Multiscale networks in multiple sclerosis.

Complex diseases such as Multiple Sclerosis (MS) cover a wide range of biological scales, from genes and proteins to cells and tissues, up to the full organism. In fact, any phenotype for an organism is dictated by the interplay among these scales. We conducted a multilayer network analysis and deep phenotyping with multi-omics data (genomics, phosphoproteomics and cytomics), brain and retinal imaging, and clinical data, obtained from a multicenter prospective cohort of 328 patients and 90 healthy controls. Multilayer networks were constructed using mutual information for topological analysis, and Boolean simulations were constructed using Pearson correlation to identified paths within and among all layers. The path more commonly found from the Boolean simulations connects protein MK03, with total T cells, the thickness of the retinal nerve fiber layer (RNFL), and the walking speed. This path contains nodes involved in protein phosphorylation, glial cell differentiation, and regulation of stress-activated MAPK cascade, among others. Specific paths identified were subsequently analyzed by flow cytometry at the single-cell level. Combinations of several proteins (GSK3AB, HSBP1 or RS6) and immune cells (Th17, Th1 non-classic, CD8, CD8 Treg, CD56 neg, and B memory) were part of the paths explaining the clinical phenotype. The advantage of the path identified from the Boolean simulations is that it connects information about these known biological pathways with the layers at higher scales (retina damage and disability). Overall, the identified paths provide a means to connect the molecular aspects of MS with the overall phenotype.

MAPK pathway and B cells overactivation in multiple sclerosis revealed by phosphoproteomics and genomic analysis.

Dysregulation of signaling pathways in multiple sclerosis (MS) can be analyzed by phosphoproteomics in peripheral blood mononuclear cells (PBMCs). We performed in vitro kinetic assays on PBMCs in 195 MS patients and 60 matched controls and quantified the phosphorylation of 17 kinases using xMAP assays. Phosphoprotein levels were tested for association with genetic susceptibility by typing 112 single-nucleotide polymorphisms (SNPs) associated with MS susceptibility. We found increased phosphorylation of MP2K1 in MS patients relative to the controls. Moreover, we identified one SNP located in the PHDGH gene and another on IRF8 gene that were associated with MP2K1 phosphorylation levels, providing a first clue on how this MS risk gene may act. The analyses in patients treated with disease-modifying drugs identified the phosphorylation of each receptor's downstream kinases. Finally, using flow cytometry, we detected in MS patients increased STAT1, STAT3, TF65, and HSPB1 phosphorylation in CD19+ cells. These findings indicate the activation of cell survival and proliferation (MAPK), and proinflammatory (STAT) pathways in the immune cells of MS patients, primarily in B cells. The changes in the activation of these kinases suggest that these pathways may represent therapeutic targets for modulation by kinase inhibitors.

Lysophosphatidic Acid Is a Proinflammatory Stimulus of Renal Tubular Epithelial Cells.

Chronic kidney disease (CKD) refers to a spectrum of diseases defined by renal fibrosis, permanent alterations in kidney structure, and low glomerular-filtration rate. Prolonged epithelial-tubular damage involves a series of changes that eventually lead to CKD, highlighting the importance of tubular epithelial cells in this process. Lysophosphatidic acid (LPA) is a bioactive lipid that signals mainly through its six cognate LPA receptors and is implicated in several chronic inflammatory pathological conditions. In this report, we have stimulated human proximal tubular epithelial cells (HKC-8) with LPA and 175 other possibly pathological stimuli, and simultaneously detected the levels of 27 intracellular phosphoproteins and 32 extracellular secreted molecules with multiplex ELISA. This quantification revealed a large amount of information concerning the signaling and the physiology of HKC-8 cells that can be extrapolated to other proximal tubular epithelial cells. LPA responses clustered with pro-inflammatory stimuli such as TNF and IL-1, promoting the phosphorylation of important inflammatory signaling hubs, including CREB1, ERK1, JUN, IκΒα, and MEK1, as well as the secretion of inflammatory factors of clinical relevance, including CCL2, CCL3, CXCL10, ICAM1, IL-6, and IL-8, most of them shown for the first time in proximal tubular epithelial cells. The identified LPA-induced signal-transduction pathways, which were pharmacologically validated, and the secretion of the inflammatory factors offer novel insights into the possible role of LPA in CKD pathogenesis.

Cadherin-11, Sparc-related modular calcium binding protein-2, and Pigment epithelium-derived factor are promising non-invasive biomarkers of kidney fibrosis.

Kidney fibrosis constitutes the shared final pathway of nearly all chronic nephropathies, but biomarkers for the non-invasive assessment of kidney fibrosis are currently not available. To address this, we characterize five candidate biomarkers of kidney fibrosis: Cadherin-11 (CDH11), Sparc-related modular calcium binding protein-2 (SMOC2), Pigment epithelium-derived factor (PEDF), Matrix-Gla protein, and Thrombospondin-2. Gene expression profiles in single-cell and single-nucleus RNA-sequencing (sc/snRNA-seq) datasets from rodent models of fibrosis and human chronic kidney disease (CKD) were explored, and Luminex-based assays for each biomarker were developed. Plasma and urine biomarker levels were measured using independent prospective cohorts of CKD: the Boston Kidney Biopsy Cohort, a cohort of individuals with biopsy-confirmed semiquantitative assessment of kidney fibrosis, and the Seattle Kidney Study, a cohort of patients with common forms of CKD. Ordinal logistic regression and Cox proportional hazards regression models were used to test associations of biomarkers with interstitial fibrosis and tubular atrophy and progression to end-stage kidney disease and death, respectively. Sc/snRNA-seq data confirmed cell-specific expression of biomarker genes in fibroblasts. After multivariable adjustment, higher levels of plasma CDH11, SMOC2, and PEDF and urinary CDH11 and PEDF were significantly associated with increasing severity of interstitial fibrosis and tubular atrophy in the Boston Kidney Biopsy Cohort. In both cohorts, higher levels of plasma and urinary SMOC2 and urinary CDH11 were independently associated with progression to end-stage kidney disease. Higher levels of urinary PEDF associated with end-stage kidney disease in the Seattle Kidney Study, with a similar signal in the Boston Kidney Biopsy Cohort, although the latter narrowly missed statistical significance. Thus, we identified CDH11, SMOC2, and PEDF as promising non-invasive biomarkers of kidney fibrosis.

Multiscale Regulation of the Intervertebral Disc: Achievements in Experimental, In Silico, and Regenerative Research.

Intervertebral disc (IVD) degeneration is a major risk factor of low back pain. It is defined by a progressive loss of the IVD structure and functionality, leading to severe impairments with restricted treatment options due to the highly demanding mechanical exposure of the IVD. Degenerative changes in the IVD usually increase with age but at an accelerated rate in some individuals. To understand the initiation and progression of this disease, it is crucial to identify key top-down and bottom-up regulations' processes, across the cell, tissue, and organ levels, in health and disease. Owing to unremitting investigation of experimental research, the comprehension of detailed cell signaling pathways and their effect on matrix turnover significantly rose. Likewise, in silico research substantially contributed to a holistic understanding of spatiotemporal effects and complex, multifactorial interactions within the IVD. Together with important achievements in the research of biomaterials, manifold promising approaches for regenerative treatment options were presented over the last years. This review provides an integrative analysis of the current knowledge about (1) the multiscale function and regulation of the IVD in health and disease, (2) the possible regenerative strategies, and (3) the in silico models that shall eventually support the development of advanced therapies.

Non-lethal proteasome inhibition activates pro-tumorigenic pathways in multiple myeloma cells.

Multiple myeloma (MM) is a haematological malignancy being characterized by clonal plasma cell proliferation in the bone marrow. Targeting the proteasome with specific inhibitors (PIs) has been proven a promising therapeutic strategy and PIs have been approved for the treatment of MM and mantle-cell lymphoma; yet, while outcome has improved, most patients inevitably relapse. As relapse refers to MM cells that survive therapy, we sought to identify the molecular responses induced in MM cells after non-lethal proteasome inhibition. By using bortezomib (BTZ), epoxomicin (EPOX; a carfilzomib-like PI) and three PIs, namely Rub999, PR671A and Rub1024 that target each of the three proteasome peptidases, we found that only BTZ and EPOX are toxic in MM cells at low concentrations. Phosphoproteomic profiling after treatment of MM cells with non-lethal (IC10 ) doses of the PIs revealed inhibitor- and cell type-specific readouts, being marked by the activation of tumorigenic STAT3 and STAT6. Consistently, cytokine/chemokine profiling revealed the increased secretion of immunosuppressive pro-tumorigenic cytokines (IL6 and IL8), along with the inhibition of potent T cell chemoattractant chemokines (CXCL10). These findings indicate that MM cells that survive treatment with therapeutic PIs shape a pro-tumorigenic immunosuppressive cellular and secretory bone marrow microenvironment that enables malignancy to relapse.

Post-treatment de-phosphorylation of p53 correlates with dasatinib responsiveness in malignant melanoma.

BACKGROUND: Dasatinib (Sprycel) was developed as a tyrosine kinase inhibitor targeting Bcr-Abl and the family of Src kinases. Dasatinib is commonly used for the treatment of acute lymphoblastic and chronic myelogenous leukemia. Previous clinical studies in melanoma returned inconclusive results and suggested that patients respond highly heterogeneously to dasatinib as single agent or in combination with standard-of-care chemotherapeutic dacarbazine. Reliable biomarkers to predict dasatinib responsiveness in melanoma have not yet been developed. RESULTS: Here, we collected comprehensive in vitro data from experimentally well-controlled conditions to study the effect of dasatinib, alone and in combination with dacarbazine, on cell proliferation and cell survival. Sixteen treatment conditions, covering therapeutically relevant concentrations ranges of both drugs, were tested in 12 melanoma cell lines with diverse mutational backgrounds. Melanoma cell lines responded heterogeneously and, importantly, dasatinib and dacarbazine did not synergize in suppressing proliferation or inducing cell death. Since dasatinib is a promiscuous kinase inhibitor, possibly affecting multiple disease-relevant pathways, we also determined if basal phospho-protein amounts and treatment-induced changes in phospho-protein levels are indicative of dasatinib responsiveness. We found that treatment-induced de-phosphorylation of p53 correlates with dasatinib responsiveness in malignant melanoma. CONCLUSIONS: Loss of p53 phosphorylation might be an interesting candidate for a kinetic marker of dasatinib responsiveness in melanoma, pending more comprehensive validation in future studies.

Dynamics and heterogeneity of brain damage in multiple sclerosis.

Multiple Sclerosis (MS) is an autoimmune disease driving inflammatory and degenerative processes that damage the central nervous system (CNS). However, it is not well understood how these events interact and evolve to evoke such a highly dynamic and heterogeneous disease. We established a hypothesis whereby the variability in the course of MS is driven by the very same pathogenic mechanisms responsible for the disease, the autoimmune attack on the CNS that leads to chronic inflammation, neuroaxonal degeneration and remyelination. We propose that each of these processes acts more or less severely and at different times in each of the clinical subgroups. To test this hypothesis, we developed a mathematical model that was constrained by experimental data (the expanded disability status scale [EDSS] time series) obtained from a retrospective longitudinal cohort of 66 MS patients with a long-term follow-up (up to 20 years). Moreover, we validated this model in a second prospective cohort of 120 MS patients with a three-year follow-up, for which EDSS data and brain volume time series were available. The clinical heterogeneity in the datasets was reduced by grouping the EDSS time series using an unsupervised clustering analysis. We found that by adjusting certain parameters, albeit within their biological range, the mathematical model reproduced the different disease courses, supporting the dynamic CNS damage hypothesis to explain MS heterogeneity. Our analysis suggests that the irreversible axon degeneration produced in the early stages of progressive MS is mainly due to the higher rate of myelinated axon degeneration, coupled to the lower capacity for remyelination. However, and in agreement with recent pathological studies, degeneration of chronically demyelinated axons is not a key feature that distinguishes this phenotype. Moreover, the model reveals that lower rates of axon degeneration and more rapid remyelination make relapsing MS more resilient than the progressive subtype. Therefore, our results support the hypothesis of a common pathogenesis for the different MS subtypes, even in the presence of genetic and environmental heterogeneity. Hence, MS can be considered as a single disease in which specific dynamics can provoke a variety of clinical outcomes in different patient groups. These results have important implications for the design of therapeutic interventions for MS at different stages of the disease.

A crowd-sourcing approach for the construction of species-specific cell signaling networks.

MOTIVATION: Animal models are important tools in drug discovery and for understanding human biology in general. However, many drugs that initially show promising results in rodents fail in later stages of clinical trials. Understanding the commonalities and differences between human and rat cell signaling networks can lead to better experimental designs, improved allocation of resources and ultimately better drugs. RESULTS: The sbv IMPROVER Species-Specific Network Inference challenge was designed to use the power of the crowds to build two species-specific cell signaling networks given phosphoproteomics, transcriptomics and cytokine data generated from NHBE and NRBE cells exposed to various stimuli. A common literature-inspired reference network with 220 nodes and 501 edges was also provided as prior knowledge from which challenge participants could add or remove edges but not nodes. Such a large network inference challenge not based on synthetic simulations but on real data presented unique difficulties in scoring and interpreting the results. Because any prior knowledge about the networks was already provided to the participants for reference, novel ways for scoring and aggregating the results were developed. Two human and rat consensus networks were obtained by combining all the inferred networks. Further analysis showed that major signaling pathways were conserved between the two species with only isolated components diverging, as in the case of ribosomal S6 kinase RPS6KA1. Overall, the consensus between inferred edges was relatively high with the exception of the downstream targets of transcription factors, which seemed more difficult to predict. CONTACT: ebilal@us.ibm.com or gustavo@us.ibm.com. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Computed Biological Relations among Five Select Treatment-Related Organ/Tissue Toxicities.

Drug toxicity presents a major challenge in drug development and patient care. We set to build upon previous works regarding select drug-induced toxicities to find common patterns in the mode of action of the drugs associated with these toxicities. In particular, we focused on five disparate organ toxicities, peripheral neuropathy (PN), rhabdomyolysis (RM), Stevens-Johnson syndrome/toxic epidermal necrosis (SJS/TEN), lung injury (LI), and heart contraction-related cardiotoxicity (CT), and identified biological commonalities between and among the toxicities in terms of pharmacological targets and nearest neighbors (indirect effects) using the hyper-geometric test and a distance metric of Spearman correlation. There were 20 significant protein targets associated with two toxicities and 0 protein targets associated with three or more toxicities. Per Spearman distance, PN was closest to SJS/TEN compared to other pairs, whereas the pairs involving RM were more different than others excluding RM. The significant targets associated with RM outnumbered those associated with every one of the other four toxicities. Enrichment analysis of drug targets that are expressed in corresponding organ/tissues determined proteins that should be avoided in drug discovery. The identified biological patterns emerging from the mode of action of these drugs are statistically associated with these serious toxicities and could potentially be used as predictors for new drug candidates. The predictive power and usefulness of these biological patterns will increase with the database of these five toxicities. Furthermore, extension of our approach to all severe adverse reactions will produce useful biological commonalities for reference in drug discovery and development.

Signaling networks in MS: a systems-based approach to developing new pharmacological therapies.

The pathogenesis of multiple sclerosis (MS) involves alterations to multiple pathways and processes, which represent a significant challenge for developing more-effective therapies. Systems biology approaches that study pathway dysregulation should offer benefits by integrating molecular networks and dynamic models with current biological knowledge for understanding disease heterogeneity and response to therapy. In MS, abnormalities have been identified in several cytokine-signaling pathways, as well as those of other immune receptors. Among the downstream molecules implicated are Jak/Stat, NF-Kb, ERK1/3, p38 or Jun/Fos. Together, these data suggest that MS is likely to be associated with abnormalities in apoptosis/cell death, microglia activation, blood-brain barrier functioning, immune responses, cytokine production, and/or oxidative stress, although which pathways contribute to the cascade of damage and can be modulated remains an open question. While current MS drugs target some of these pathways, others remain untouched. Here, we propose a pragmatic systems analysis approach that involves the large-scale extraction of processes and pathways relevant to MS. These data serve as a scaffold on which computational modeling can be performed to identify disease subgroups based on the contribution of different processes. Such an analysis, targeting these relevant MS-signaling pathways, offers the opportunity to accelerate the development of novel individual or combination therapies.

Detecting and removing inconsistencies between experimental data and signaling network topologies using integer linear programming on interaction graphs.

Cross-referencing experimental data with our current knowledge of signaling network topologies is one central goal of mathematical modeling of cellular signal transduction networks. We present a new methodology for data-driven interrogation and training of signaling networks. While most published methods for signaling network inference operate on Bayesian, Boolean, or ODE models, our approach uses integer linear programming (ILP) on interaction graphs to encode constraints on the qualitative behavior of the nodes. These constraints are posed by the network topology and their formulation as ILP allows us to predict the possible qualitative changes (up, down, no effect) of the activation levels of the nodes for a given stimulus. We provide four basic operations to detect and remove inconsistencies between measurements and predicted behavior: (i) find a topology-consistent explanation for responses of signaling nodes measured in a stimulus-response experiment (if none exists, find the closest explanation); (ii) determine a minimal set of nodes that need to be corrected to make an inconsistent scenario consistent; (iii) determine the optimal subgraph of the given network topology which can best reflect measurements from a set of experimental scenarios; (iv) find possibly missing edges that would improve the consistency of the graph with respect to a set of experimental scenarios the most. We demonstrate the applicability of the proposed approach by interrogating a manually curated interaction graph model of EGFR/ErbB signaling against a library of high-throughput phosphoproteomic data measured in primary hepatocytes. Our methods detect interactions that are likely to be inactive in hepatocytes and provide suggestions for new interactions that, if included, would significantly improve the goodness of fit. Our framework is highly flexible and the underlying model requires only easily accessible biological knowledge. All related algorithms were implemented in a freely available toolbox SigNetTrainer making it an appealing approach for various applications.

Comparative toxicological assessment of cigarettes and new category products via an in vitro multiplex proteomics platform.

Cigarette smoking is a risk factor for several diseases such as cancer, cardiovascular disease (CVD), and chronic obstructive pulmonary diseases (COPD), however, the underlying mechanisms are not fully understood. Alternative nicotine products with reduced risk potential (RRPs) including tobacco heating products (THPs), and e-cigarettes have recently emerged as viable alternatives to cigarettes that may contribute to the overall strategy of tobacco harm reduction due to the significantly lower levels of toxicants in these products' emissions as compared to cigarette smoke. Assessing the effects of RRPs on biological responses is important to demonstrate the potential value of RRPs towards tobacco harm reduction. Here, we evaluated the inflammatory and signaling responses of human lung epithelial cells to aqueous aerosol extracts (AqE) generated from the 1R6F reference cigarette, the glo™ THP, and the Vype ePen 3.0 e-cigarette using multiplex analysis of 37 inflammatory and phosphoprotein markers. Cellular exposure to the different RRPs and 1R6F AqEs resulted in distinct response profiles with 1R6F being the most biologically active followed by glo™ and ePen 3.0. 1R6F activated stress-related and pro-survival markers c-JUN, CREB1, p38 MAPK and MEK1 and led to the release of IL-1α. glo™ activated MEK1 and decreased IL-1ß levels, whilst ePen 3.0 affected IL-1ß levels but had no effect on the signaling activity compared to untreated cells. Our results demonstrated the reduced biological effect of RRPs and suggest that targeted analysis of inflammatory and cell signaling mediators is a valuable tool for the routine assessment of RRPs.

Protein kinase D drives the secretion of invasion mediators in triple-negative breast cancer cell lines.

The protein kinase D (PKD) family members regulate the fission of cargo vesicles at the Golgi complex and play a pro-oncogenic role in triple-negative breast cancer (TNBC). Whether PKD facilitates the secretion of tumor-promoting factors in TNBC, however, is still unknown. Using the pharmacological inhibition of PKD activity and siRNA-mediated depletion of PKD2 and PKD3, we identified the PKD-dependent secretome of the TNBC cell lines MDA-MB-231 and MDA-MB-468. Mass spectrometry-based proteomics and antibody-based assays revealed a significant downregulation of extracellular matrix related proteins and pro-invasive factors such as LIF, MMP-1, MMP-13, IL-11, M-CSF and GM-CSF in PKD-perturbed cells. Notably, secretion of these proteins in MDA-MB-231 cells was predominantly controlled by PKD2 and enhanced spheroid invasion. Consistently, PKD-dependent secretion of pro-invasive factors was more pronounced in metastatic TNBC cell lines. Our study thus uncovers a novel role of PKD2 in releasing a pro-invasive secretome.

Predicting disease severity in multiple sclerosis using multimodal data and machine learning.

BACKGROUND: Multiple sclerosis patients would benefit from machine learning algorithms that integrates clinical, imaging and multimodal biomarkers to define the risk of disease activity. METHODS: We have analysed a prospective multi-centric cohort of 322 MS patients and 98 healthy controls from four MS centres, collecting disability scales at baseline and 2 years later. Imaging data included brain MRI and optical coherence tomography, and omics included genotyping, cytomics and phosphoproteomic data from peripheral blood mononuclear cells. Predictors of clinical outcomes were searched using Random Forest algorithms. Assessment of the algorithm performance was conducted in an independent prospective cohort of 271 MS patients from a single centre. RESULTS: We found algorithms for predicting confirmed disability accumulation for the different scales, no evidence of disease activity (NEDA), onset of immunotherapy and the escalation from low- to high-efficacy therapy with intermediate to high-accuracy. This accuracy was achieved for most of the predictors using clinical data alone or in combination with imaging data. Still, in some cases, the addition of omics data slightly increased algorithm performance. Accuracies were comparable in both cohorts. CONCLUSION: Combining clinical, imaging and omics data with machine learning helps identify MS patients at risk of disability worsening.

Leveraging systems biology approaches in clinical pharmacology.

Computational modeling has been adopted in all aspects of drug research and development, from the early phases of target identification and drug discovery to the late-stage clinical trials. The different questions addressed during each stage of drug R&D has led to the emergence of different modeling methodologies. In the research phase, systems biology couples experimental data with elaborate computational modeling techniques to capture lifecycle and effector cellular functions (e.g. metabolism, signaling, transcription regulation, protein synthesis and interaction) and integrates them in quantitative models. These models are subsequently used in various ways, i.e. to identify new targets, generate testable hypotheses, gain insights on the drug's mode of action (MOA), translate preclinical findings, and assess the potential of clinical drug efficacy and toxicity. In the development phase, pharmacokinetic/pharmacodynamic (PK/PD) modeling is the established way to determine safe and efficacious doses for testing at increasingly larger, and more pertinent to the target indication, cohorts of subjects. First, the relationship between drug input and its concentration in plasma is established. Second, the relationship between this concentration and desired or undesired PD responses is ascertained. Recognizing that the interface of systems biology with PK/PD will facilitate drug development, systems pharmacology came into existence, combining methods from PK/PD modeling and systems engineering explicitly to account for the implicated mechanisms of the target system in the study of drug-target interactions. Herein, a number of popular system biology methodologies are discussed, which could be leveraged within a systems pharmacology framework to address major issues in drug development.

A Combination of Conformation-Specific RAF Inhibitors Overcome Drug Resistance Brought about by RAF Overexpression.

Cancer cells often adapt to targeted therapies, yet the molecular mechanisms underlying adaptive resistance remain only partially understood. Here, we explore a mechanism of RAS/RAF/MEK/ERK (MAPK) pathway reactivation through the upregulation of RAF isoform (RAFs) abundance. Using computational modeling and in vitro experiments, we show that the upregulation of RAFs changes the concentration range of paradoxical pathway activation upon treatment with conformation-specific RAF inhibitors. Additionally, our data indicate that the signaling output upon loss or downregulation of one RAF isoform can be compensated by overexpression of other RAF isoforms. We furthermore demonstrate that, while single RAF inhibitors cannot efficiently inhibit ERK reactivation caused by RAF overexpression, a combination of two structurally distinct RAF inhibitors synergizes to robustly suppress pathway reactivation.

Α Humanized RANKL Transgenic Mouse Model of Progestin-Induced Mammary Carcinogenesis for Evaluation of Novel Therapeutics.

Receptor activator of nuclear factor-κB ligand (RANKL) is critically involved in mammary gland pathophysiology, while its pharmaceutical inhibition is being currently investigated in breast cancer. Herein, we investigated whether the overexpression of human RANKL in transgenic mice affects hormone-induced mammary carcinogenesis, and evaluated the efficacy of anti-RANKL treatments, such as OPG-Fc targeting both human and mouse RANKL or Denosumab against human RANKL. We established novel MPA/DMBA-driven mammary carcinogenesis models in TgRANKL mice that express both human and mouse RANKL, as well as in humanized humTgRANKL mice expressing only human RANKL, and compared them to MPA/DMBA-treated wild-type (WT) mice. Our results show that TgRANKL and WT mice have similar levels of susceptibility to mammary carcinogenesis, while OPG-Fc treatment restored mammary ductal density, and prevented ductal branching and the formation of neoplastic foci in both genotypes. humTgRANKL mice also developed MPA/DMBA-induced tumors with similar incidence and burden to those of WT and TgRANKL mice. The prophylactic treatment of humTgRANKL mice with Denosumab significantly prevented the rate of appearance of mammary tumors from 86.7% to 15.4% and the early stages of carcinogenesis, whereas therapeutic treatment did not lead to any significant attenuation of tumor incidence or tumor burden compared to control mice, suggesting the importance of RANKL primarily in the initial stages of tumorigenesis. Overall, we provide unique genetic tools for investigating the involvement of RANKL in breast carcinogenesis, and allow the preclinical evaluation of novel therapeutics that target hormone-related breast cancers.

Targeted Proteomic Analysis of Patients with Ascending Thoracic Aortic Aneurysm.

BACKGROUND: There is a need for clinical markers to aid in the detection of individuals at risk of harboring an ascending thoracic aneurysm (ATAA) or developing one in the future. OBJECTIVES: To our knowledge, ATAA remains without a specific biomarker. This study aims to identify potential biomarkers for ATAA using targeted proteomic analysis. METHODS: In this study, 52 patients were divided into three groups depending on their ascending aorta diameter: 4.0-4.5 cm (N = 23), 4.6-5.0 cm (N = 20), and >5.0 cm (N = 9). A total of 30 controls were in-house populations ethnically matched to cases without known or visible ATAA-related symptoms and with no ATAA familial history. Before the debut of our study, all patients provided medical history and underwent physical examination. Diagnosis was confirmed by echocardiography and angio-computed tomography (CT) scans. Targeted-proteomic analysis was conducted to identify possible biomarkers for the diagnosis of ATAA. RESULTS: A Kruskal-Wallis test revealed that C-C motif chemokine ligand 5 (CCL5), defensin beta 1 (HBD1), intracellular adhesion molecule-1 (ICAM1), interleukin-8 (IL8), tumor necrosis factor alpha (TNFα) and transforming growth factor-beta 1 (TGFB1) expressions are significantly increased in ATAA patients in comparison to control subjects with physiological aorta diameter (p < 0.0001). The receiver-operating characteristic analysis showed that the area under the curve values for CCL5 (0.84), HBD1 (0.83) and ICAM1 (0.83) were superior to that of the other analyzed proteins. CONCLUSIONS: CCL5, HBD1 and ICAM1 are very promising biomarkers with satisfying sensitivity and specificity that could be helpful in stratifying risk for the development of ATAA. These biomarkers may assist in the diagnosis and follow-up of patients at risk of developing ATAA. This retrospective study is very encouraging; however, further in-depth studies may be worthwhile to investigate the role of these biomarkers in the pathogenesis of ATAA.