Guillermo de Anda-Jáuregui

Crosstalk events in the estrogen signaling pathway may affect tamoxifen efficacy in breast cancer molecular subtypes

Steroid hormones are involved on cell growth, development and differentiation. Such effects are often mediated by steroid receptors. One paradigmatic example of this coupling is the estrogen signaling pathway. Its dysregulation is involved in most tumors of the mammary gland. It is thus an important pharmacological target in breast cancer. This pathway, however, crosstalks with several other molecular pathways, a fact that may have consequences for the effectiveness of hormone modulating drug therapies, such as tamoxifen. For this work, we performed a systematic analysis of the major routes involved in crosstalk phenomena with the estrogen pathway - based on gene expression experiments (819 samples) and pathway analysis (493 samples) - for biopsy-captured tissue and contrasted in two independent datasets with in vivo and in vitro pharmacological stimulation. Our results confirm the presence of a number of crosstalk events across the estrogen signaling pathway with others that are dysregulated in different molecular subtypes of breast cancer. These may be involved in proliferation, invasiveness and apoptosis-evasion in patients. The results presented may open the way to new designs of adjuvant and neoadjuvant therapies for breast cancer treatment.

RNA-Seq based genome-wide analysis reveals loss of inter-chromosomal regulation in breast cancer

Breast cancer is a complex heterogeneous disease. Common hallmark features of cancer can be found. Their origin may be traced back to their intricate relationships governing regulatory programs during the development of this disease. To unveil distinctive features of the transcriptional regulation program in breast cancer, a pipeline for RNA-seq analysis in 780 breast cancer and 101 healthy breast samples, at gene expression and network level, was implemented. Inter-chromosomal relationships between genes resulted strikingly scarce in a cancer network, in comparison to its healthy counterpart. We suggest that inter-chromosomal regulation loss may be a novel feature in breast cancer. Additional evidence was obtained by independent validation in microarray and Hi-C data as well as supplementary computational analyses. Functional analysis showed upregulation in processes related to cell cycle and division; while migration, adhesion and cell-to-cell communication, were downregulated. Both the BRCA1 DNA repairing signalling and the Estrogen-mediated G1/S phase entry pathways were found upregulated. In addition, a synergistic underexpression of the γ-protocadherin complex, located at Chr5q31 is also shown. This region has previously been reported to be hypermethylated in breast cancer. These findings altogether provide further evidence for the central role of transcriptional regulatory programs in shaping malignant phenotypes.

Exploration of the Anti-Inflammatory Drug Space Through Network Pharmacology: Applications for Drug Repurposing

The quintessential biological response to disease is inflammation. It is a driver and an important element in a wide range of pathological states. Pharmacological management of inflammation is therefore central in the clinical setting. Anti-inflammatory drugs modulate specific molecules involved in the inflammatory response; these drugs are traditionally classified as steroidal and non-steroidal drugs. However, the effects of these drugs are rarely limited to their canonical targets, affecting other molecules and altering biological functions with system-wide effects that can lead to the emergence of secondary therapeutic applications or adverse drug reactions (ADRs). In this study, relationships among anti-inflammatory drugs, functional pathways, and ADRs were explored through network models. We integrated structural drug information, experimental anti-inflammatory drug perturbation gene expression profiles obtained from the Connectivity Map and Library of Integrated Network-Based Cellular Signatures, functional pathways in the Kyoto Encyclopedia of Genes and Genomes (KEGG) and Reactome databases, as well as adverse reaction information from the U.S. Food and Drug Administration (FDA) Adverse Event Reporting System (FAERS). The network models comprise nodes representing anti-inflammatory drugs, functional pathways, and adverse effects. We identified structural and gene perturbation similarities linking anti-inflammatory drugs. Functional pathways were connected to drugs by implementing Gene Set Enrichment Analysis (GSEA). Drugs and adverse effects were connected based on the proportional reporting ratio (PRR) of an adverse effect in response to a given drug. Through these network models, relationships among anti-inflammatory drugs, their functional effects at the pathway level, and their adverse effects were explored. These networks comprise 70 different anti-inflammatory drugs, 462 functional pathways, and 1,175 ADRs. Network-based properties, such as degree, clustering coefficient, and node strength, were used to identify new therapeutic applications within and beyond the anti-inflammatory context, as well as ADR risk for these drugs, helping to select better repurposing candidates. Based on these parameters, we identified naproxen, meloxicam, etodolac, tenoxicam, flufenamic acid, fenoprofen, and nabumetone as candidates for drug repurposing with lower ADR risk. This network-based analysis pipeline provides a novel way to explore the effects of drugs in a therapeutic space.

Network Modularity in Breast Cancer Molecular Subtypes

Breast cancer is a heterogeneous and complex disease, a clear manifestation of this is its classification into different molecular subtypes. On the other hand, gene transcriptional networks may exhibit different modular structures that can be related to known biological processes. Thus, modular structures in transcriptional networks may be seen as manifestations of regulatory structures that tightly controls biological processes. In this work, we identify modular structures on gene transcriptional networks previously inferred from microarray data of molecular subtypes of breast cancer: luminal A, luminal B, basal, and HER2-enriched. We analyzed the modules (communities) found in each network to identify particular biological functions (described in the Gene Ontology database) associated to them. We further explored these modules and their associated functions to identify common and unique features that could allow a better level of description of breast cancer, particularly in the basal-like subtype, the most aggressive and poor prognosis manifestation. Our findings related to the immune system and a decrease in cell death-related processes in basal subtype could help to understand it and design strategies for its treatment.

Network Modularity and Hierarchical Structure in Breast Cancer Molecular Subtypes

Breast Cancer is the malignant neoplasm with the highest incidence and mortality among women worldwide. It is a heterogeneous and complex disease, its classification in different molecular subtypes is a clear manifestation of this. The recent abundance of genomic data on cancer, make possible to propose theoretical approaches to model the process of genetic regulation. One of these approaches is gene transcriptional networks which represent the regulation and co-expression of genes as well-defined mathematical objects. These complex networks have global topological and dynamic properties. One of these properties is modular structure, which may be related to known or annotated biological processes. In this way, different modular structures in transcription networks can be seen as manifestations of regulatory structures that closely control some biological processes. In this work, we identify modular structures on gene transcriptional networks previously inferred from microarray data of molecular subtypes of breast cancer: luminal A, luminal B, basal, and HER2-enriched. Using a methodology based on the identification of functional modules in transcriptional networks, we analyzed the modules (communities) found in each network to identify particular biological functions (described in the Gene Ontology database) associated to them. We also explored the hierarchical structure of these modules and their functions to identify unique and common characteristics that could allow a better level of description of such molecular subtypes of breast cancer. This approach and its findings are leading us to a better understanding of the molecular cancer subtypes and even contribute to direct experiments and design strategies for their treatment.

Identification of Casiopeina II-gly secondary targets through a systems pharmacology approach

Casiopeinas are a group of copper-based compounds designed to be used as less toxic, more efficient chemotherapeutic agents. In this study, we analyzed the in vitro effects of Casiopeina Il-gly on the expression of canonical biological pathways. Using microarray data from HeLa cell lines treated with Casiopeina II-gly, we identified biological pathways that are perturbed after treatment. We present a novel approach integrating pathway analysis and network theory: The Pathway Crosstalk Network. We constructed a network with deregulated pathways, featuring links between those pathways that crosstalk with each other. We identified modules grouping deregulated pathways that are functionally related. Through this approach, we were able to identify three features of Casiopeina treatment: a) Perturbation of signaling pathways, related to induction of apoptosis; b) perturbation of metabolic pathways, and c) activation of immune responses. These findings can be useful to drive new experimental exploration on their role in adverse effects and efficacy of Casiopeinas.

Loss of Trans Regulation in Breast Cancer Molecular Subtypes

Background: Breast cancer is a complex heterogeneous disease. A clear example is given by the four molecular subtypes: Luminal A, Luminal B, HER2-Enriched and Basal-like. These subtypes give way to different therapeutic approaches to deal with different prognosis. Despite these differences, common hallmark features of cancer can be found, which its origin is traced back to the intricate relationships governing regulatory programs. In our recent work, by constructing RNA-Seq normal tissue and breast cancer gene regulatory networks, we have observed the phenomenon of loss of inter-chromosomal regulation. Our results showed that cis- regulation in breast cancer tissue occurs mostly between neighbour genes. On the contrast, in non-cancerous tissue, gene-gene regulation appears along the whole genome. Here, we extend the aforementioned approach, in order to observe into what extent the loss of trans- regulation occurs in the different intrinsic breast cancer subtypes. Methods: A collection of 780 RNA-Seq The Cancer Genome Atlas breast cancer samples were classified using PAM50 algorithm. Differential expression analysis was performed between each subtype and additional 101 normal tissue samples. Gene regulatory networks were inferred for each of the four subtypes and the normal tissue. Circos plots visualization was used to contrast the cis/trans regulation proportion. Finally, power-law regression analyses were fitted to explain the statistical relationship between genes and the distance between genes. Results: Inter and intra-chromosome relationships occur approximately in the same proportion in the healthy network. Meanwhile, the four subtypes present a loss of trans- regulation. The decrease of trans- regulations exhibits different patterns among subtypes. Additionally, the strength of cis- regulatory interactions decays exponentially with the distance in the four subtypes. But, in the non-cancerous phenotype, distance does not influence the strength of the interactions. Conclusions: With this kind of approach, we have been able to integrate gene regulation and physical distance to elaborate a more comprehensive landscape in cancer genomics. Here, we opened the possibility to analyse in a complementary fashion the regulatory program of molecular subtypes of breast cancer. This effort may be complemented with copy number alterations, micro-RNAs or Hi-C data with the aim of providing a multi-omics-based framework to elaborate more specific questions in the era of personalized medicine.Breast carcinomas are characterized by anomalous gene regulatory programs. As is well known, gene expression programs are able to shape phenotypes. Hence, the understanding of gene co-expression may shed light on the underlying mechanisms behind the transcriptional regulatory programs affecting tumor development and evolution. For instance, in breast cancer, there is a clear loss of inter-chromosomal (trans-) co-expression, compared with healthy tissue. At the same time cis- (intra-chromosomal) interactions are favored in breast tumors. In order to have a deeper understanding of regulatory phenomena in cancer, here, we constructed Gene Co-expression Networks by using 848 RNA-seq whole-genome samples corresponding to the four breast cancer molecular subtypes, as well as healthy tissue. We quantify the cis-/trans- co-expression imbalance in all phenotypes. Additionally, we measured the association between co-expression and physical distance between genes, and characterized the proportion of intra/inter-cytoband interactions per phenotype. We confirmed loss of trans- co-expression in all molecular subtypes. We also observed that gene cisco-expression decays abruptly with distance in all tumors in contrast with healthy tissue. We observed co-expressed gene hotspots, that tend to be connected at cytoband regions, and coincide accurately with already known copy number altered regions, such as Chr17q12, or Chr8q24.3 for all subtypes. Our methodology recovered different alterations already reported for specific breast cancer subtypes, showing how co-expression network approaches might help to capture distinct events that modify the cell regulatory program.

The Hierarchical Modular Structure of HER2+ Breast Cancer Network

HER2-enriched breast cancer is a complex disease characterized by the overexpression of the ERBB2 amplicon. While the effects of this genomic aberration on the pathology have been studied, genome-wide deregulation patterns in this subtype of cancer are also observed. A novel approach to the study of this malignant neoplasy is the use of transcriptional networks. These networks generally exhibit modular structures, which in turn may be associated to biological processes. This modular regulation of biological functions may also exhibit a hierarchical structure, with deeper levels of modular organization accounting for more specific functional regulation. In this work, we identified the most probable (maximum likelihood) model of the hierarchical modular structure of the HER2-enriched transcriptional network as reconstructed from gene expression data, and analyzed the statistical associations of modules and submodules to biological functions. We found modular structures, independent from direct ERBB2 amplicon regulation, involved in different biological functions such as signaling, immunity, and cellular morphology. Higher resolution submodules were identified in more specific functions, such as micro-RNA regulation and the activation of viral-like immune response. We propose the approach presented here as one that may help to unveil mechanisms involved in the development of the pathology.

Nonredundant, Highly Connected MicroRNAs Control Functionality in Breast Cancer Networks

Alterations to transcriptional regulation are an important factor in breast cancer. Noncoding RNA, such as microRNA (miR), have very influential roles in the transcriptional regulation of genes. Transcriptional regulation can be successfully modeled and analyzed using complex network theory. Particularly, interactions between two distinct classes of biological elements, such as miR and genes, can be approached through the bipartite network formalism. Based on bipartite network properties, it is possible to identify highly influential miRs in the network, such as those that have a large number of connections indicating regulation of a large set of genes. Some miRs in a network are nonredundant, which indicates that they are solely responsible of the regulation of a particular set of genes, which in turn may be associated to a particular biological process. We hypothesize that highly influential, nonredundant miRs, which we call Commodore miRs (Cdre-miRs), have an important role on the control of biological functions through transcriptional networks. In this work, we analyze the regulation of gene expression by miRs in healthy and cancerous breast tissue using bipartite miR-gene networks inferred from the Cancer Genome Atlas (TCGA) expression data. We observe differences in the degree, clustering coefficient and redundancy distributions for miRs and genes in the network, indicating differences in the way that these elements interact with each other. Furthermore, we identify a small set of five Cdre-miRs in the breast cancer network: miR-190b, miR-let7i, miR-292-b, miR-511, and miR-141. The neighborhood of genes controlled by each of these miRs is involved in particular biological functions such as dynein structure-associated processes, immune response, angiogenesis, cytokine activity, and cell motility. We propose that these Cdre-miRs are important control elements of biological functions deregulated in breast cancer.