Uniformly curated signaling pathways reveal tissue-specific cross-talks and support drug target discovery

Motivation: Signaling pathways control a large variety of cellular processes. However, currently, even within the same database signaling pathways are often curated at different levels of detail. This makes comparative and cross-talk analyses difficult. Results: We present SignaLink, a database containing 8 major signaling pathways from Caenorhabditis elegans, Drosophila melanogaster, and humans. Based on 170 review and approx. 800 research articles, we have compiled pathways with semi-automatic searches and uniform, well-documented curation rules. We found that in humans any two of the 8 pathways can cross-talk. We quantified the possible tissue- and cancer-specific activity of cross-talks and found pathway-specific expression profiles. In addition, we identified 327 proteins relevant for drug target discovery. Conclusions: We provide a novel resource for comparative and cross-talk analyses of signaling pathways. The identified multi-pathway and tissue-specific cross-talks contribute to the understanding of the signaling complexity in health and disease and underscore its importance in network-based drug target selection. Availability: http://SignaLink.orgComment: 9 pages, 4 figures, 2 tables and a supplementary info with 5 Figures and 13 Table

Utility of network integrity methods in therapeutic target identification

Analysis of the biological gene networks involved in a disease may lead to the identification of therapeutic targets. Such analysis requires exploring network properties, in particular the importance of individual network nodes (i.e., genes). There are many measures that consider the importance of nodes in a network and some may shed light on the biological significance and potential optimality of a gene or set of genes as therapeutic targets. This has been shown to be the case in cancer therapy. A dilemma exists, however, in finding the best therapeutic targets based on network analysis since the optimal targets should be nodes that are highly influential in, but not toxic to, the functioning of the entire network. In addition, cancer therapeutics targeting a single gene often result in relapse since compensatory, feedback and redundancy loops in the network may offset the activity associated with the targeted gene. Thus, multiple genes reflecting parallel functional cascades in a network should be targeted simultaneously, but require the identification of such targets. We propose a methodology that exploits centrality statistics characterizing the importance of nodes within a gene network that is constructed from the gene expression patterns in that network. We consider centrality measures based on both graph theory and spectral graph theory. We also consider the origins of a network topology, and show how different available representations yield different node importance results. We apply our techniques to tumor gene expression data and suggest that the identification of optimal therapeutic targets involving particular genes, pathways and sub-networks based on an analysis of the nodes in that network is possible and can facilitate individualized cancer treatments. The proposed methods also have the potential to identify candidate cancer therapeutic targets that are not thought to be oncogenes but nonetheless play important roles in the functioning of a cancer-related network or pathway

Identification of critical paralog groups with indispensable roles in the regulation of signaling flow

Extensive cross-talk between signaling pathways is required to integrate the myriad of extracellular signal combinations at the cellular level. Gene duplication events may lead to the emergence of novel functions, leaving groups of similar genes - termed paralogs - in the genome. To distinguish critical paralog groups (CPGs) from other paralogs in human signaling networks, we developed a signaling network-based method using cross-talk annotation and tissue-specific signaling flow analysis. 75 CPGs were found with higher degree, betweenness centrality, closeness, and ‘bowtieness’ when compared to other paralogs or other proteins in the signaling network. CPGs had higher diversity in all these measures, with more varied biological functions and more specific post-transcriptional regulation than non-critical paralog groups (non-CPG). Using TGF-beta, Notch and MAPK pathways as examples, SMAD2/3, NOTCH1/2/3 and MEK3/6-p38 CPGs were found to regulate the signaling flow of their respective pathways. Additionally, CPGs showed a higher mutation rate in both inherited diseases and cancer, and were enriched in drug targets. In conclusion, the results revealed two distinct types of paralog groups in the signaling network: CPGs and non-CPGs. Thus highlighting the importance of CPGs as compared to non-CPGs in drug discovery and disease pathogenesis

Customizing the therapeutic response of signaling networks to promote antitumor responses by drug combinations

Drug resistance, de novo and acquired, pervades cellular signaling networks (SNs) from one signaling motif to another as a result of cancer progression and/or drug intervention. This resistance is one of the key determinants of efficacy in targeted anti-cancer drug therapy. Although poorly understood, drug resistance is already being addressed in combination therapy by selecting drug targets where SN sensitivity increases due to combination components or as a result of de novo or acquired mutations. Additionally, successive drug combinations have shown low resistance potential. To promote a rational, systematic development of combination therapies, it is necessary to establish the underlying mechanisms that drive the advantages of combination therapies, and design methods to determine drug targets for combination regimens. Based on a joint systems analysis of cellular SN response and its sensitivity to drug action and oncogenic mutations, we describe an in silico method to analyze the targets of drug combinations. Our method explores mechanisms of sensitizing the SN through a combination of two drugs targeting vertical signaling pathways. We propose a paradigm of SN response customization by one drug to both maximize the effect of another drug in combination and promote a robust therapeutic response against oncogenic mutations. The method was applied to customize the response of the ErbB/PI3K/PTEN/AKT pathway by combination of drugs targeting HER2 receptors and proteins in the down-stream pathway. The results of a computational experiment showed that the modification of the SN response from hyperbolic to smooth sigmoid response by manipulation of two drugs in combination leads to greater robustness in therapeutic response against oncogenic mutations determining cancer heterogeneity. The application of this method in drug combination co-development suggests a combined evaluation of inhibition effects together with the capability of drug combinations to suppress resistance mechanisms before they become clinically manifest

New markers for human ovarian cancer that link platinum resistance to the cancer stem cell phenotype and define new therapeutic combinations and diagnostic tools

BACKGROUND: Ovarian cancer is the leading cause of gynecologic cancer-related death, due in part to a late diagnosis and a high rate of recurrence. Primary and acquired platinum resistance is related to a low response probability to subsequent lines of treatment and to a poor survival. Therefore, a comprehensive understanding of the mechanisms that drive platinum resistance is urgently needed. METHODS: We used bioinformatics analysis of public databases and RT-qPCR to quantitate the relative gene expression profiles of ovarian tumors. Many of the dysregulated genes were cancer stem cell (CSC) factors, and we analyzed its relation to therapeutic resistance in human primary tumors. We also performed clustering and in vitro analyses of therapy cytotoxicity in tumorspheres. RESULTS: Using bioinformatics analysis, we identified transcriptional targets that are common endpoints of genetic alterations linked to platinum resistance in ovarian tumors. Most of these genes are grouped into 4 main clusters related to the CSC phenotype, including the DNA damage, Notch and C-KIT/MAPK/MEK pathways. The relative expression of these genes, either alone or in combination, is related to prognosis and provide a connection between platinum resistance and the CSC phenotype. However, the expression of the CSC-related markers was heterogeneous in the resistant tumors, most likely because there were different CSC pools. Furthermore, our in vitro results showed that the inhibition of the CSC-related targets lying at the intersection of the DNA damage, Notch and C-KIT/MAPK/MEK pathways sensitize CSC-enriched tumorspheres to platinum therapies, suggesting a new option for the treatment of patients with platinum-resistant ovarian cancer. CONCLUSIONS: The current study presents a new approach to target the physiology of resistant ovarian tumor cells through the identification of core biomarkers. We hypothesize that the identified mutations confer platinum resistance by converging to activate a few pathways and to induce the expression of a few common, measurable and targetable essential genes. These pathways include the DNA damage, Notch and C-KIT/MAPK/MEK pathways. Finally, the combined inhibition of one of these pathways with platinum treatment increases the sensitivity of CSC-enriched tumorspheres to low doses of platinum, suggesting a new treatment for ovarian cancerSpanish Ministry of Education FPU12/01380Spanish Ministry of Economy and Competitivity, Plan Estatal de I + D + I 2013–2016Spanish Ministry of Science, Innovation and Universities (RTI2018–097455-B-I00)CIBER de Cáncer (CD16/12/00275)Spanish Consejería de Salud of the Junta de Andalucia (PI-0397-2017

The ErbB2 receptor in gastric cancer. the quick-change artist

The ErbB family of receptors is providing the oncogenic signals necessary to cells to become transformed. In gastric cancer (GC) the ErbB2 (HER2) expression is associated with a poor prognosis, but addition of ErbB-targeted therapeutics to chemotherapy has produced unsatisfactory results with moderate improved outcomes for patients. The ToGA trail has revolutionized the treatment of GC, introducing the use of trastuzumab and changing the poor prognosis of these patients. However, this study reported only a modest prolongation of progression-free survival (PFS) and overall survival (OS) in patients with high expression of ErbB2 protein, with a large percentage of initially good responders, then becoming refractory to therapy within one year. These findings indicate the occurrence of resistant phenotypes arising from diverse adaptive and genetic changes. Due to the promiscuity of ErbB2 in the EGFR family signaling network, the use of ErbB targeted mono-therapies certainly contributes to a redistribution of the stoichiometry among receptors leading to the activation of compensatory pathways, suggesting that survival of cancer cells is sustained, at least in part, by the network of the ErbB receptors and their ligands. For these reasons, the use of combination therapies is becoming the most logical strategy for any type of cancer treatment, including GC. In this review we summarize information regarding mechanisms, pathways and molecules involved in the resistance to ErbB-targeted molecules with the intent to provide rational guidelines for developing more efficient therapeutic approaches

A Comprehensive Molecular Interaction Map for Rheumatoid Arthritis

Computational biology contributes to a variety of areas related to life sciences and, due to the growing impact of translational medicine - the scientific approach to medicine in tight relation with basic science -, it is becoming an important player in clinical-related areas. In this study, we use computation methods in order to improve our understanding of the complex interactions that occur between molecules related to Rheumatoid Arthritis (RA).Due to the complexity of the disease and the numerous molecular players involved, we devised a method to construct a systemic network of interactions of the processes ongoing in patients affected by RA. The network is based on high-throughput data, refined semi-automatically with carefully curated literature-based information. This global network has then been topologically analysed, as a whole and tissue-specifically, in order to translate the experimental molecular connections into topological motifs meaningful in the identification of tissue-specific markers and targets in the diagnosis, and possibly in the therapy, of RA.’

Trop2 and its overexpression in cancers: regulation and clinical/therapeutic implications.

Trop2 is a transmembrane glycoprotein encoded by the Tacstd2 gene. It is an intracellular calcium signal transducer that is differentially expressed in many cancers. It signals cells for self-renewal, proliferation, invasion, and survival. It has stem cell-like qualities. Trop2 is expressed in many normal tissues, though in contrast, it is overexpressed in many cancers and the overexpression of Trop2 is of prognostic significance. Several ligands have been proposed that interact with Trop2. Trop2 signals the cells via different pathways and it is transcriptionally regulated by a complex network of several transcription factors. Trop2 expression in cancer cells has been correlated with drug resistance. Several strategies target Trop2 on cancer cells that include antibodies, antibody fusion proteins, chemical inhibitors, nanoparticles, etc. The in vitro studies and pre-clinical studies, using these various therapeutic treatments, have resulted in significant inhibition of tumor cell growth both in vitro and in vivo in mice. A clinical study is underway using IMMU-132 (hrS7 linked to SN38) in patients with epithelial cancers. This review describes briefly the various characteristics of cancer cells overexpressing Trop2 and the potential application of Trop2 as both a prognostic biomarker and as a therapeutic target to reverse resistance

Early and late effects of pharmacological ALK inhibition on the neuroblastoma transcriptome

Background: Neuroblastoma is an aggressive childhood malignancy of the sympathetic nervous system. Despite multi-modal therapy, survival of high-risk patients remains disappointingly low, underscoring the need for novel treatment strategies. The discovery of ALK activating mutations opened the way to precision treatment in a subset of these patients. Previously, we investigated the transcriptional effects of pharmacological ALK inhibition on neuroblastoma cell lines, six hours after TAE684 administration, resulting in the 77-gene ALK signature, which was shown to gradually decrease from 120 minutes after TAE684 treatment, to gain deeper insight into the molecular effects of oncogenic ALK signaling. Aim: Here, we further dissected the transcriptional dynamic profiles of neuroblastoma cells upon TAE684 treatment in a detailed timeframe of ten minutes up to six hours after inhibition, in order to identify additional early targets for combination treatment. Results: We observed an unexpected initial upregulation of positively regulated MYCN target genes following subsequent downregulation of overall MYCN activity. In addition, we identified adrenomedullin (ADM), previously shown to be implicated in sunitinib resistance, as the earliest response gene upon ALK inhibition. Conclusions: We describe the early and late effects of ALK inhibitor TAE684 treatment on the neuroblastoma transcriptome. The observed unexpected upregulation of ADM warrants further investigation in relation to putative ALK resistance in neuroblastoma patients currently undergoing ALK inhibitor treatment