover new genes, relate them to documented gene networks, and examine the way in which genes (and the process that they themselves control) are regulated. With access to broad new avenues of research come strategic and logistical headaches, most of which are embodied in the reams of data that are created over the course of an experiment. The solutions to these problems have provided interesting computational tools, which will allow us to compile huge data sets and to construct a genome-wide view of development. We are on the threshold of a new vista of possibilities where we might consider in comprehensive and yet specific detail, for example, the degree to which diverse organisms utilize similar genetic networks to achieve similar ends. (C) 2002 Elsevier Science Inc. All rights reserved

High-Resolution Cartography of the Transcriptome and Methylome Landscapes of Diffuse Gliomas

Molecular mechanisms of lower-grade (II–III) diffuse gliomas (LGG) are still poorly understood, mainly because of their heterogeneity. They split into astrocytoma- (IDH-A) and oligodendroglioma-like (IDH-O) tumors both carrying mutations(s) at the isocitrate dehydrogenase (IDH) gene and into IDH wild type (IDH-wt) gliomas of glioblastoma resemblance. We generated detailed maps of the transcriptomes and DNA methylomes, revealing that cell functions divided into three major archetypic hallmarks: (i) increased proliferation in IDH-wt and, to a lesser degree, IDH-O; (ii) increased inflammation in IDH-A and IDH-wt; and (iii) the loss of synaptic transmission in all subtypes. Immunogenic properties of IDH-A are diverse, partly resembling signatures observed in grade IV mesenchymal glioblastomas or in grade I pilocytic astrocytomas. We analyzed details of coregulation between gene expression and DNA methylation and of the immunogenic micro-environment presumably driving tumor development and treatment resistance. Our transcriptome and methylome maps support personalized, case-by-case views to decipher the heterogeneity of glioma states in terms of data portraits. Thereby, molecular cartography provides a graphical coordinate system that links gene-level information with glioma subtypes, their phenotypes, and clinical context

Systems biology approach to model the life cycle of Trypanosoma cruzi

Due to recent advances in reprogramming cell phenotypes, many efforts have been dedicated to developing reverse engineering procedures for the identification of gene regulatory networks that emulate dynamical properties associated with the cell fates of a given biological system. In this work, we propose a systems biology approach for the reconstruction of the gene regulatory network underlying the dynamics of the Trypanosoma cruzi's life cycle. By means of an optimisation procedure, we embedded the steady state maintenance, and the known phenotypic transitions between these steady states in response to environmental cues, into the dynamics of a gene network model. In the resulting network architecture we identified a small subnetwork, formed by seven interconnected nodes, that controls the parasite's life cycle. The present approach could be useful for better understanding other single cell organisms with multiple developmental stages.Revisión disponible en http://sedici.unlp.edu.ar/handle/10915/87345Centro Regional de Estudios Genómico