A network-based target overlap score for characterizing drug combinations: High correlation with cancer clinical trial results

Drug combinations are highly efficient in systemic treatment of complex multigene diseases such as cancer, diabetes, arthritis and hypertension. Most currently used combinations were found in empirical ways, which limits the speed of discovery for new and more effective combinations. Therefore, there is a substantial need for efficient and fast computational methods. Here, we present a principle that is based on the assumption that perturbations generated by multiple pharmaceutical agents propagate through an interaction network and can cause unexpected amplification at targets not immediately affected by the original drugs. In order to capture this phenomenon, we introduce a novel Target Overlap Score (TOS) that is defined for two pharmaceutical agents as the number of jointly perturbed targets divided by the number of all targets potentially affected by the two agents. We show that this measure is correlated with the known effects of beneficial and deleterious drug combinations taken from the DCDB, TTD and Drugs.com databases. We demonstrate the utility of TOS by correlating the score to the outcome of recent clinical trials evaluating trastuzumab, an effective anticancer agent utilized in combination with anthracycline- and taxane-based systemic chemotherapy in HER2-receptor (erb-b2 receptor tyrosine kinase 2) positive breast cancer. © 2015 Ligeti et al

Interpreting mechanism of Synergism of drug combinations using attention based hierarchical graph pooling

The synergistic drug combinations provide huge potentials to enhance therapeutic efficacy and to reduce adverse reactions. However, effective and synergistic drug combination prediction remains an open question because of the unknown causal disease signaling pathways. Though various deep learning (AI) models have been proposed to quantitatively predict the synergism of drug combinations. The major limitation of existing deep learning methods is that they are inherently not interpretable, which makes the conclusion of AI models un-transparent to human experts, henceforth limiting the robustness of the model conclusion and the implementation ability of these models in the real-world human-AI healthcare. In this paper, we develop an interpretable graph neural network (GNN) that reveals the underlying essential therapeutic targets and mechanism of the synergy (MoS) by mining the sub-molecular network of great importance. The key point of the interpretable GNN prediction model is a novel graph pooling layer, Self-Attention based Node and Edge pool (henceforth SANEpool), that can compute the attention score (importance) of nodes and edges based on the node features and the graph topology. As such, the proposed GNN model provides a systematic way to predict and interpret the drug combination synergism based on the detected crucial sub-molecular network. We evaluate SANEpool on molecular networks formulated by genes from 46 core cancer signaling pathways and drug combinations from NCI ALMANAC drug combination screening data. The experimental results indicate that 1) SANEpool can achieve the current state-of-art performance among other popular graph neural networks; and 2) the sub-molecular network detected by SANEpool are self-explainable and salient for identifying synergistic drug combinations

Integration of Genome Scale Metabolic Networks and gene regulation of metabolic enzymes with Physiologically Based Pharmacokinetics

The scope of Physiologically Based Pharmacokinetic (PBPK) modelling can be expanded by assimilation of the mechanistic models of intracellular processes from Systems Biology field. Genome Scale Metabolic Networks (GSMNs) represent a whole set of metabolic enzymes expressed in human tissues. Dynamic models of the gene regulation of key drug metabolism enzymes are available. Here, we introduce GSMNs and review ongoing work on integration of PBPK, GSMNs and metabolic gene regulation. We demonstrate example models

Network-based analysis of gene expression data

The methods of molecular biology for the quantitative measurement of gene expression have undergone a rapid development in the past two decades. High-throughput assays with the microarray and RNA-seq technology now enable whole-genome studies in which several thousands of genes can be measured at a time. However, this has also imposed serious challenges on data storage and analysis, which are subject of the young, but rapidly developing field of computational biology. To explain observations made on such a large scale requires suitable and accordingly scaled models of gene regulation. Detailed models, as available for single genes, need to be extended and assembled in larger networks of regulatory interactions between genes and gene products. Incorporation of such networks into methods for data analysis is crucial to identify molecular mechanisms that are drivers of the observed expression. As methods for this purpose emerge in parallel to each other and without knowing the standard of truth, results need to be critically checked in a competitive setup and in the context of the available rich literature corpus. This work is centered on and contributes to the following subjects, each of which represents important and distinct research topics in the field of computational biology: (i) construction of realistic gene regulatory network models; (ii) detection of subnetworks that are significantly altered in the data under investigation; and (iii) systematic biological interpretation of detected subnetworks. For the construction of regulatory networks, I review existing methods with a focus on curation and inference approaches. I first describe how literature curation can be used to construct a regulatory network for a specific process, using the well-studied diauxic shift in yeast as an example. In particular, I address the question how a detailed understanding, as available for the regulation of single genes, can be scaled-up to the level of larger systems. I subsequently inspect methods for large-scale network inference showing that they are significantly skewed towards master regulators. A recalibration strategy is introduced and applied, yielding an improved genome-wide regulatory network for yeast. To detect significantly altered subnetworks, I introduce GGEA as a method for network-based enrichment analysis. The key idea is to score regulatory interactions within functional gene sets for consistency with the observed expression. Compared to other recently published methods, GGEA yields results that consistently and coherently align expression changes with known regulation types and that are thus easier to explain. I also suggest and discuss several significant enhancements to the original method that are improving its applicability, outcome and runtime. For the systematic detection and interpretation of subnetworks, I have developed the EnrichmentBrowser software package. It implements several state-of-the-art methods besides GGEA, and allows to combine and explore results across methods. As part of the Bioconductor repository, the package provides a unified access to the different methods and, thus, greatly simplifies the usage for biologists. Extensions to this framework, that support automating of biological interpretation routines, are also presented. In conclusion, this work contributes substantially to the research field of network-based analysis of gene expression data with respect to regulatory network construction, subnetwork detection, and their biological interpretation. This also includes recent developments as well as areas of ongoing research, which are discussed in the context of current and future questions arising from the new generation of genomic data

Microfluidics-Based Single-Cell Functional Proteomics Microchip for Portraying Protein Signal Transduction Networks within the Framework of Physicochemical Principles, with Applications in Fundamental and Translational Cancer Research

Single-cell functional proteomics assays can connect genomic information to biological function through quantitative and multiplex protein measurements. Tools for single-cell proteomics have developed rapidly over the past 5 years and are providing unique opportunities. This thesis describes an emerging microfluidics-based toolkit for single cell functional proteomics, focusing on the development of the single cell barcode chips (SCBCs) with applications in fundamental and translational cancer research. The microchip designed to simultaneously quantify a panel of secreted, cytoplasmic and membrane proteins from single cells will be discussed at the beginning, which is the prototype for subsequent proteomic microchips with more sophisticated design in preclinical cancer research or clinical applications. The SCBCs are a highly versatile and information rich tool for single-cell functional proteomics. They are based upon isolating individual cells, or defined number of cells, within microchambers, each of which is equipped with a large antibody microarray (the barcode), with between a few hundred to ten thousand microchambers included within a single microchip. Functional proteomics assays at single-cell resolution yield unique pieces of information that significantly shape the way of thinking on cancer research. An in-depth discussion about analysis and interpretation of the unique information such as functional protein fluctuations and protein-protein correlative interactions will follow. The SCBC is a powerful tool to resolve the functional heterogeneity of cancer cells. It has the capacity to extract a comprehensive picture of the signal transduction network from single tumor cells and thus provides insight into the effect of targeted therapies on protein signaling networks. We will demonstrate this point through applying the SCBCs to investigate three isogenic cell lines of glioblastoma multiforme (GBM). The cancer cell population is highly heterogeneous with high-amplitude fluctuation at the single cell level, which in turn grants the robustness of the entire population. The concept that a stable population existing in the presence of random fluctuations is reminiscent of many physical systems that are successfully understood using statistical physics. Thus, tools derived from that field can probably be applied to using fluctuations to determine the nature of signaling networks. In the second part of the thesis, we will focus on such a case to use thermodynamics-motivated principles to understand cancer cell hypoxia, where single cell proteomics assays coupled with a quantitative version of Le Chatelier's principle derived from statistical mechanics yield detailed and surprising predictions, which were found to be correct in both cell line and primary tumor model. The third part of the thesis demonstrates the application of this technology in the preclinical cancer research to study the GBM cancer cell resistance to molecular targeted therapy. Physical approaches to anticipate therapy resistance and to identify effective therapy combinations will be discussed in detail. Our approach is based upon elucidating the signaling coordination within the phosphoprotein signaling pathways that are hyperactivated in human GBMs, and interrogating how that coordination responds to the perturbation of targeted inhibitor. Strongly coupled protein-protein interactions constitute most signaling cascades. A physical analogy of such a system is the strongly coupled atom-atom interactions in a crystal lattice. Similar to decomposing the atomic interactions into a series of independent normal vibrational modes, a simplified picture of signaling network coordination can also be achieved by diagonalizing protein-protein correlation or covariance matrices to decompose the pairwise correlative interactions into a set of distinct linear combinations of signaling proteins (i.e. independent signaling modes). By doing so, two independent signaling modes – one associated with mTOR signaling and a second associated with ERK/Src signaling have been resolved, which in turn allow us to anticipate resistance, and to design combination therapies that are effective, as well as identify those therapies and therapy combinations that will be ineffective. We validated our predictions in mouse tumor models and all predictions were borne out. In the last part, some preliminary results about the clinical translation of single-cell proteomics chips will be presented. The successful demonstration of our work on human-derived xenografts provides the rationale to extend our current work into the clinic. It will enable us to interrogate GBM tumor samples in a way that could potentially yield a straightforward, rapid interpretation so that we can give therapeutic guidance to the attending physicians within a clinical relevant time scale. The technical challenges of the clinical translation will be presented and our solutions to address the challenges will be discussed as well. A clinical case study will then follow, where some preliminary data collected from a pediatric GBM patient bearing an EGFR amplified tumor will be presented to demonstrate the general protocol and the workflow of the proposed clinical studies.</p

The impact of genetic interactions in antibiotic resistance

Tese de doutoramento, Biologia (Genética), Universidade de Lisboa, Faculdade de Ciências, 2012Genetic interactions, both between genetic material and between this and the surrounding environment of a given individual are important factors for understanding the process of evolution of natural populations. The study of this interactions as well as their integration in evolutionary terms may have several applications such as, for example, understanding the observed prevalence of antibiotic resistance in natural populations. In this thesis the patterns of genetic interactions between multiple-resistances to antibiotics were explored. In particular, epistasis and genotype-by-environment interactions operating among antibiotic resistances were studied. To measure levels of epistasis occurring between multiple-antibiotic-resistances in the complete absence of antibiotics, mutants resistant to three antibiotics commonly used in clinic were first generated: nalidixic acid, rifampicin and streptomycin. With these mutants double resistant mutants were created. By measuring the costs of each mutation individually and jointly the type of epistasis operating between different sets of resistance mutations was determined. The cost of double resistance was majorly lower than expected, revealing the presence of positive epistasis between mutations conferring resistance to the studied antibiotics. The same patterns were observed for interactions between a set of resistance mutations and conjugative plasmids with multiple-resistance factors. However, in this case, extreme cases of positive epistasis coined sign epistasis were observed for a large fraction of combinations. This type of interactions was also observed in the previous study yet less frequently. Naturally occurring bacteria are often faced with a multitude of environments. The action of environmental changes in the effects of antibiotic resistance mutations was studied for a group of mutations resistant to four antibiotics in three environments differing in the CHAPTER I viii number of comprised environmental stresses. This study revealed, for most cases, a strong pattern of interactions between the different genotypes and the environment.As interacções genéticas, quer entre material genético quer entre este e o ambiente que rodeia um determinado organismo, são factores importantes para entender o processo de evolução de populações naturais. O estudo destas interacções bem como a sua integração em termos evolutivos pode ter diversas aplicações tais como, por exemplo, a compreensão da prevalência da resistência a antibióticos observada em populações naturais. Nesta tese exploraram-se os padrões de interacções genéticas entre múltiplas resistências a antibióticos nomeadamente, epistasia e as interacções genótipo-ambiente que ocorrem em resistências a antibióticos. Para medir os níveis de epistasia entre múltiplas resistências a antibióticos na ausência dos mesmos, foram construídos mutantes resistentes a três antibióticos vulgarmente usados em clínica: ácido nalidíxico, rifampicina e estreptomicina. Com estes mutantes foram gerados duplos mutantes. Medindo os custos de cada uma das mutações individualmente e em conjunto determinou-se o tipo de epistasia a operar entre diferentes conjuntos de mutações de resistência. O custo da dupla resistência foi maioritariamente menor do que o esperado, revelando a presença de epistasia positiva entre mutações que conferem resistência aos antibióticos estudados. Os mesmos padrões foram observados para interacções entre um conjunto de mutações que conferem resistência e plasmídeos conjugativos com múltiplos factores de resistência. No entanto, neste caso, foram observados numa larga fracção de combinações casos extremos de epistasia positiva denominados epistasia de sinal, situação também observada anteriormente mas em menor frequência. Na natureza, as bactérias enfrentam frequentemente uma multiplicidade de ambientes. A acção das alterações ambientais nos efeitos das mutações que conferem resistência a antibióticos foi CHAPTER I x estudada num grupo de mutações resistentes a quatro antibióticos em três ambientes que diferem no número de factores ambientais prejudiciais comportados por cada um. Este estudo revelou, para a maioria dos casos, um forte padrão de interacções entre os vários genótipos e o ambiente.Fundação para a Ciência e a Tecnologia (FCT, SFRH/BD/40162/2007

Network-based analysis of gene expression data

The methods of molecular biology for the quantitative measurement of gene expression have undergone a rapid development in the past two decades. High-throughput assays with the microarray and RNA-seq technology now enable whole-genome studies in which several thousands of genes can be measured at a time. However, this has also imposed serious challenges on data storage and analysis, which are subject of the young, but rapidly developing field of computational biology. To explain observations made on such a large scale requires suitable and accordingly scaled models of gene regulation. Detailed models, as available for single genes, need to be extended and assembled in larger networks of regulatory interactions between genes and gene products. Incorporation of such networks into methods for data analysis is crucial to identify molecular mechanisms that are drivers of the observed expression. As methods for this purpose emerge in parallel to each other and without knowing the standard of truth, results need to be critically checked in a competitive setup and in the context of the available rich literature corpus. This work is centered on and contributes to the following subjects, each of which represents important and distinct research topics in the field of computational biology: (i) construction of realistic gene regulatory network models; (ii) detection of subnetworks that are significantly altered in the data under investigation; and (iii) systematic biological interpretation of detected subnetworks. For the construction of regulatory networks, I review existing methods with a focus on curation and inference approaches. I first describe how literature curation can be used to construct a regulatory network for a specific process, using the well-studied diauxic shift in yeast as an example. In particular, I address the question how a detailed understanding, as available for the regulation of single genes, can be scaled-up to the level of larger systems. I subsequently inspect methods for large-scale network inference showing that they are significantly skewed towards master regulators. A recalibration strategy is introduced and applied, yielding an improved genome-wide regulatory network for yeast. To detect significantly altered subnetworks, I introduce GGEA as a method for network-based enrichment analysis. The key idea is to score regulatory interactions within functional gene sets for consistency with the observed expression. Compared to other recently published methods, GGEA yields results that consistently and coherently align expression changes with known regulation types and that are thus easier to explain. I also suggest and discuss several significant enhancements to the original method that are improving its applicability, outcome and runtime. For the systematic detection and interpretation of subnetworks, I have developed the EnrichmentBrowser software package. It implements several state-of-the-art methods besides GGEA, and allows to combine and explore results across methods. As part of the Bioconductor repository, the package provides a unified access to the different methods and, thus, greatly simplifies the usage for biologists. Extensions to this framework, that support automating of biological interpretation routines, are also presented. In conclusion, this work contributes substantially to the research field of network-based analysis of gene expression data with respect to regulatory network construction, subnetwork detection, and their biological interpretation. This also includes recent developments as well as areas of ongoing research, which are discussed in the context of current and future questions arising from the new generation of genomic data