Identifying Network Biomarkers for Each Breast Cancer Subtypes Along with Their Effective Single and Paired Repurposed Drugs Using Network-Based Machine Learning Techniques

Breast cancer is a complex disease that can be classified into at least 10 different molecular subtypes. Appropriate diagnosis of specific subtypes is critical for ensuring the best possible patient treatment and response to therapy. Current computational methods for determining the subtypes are based on identifying differentially expressed genes (i.e., biomarkers) that can best discriminate the subtypes. Such approaches, however, are known to be unreliable since they yield different biomarker sets when applied to data sets from different studies. Gathering knowledge about the functional relationship among genes will identify “network biomarkers” that will enrich the criteria for biomarker selection. Cancer network biomarkers are subnetworks of functionally related genes that “work in concert” to perform functions associated with a tumorigenic. We propose a machine learning framework that can be used to identify network biomarkers and driver genes for each specific breast cancer subtype. Our results show that the resulting network biomarkers can separate onesubtype from the others with very high accuracy. We also propose an integrated approach that can best capture knowledge (and complex relationships) contained within and between drugs, genes and disease data. A network-based machine learning approach is applied thereafter by using the extracted knowledge and relationships in order to identify single and pair of approved or experimental drugs with potential therapeutic effects on different breast cancer subtypes

iSOM-GSN: An Integrative Approach for Transforming Multi-omic Data into Gene Similarity Networks via Self-organizing Maps

Deep learning models are currently applied in diverse domains, including image recognition, text generation, and event prediction. With the advent of new high-throughput sequencing technologies, a multitude of genomic data has been generated and made available. The representation of such data using deep neural networks, or for that matter, application of differential analysis has, however, not been able to match the growth of that data. One of the main challenges in applying convolutional neural networks on gene interaction data is the lack of understanding of the vector space domain to which they belong and also the inherent difficulties involved in representing those interactions on a significantly lower dimension viz Euclidean spaces. These challenges become more prevalent when dealing with various types of omics data with different forms. In this regard, we introduce a systematic, and generalized method, called iSOM-GSN, used to transform multi-omic genomic data with higher-dimensions into a two-dimensional grid. Afterwards, we apply a convolutional neural network (CNN) to predict disease states of various types. Based on the idea of the Kohonen\u27s self-organizing map (SOM), we generate a two-dimensional grid for each sample for a given set of genes that represent a gene similarity network (GSN). The set of genes that are significantly highly mutated across the whole genome, are related to each other based on functional interactions. We then test the model to predict breast and prostate cancer stages using gene expression, DNA methylation, and copy number alteration, yielding accuracies in the 94-98% range for tumor stages of breast cancer and calculated Gleason scores of prostate cancer with just 14 input genes for both cases. To our knowledge, this is the first attempt to use self-organizing maps and convolutional neural networks on integrating high-dimensional multi-omics data. The scheme not only outputs nearly perfect classification accuracy, but also provides an enhanced scheme for visualization, dimensionality reduction, and interpretation of the results

Precision medicine and future of cancer treatment

Over the last few decades, there has been a deluge in the production of large-scale biological data mainly due to the advances in high-throughput technology. This initiated a paradigm shift on the focus in medical research. Ability to investigate molecular changes over the whole genome provided a unique opportunity in the field of translational research. This also gave rise to the concept of precision medicine which provided a strong hope for the development of better diagnostic and therapeutic tools. This is especially relevant to cancer as its incidence is increasing throughout the world. The purpose of this article is to review tools and applications of precision medicine in cancer

Leveraging Multilayered “Omics” Data for Atopic Dermatitis: A Road Map to Precision Medicine

Atopic dermatitis (AD) is a complex multifactorial inflammatory skin disease that affects ~280 million people worldwide. About 85% of AD cases begin in childhood, a significant portion of which can persist into adulthood. Moreover, a typical progression of children with AD to food allergy, asthma or allergic rhinitis has been reported (“allergic march” or “atopic march”). AD comprises highly heterogeneous sub-phenotypes/endotypes resulting from complex interplay between intrinsic and extrinsic factors, such as environmental stimuli, and genetic factors regulating cutaneous functions (impaired barrier function, epidermal lipid, and protease abnormalities), immune functions and the microbiome. Though the roles of high-throughput “omics” integrations in defining endotypes are recognized, current analyses are primarily based on individual omics data and using binary clinical outcomes. Although individual omics analysis, such as genome-wide association studies (GWAS), can effectively map variants correlated with AD, the majority of the heritability and the functional relevance of discovered variants are not explained or known by the identified variants. The limited success of singular approaches underscores the need for holistic and integrated approaches to investigate complex phenotypes using trans-omics data integration strategies. Integrating omics layers (e.g., genome, epigenome, transcriptome, proteome, metabolome, lipidome, exposome, microbiome), which often have complementary and synergistic effects, might provide the opportunity to capture the flow of information underlying AD disease manifestation. Overlapping genes/candidates derived from multiple omics types include FLG, SPINK5, S100A8, and SERPINB3 in AD pathogenesis. Overlapping pathways include macrophage, endothelial cell and fibroblast activation pathways, in addition to well-known Th1/Th2 and NFkB activation pathways. Interestingly, there was more multi-omics overlap at the pathway level than gene level. Further analysis of multi-omics overlap at the tissue level showed that among 30 tissue types from the GTEx database, skin and esophagus were significantly enriched, indicating the biological interconnection between AD and food allergy. The present work explores multi-omics integration and provides new biological insights to better define the biological basis of AD etiology and confirm previously reported AD genes/pathways. In this context, we also discuss opportunities and challenges introduced by “big omics data” and their integration

Network-based Computational Drug Repurposing and Repositioning for Breast Cancer Disease

Pharmaceutical drug development is a complex, time-consuming and expensive process which is also limited to a relatively small number of targets. Drug repositioning is a vital function which involves finding new uses and indications for already approved and existing drugs. It is a cost-effective process in contrast to experimental drug discovery. Previous studies have shown that the network-based method is a versatile platform for drug repositioning as there exists more biological networks which can be used to model interaction between the biological concepts. In this thesis, we are interested in finding the best drugs for one of the most prevailing disease, the Breast Cancer using the existing Protein-protein interaction (PPI) networks. The proposed method is based on the idea that if a perturbation gene expression profile inversely corelates with the disease gene expression profile, the drug may have a curing effect on the disease. Six samples of stroma surrounding invasive breast primary tumours and six matched samples of normal stroma are extracted from the public functional genomics data repository, Gene Expression Omnibus. The perturbation gene expression data corresponding to MCF7 cell line was extracted from the National Institute of Health’s (NIH) Library of Integrated Network-Based Cellular Signatures (LINCS) dataset. Machine Learning techniques are used to select the best suited drug for the breast cancer disease. We have used a ranking algorithm to obtain a ranked list of suitable drug repurposing and repositioning candidates

A Systems Biology Approach to Epigenetic Gene Regulation

The ability to control when, and how much of the genetic code is being expressed is the underlying principle behind gene regulation. Control of gene production is able to influence a cell's phenotype by determining which structural components of the cell's observable traits (shape, growth, and behavior) are made. In multicellular organism’s different cell types are able to arise from the same genetic code due to a difference in the patterns of genes being expressed. Essentially anywhere in the process of gene expression from transcription, RNA processing, translation, and post-translational modifications of the protein is subject to regulation. As transcription is the first step in the process of gene expression, it is the first level of regulation for influencing the cell phenotype. The actions of transcription factors, histone modifiers, and other proteins work together to influence RNA polymerase's ability to complete the process of transcription. The actions of transcription factors are able to influence transcription by controlling the ability of RNA polymerase to be recruited to the start of a protein coding region and histone modifiers can rearrange the histones of the chromatin causing entire regions of a chromosome to become exposed or sequestered. These transcriptional regulators are able to work in a combinatorial fashion with one another to either activate and/or repress wide repertoires of transcriptional targets. Mapping out a network of interactions between these transcriptional regulators in gene expression programs allows researchers to understand how each protein is able to influence the phenotype of the cell, and how mutations to any of these transcriptional regulators are able to drive the cell into a diseased state. In the case of cancer, changes in the mechanisms of gene regulation brought on by mutations to these transcriptional regulators may drive the cell's hyper proliferative state. With the creation of next generation sequencing researchers are now better able to define where regulation is taking place in the genome, and how much it is able to influence gene expression. This gives researchers the ability to build these gene regulatory networks and evaluate their impact on gene expression. The subsequent chapters of this dissertation are a reflection of my published work investigating the contribution of oncogenic processes to gene regulatory networks in cancer through the study of hyperactivating somatic mutation of a histone modifier, changes in transcription factor response element specificity, epigenetic regulation of transcription factor signaling, and a transcription factor coactivation network

Role of network topology based methods in discovering novel gene-phenotype associations

The cell is governed by the complex interactions among various types of biomolecules. Coupled with environmental factors, variations in DNA can cause alterations in normal gene function and lead to a disease condition. Often, such disease phenotypes involve coordinated dysregulation of multiple genes that implicate inter-connected pathways. Towards a better understanding and characterization of mechanisms underlying human diseases, here, I present GUILD, a network-based disease-gene prioritization framework. GUILD associates genes with diseases using the global topology of the protein-protein interaction network and an initial set of genes known to be implicated in the disease. Furthermore, I investigate the mechanistic relationships between disease-genes and explain the robustness emerging from these relationships. I also introduce GUILDify, an online and user-friendly tool which prioritizes genes for their association to any user-provided phenotype. Finally, I describe current state-of-the-art systems-biology approaches where network modeling has helped extending our view on diseases such as cancer.La cèl•lula es regeix per interaccions complexes entre diferents tipus de biomolècules. Juntament amb factors ambientals, variacions en el DNA poden causar alteracions en la funció normal dels gens i provocar malalties. Sovint, aquests fenotips de malaltia involucren una desregulació coordinada de múltiples gens implicats en vies interconnectades. Per tal de comprendre i caracteritzar millor els mecanismes subjacents en malalties humanes, en aquesta tesis presento el programa GUILD, una plataforma que prioritza gens relacionats amb una malaltia en concret fent us de la topologia de xarxe. A partir d’un conjunt conegut de gens implicats en una malaltia, GUILD associa altres gens amb la malaltia mitjancant la topologia global de la xarxa d’interaccions de proteïnes. A més a més, analitzo les relacions mecanístiques entre gens associats a malalties i explico la robustesa es desprèn d’aquesta anàlisi. També presento GUILDify, un servidor web de fácil ús per la priorització de gens i la seva associació a un determinat fenotip. Finalment, descric els mètodes més recents en què el model•latge de xarxes ha ajudat extendre el coneixement sobre malalties complexes, com per exemple a càncer

New Prognostic and Predictive Markers in Cancer Progression

Biomarkers are of critical medical importance for oncologists, allowing them to predict and detect disease and to determine the best course of action for cancer patient care. Prognostic markers are used to evaluate a patient’s outcome and cancer recurrence probability after initial interventions such as surgery or drug treatments and, hence, to select follow-up and further treatment strategies. On the other hand, predictive markers are increasingly being used to evaluate the probability of benefit from clinical intervention(s), driving personalized medicine. Evolving technologies and the increasing availability of “multiomics” data are leading to the selection of numerous potential biomarkers, based on DNA, RNA, miRNA, protein, and metabolic alterations within cancer cells or tumor microenvironment, that may be combined with clinical and pathological data to greatly improve the prediction of both cancer progression and therapeutic treatment responses. However, in recent years, few biomarkers have progressed from discovery to become validated tools to be used in clinical practice. This Special Issue comprises eight review articles and five original studies on novel potential prognostic and predictive markers for different cancer types

Annual Report 2018

Memoria Científica del año 2018