Gene network-based and ensemble modeling-based selection of tumor-associated antigens with a predicted low risk of tissue damage for targeted immunotherapy.

BACKGROUND: Tumor-associated antigens and their derived peptides constitute an opportunity to design off-the-shelf mainline or adjuvant anti-cancer immunotherapies for a broad array of patients. A performant and rational antigen selection pipeline would lay the foundation for immunotherapy trials with the potential to enhance treatment, tremendously benefiting patients suffering from rare, understudied cancers. METHODS: We present an experimentally validated, data-driven computational pipeline that selects and ranks antigens in a multipronged approach. In addition to minimizing the risk of immune-related adverse events by selecting antigens based on their expression profile in tumor biopsies and healthy tissues, we incorporated a network analysis-derived antigen indispensability index based on computational modeling results, and candidate immunogenicity predictions from a machine learning ensemble model relying on peptide physicochemical characteristics. RESULTS: In a model study of uveal melanoma, Human Leukocyte Antigen (HLA) docking simulations and experimental quantification of the peptide-major histocompatibility complex binding affinities confirmed that our approach discriminates between high-binding and low-binding affinity peptides with a performance similar to that of established methodologies. Blinded validation experiments with autologous T-cells yielded peptide stimulation-induced interferon-γ secretion and cytotoxic activity despite high interdonor variability. Dissecting the score contribution of the tested antigens revealed that peptides with the potential to induce cytotoxicity but unsuitable due to potential tissue damage or instability of expression were properly discarded by the computational pipeline. CONCLUSIONS: In this study, we demonstrate the feasibility of the de novo computational selection of antigens with the capacity to induce an anti-tumor immune response and a predicted low risk of tissue damage. On translation to the clinic, our pipeline supports fast turn-around validation, for example, for adoptive T-cell transfer preparations, in both generalized and personalized antigen-directed immunotherapy settings.

Glaucoma-TrEl: A web-based interactive database to build evidence-based hypotheses on the role of trace elements in glaucoma.

OBJECTIVE: Glaucoma is a chronic neurological disease that is associated with high intraocular pressure (IOP), causes gradual damage to retinal ganglion cells, and often culminates in vision loss. Recent research suggests that glaucoma is a complex multifactorial disease in which multiple interlinked genes and pathways play a role during onset and development. Also, differential availability of trace elements seems to play a role in glaucoma pathophysiology, although their mechanism of action is unknown. The aim of this work is to disseminate a web-based repository on interactions between trace elements and protein-coding genes linked to glaucoma pathophysiology. RESULTS: In this study, we present Glaucoma-TrEl, a web database containing information about interactions between trace elements and protein-coding genes that are linked to glaucoma. In the database, we include interactions between 437 unique genes and eight trace elements. Our analysis found a large number of interactions between trace elements and protein-coding genes mutated or linked to the pathophysiology of glaucoma. We associated genes interacting with multiple trace elements to pathways known to play a role in glaucoma. The web-based platform provides an easy-to-use and interactive tool, which serves as an information hub facilitating future research work on trace elements in glaucoma.

Alzheimer's disease protease-containing plasma extracellular vesicles transfer to the hippocampus via the choroid plexus.

BACKGROUND: Plasma extracellular vesicles (pEV) can harbor a diverse array of factors including active proteases and the amyloid-precursor-protein (APP) cleavage product Aß, involved in plaque formation in Alzheimer`s diseases (AD). A potential role of such vesicles in AD pathology is unexplored. METHODS: In a case-control study of randomly selected patients with AD and other neurological diseases (n = 14), and healthy controls (n = 7), we systematically analyzed the content of pEV, using different assay systems. In addition, we determined their entry path into brain tissue, employing animal (mice) injection experiments with ex vivo generated EV that were similar to AD-pEV, followed by multi antigen analysis (MAA) of brain tissue (n = 4 per condition). The results were compared with an IHC staining of human brain tissue in a small cohort of AD patients (n = 3) and controls with no neurodegenerative diseases (n = 3). FINDINGS: We show that pEV levels are considerably upregulated in AD patients. Besides numerous inflammatory effectors, AD-pEV contained α-, ß- and γ-secretases, able to cleave APP in in target cells. In vitro generated EV with similar characteristics as AD-pEV accumulated in the choroid plexus (CP) of injected animals and reached primarily hippocampal neurons. Corroborating findings were made in human brain samples. An inhibitor of hyaluronic-acid-synthetase (HAS) blocked uploading of proteases and Hyaluronan onto EV in vitro and abolished CP targeting in animal injection experiments. INTERPRETATION: We conclude that protease-containing pEV could be part of a communication axis between the periphery and the brain that could be become detrimental depending on pEV concentration and duration of target cell impact. FUNDING: See the Acknowledgements section.

Plasma-derived extracellular vesicles discriminate type-1 allergy subjects from non-allergic controls.

BACKGROUND: Allergies are on the rise globally, with an enormous impact on affected individuals' quality of life as well as health care resources. They cause a wide range of symptoms, from slightly inconvenient to potentially fatal immune reactions. While allergies have been described and classified phenomenologically, there is an unmet need for easily accessible biomarkers to stratify the severity of clinical symptoms. Furthermore, biomarkers marking the success of specific immunotherapy are urgently needed. OBJECTIVES: Plasma extracellular vesicles (pEV) play a role in coordinating the immune response and may be useful future biomarkers. A pilot study on differences in pEV content was carried out between patients with type I allergy, suffering from rhinoconjunctivitis with or without asthma, and voluntary non-allergic donors. METHODS: We examined pEV from 38 individuals (22 patients with allergies and 16 controls) for 38 chemokines, cytokines, and soluble factors using high-throughput data mining approaches. RESULTS: Patients with allergies had a distinct biomarker pattern, with 7 upregulated (TNF-alpha, IL-4, IL-5, IL-6, IL-17F, CCL2, and CCL17) and 3 downregulated immune mediators (IL-11, IL-27, and CCL20) in pEV compared to controls. This reduced set of 10 factors was able to discriminate controls and allergic patients better than the total array. CONCLUSIONS: The content of pEV showed potential as a target for biomarker research in allergies. Plasma EV, which are readily measurable via blood test, may come to play an important role in allergy diagnosis. In this proof-of-principle study, it could be shown that pEV's discriminate patients with allergies from controls. Further studies investigating whether the content of pEVs may predict the severity of allergic symptoms or even the induction of tolerance to allergens are needed.

IL-33-induced metabolic reprogramming controls the differentiation of alternatively activated macrophages and the resolution of inflammation.

Alternatively activated macrophages (AAMs) contribute to the resolution of inflammation and tissue repair. However, molecular pathways that govern their differentiation have remained incompletely understood. Here, we show that uncoupling protein-2-mediated mitochondrial reprogramming and the transcription factor GATA3 specifically controlled the differentiation of pro-resolving AAMs in response to the alarmin IL-33. In macrophages, IL-33 sequentially triggered early expression of pro-inflammatory genes and subsequent differentiation into AAMs. Global analysis of underlying signaling events revealed that IL-33 induced a rapid metabolic rewiring of macrophages that involved uncoupling of the respiratory chain and increased production of the metabolite itaconate, which subsequently triggered a GATA3-mediated AAM polarization. Conditional deletion of GATA3 in mononuclear phagocytes accordingly abrogated IL-33-induced differentiation of AAMs and tissue repair upon muscle injury. Our data thus identify an IL-4-independent and GATA3-dependent pathway in mononuclear phagocytes that results from mitochondrial rewiring and controls macrophage plasticity and the resolution of inflammation.

ß2-microglobulin triggers NLRP3 inflammasome activation in tumor-associated macrophages to promote multiple myeloma progression.

As substantial constituents of the multiple myeloma (MM) microenvironment, pro-inflammatory macrophages have emerged as key promoters of disease progression, bone destruction, and immune impairment. We identify beta-2-microglobulin (ß2m) as a driver in initiating inflammation in myeloma-associated macrophages (MAMs). Lysosomal accumulation of phagocytosed ß2m promotes ß2m amyloid aggregation in MAMs, resulting in lysosomal rupture and ultimately production of active interleukin-1ß (IL-1ß) and IL-18. This process depends on activation of the NLRP3 inflammasome after ß2m accumulation, as macrophages from NLRP3-deficient mice lack efficient ß2m-induced IL-1ß production. Moreover, depletion or silencing of ß2m in MM cells abrogates inflammasome activation in a murine MM model. Finally, we demonstrate that disruption of NLRP3 or IL-18 diminishes tumor growth and osteolytic bone destruction normally promoted by ß2m-induced inflammasome signaling. Our results provide mechanistic evidence for ß2m's role as an NLRP3 inflammasome activator during MM pathogenesis. Moreover, inhibition of NLRP3 represents a potential therapeutic approach in MM.

The IKZF1-IRF4/IRF5 Axis Controls Polarization of Myeloma-Associated Macrophages.

The bone marrow niche has a pivotal role in progression, survival, and drug resistance of multiple myeloma cells. Therefore, it is important to develop means for targeting the multiple myeloma bone marrow microenvironment. Myeloma-associated macrophages (MAM) in the bone marrow niche are M2 like. They provide nurturing signals to multiple myeloma cells and promote immune escape. Reprogramming M2-like macrophages toward a tumoricidal M1 phenotype represents an intriguing therapeutic strategy. This is especially interesting in view of the successful use of mAbs against multiple myeloma cells, as these therapies hold the potential to trigger macrophage-mediated phagocytosis and cytotoxicity. In this study, we observed that MAMs derived from patients treated with the immunomodulatory drug (IMiD) lenalidomide skewed phenotypically and functionally toward an M1 phenotype. Lenalidomide is known to exert its beneficial effects by modulating the CRBN-CRL4 E3 ligase to ubiquitinate and degrade the transcription factor IKAROS family zinc finger 1 (IKZF1). In M2-like MAMs, we observed enhanced IKZF1 levels that vanished through treatment with lenalidomide, yielding MAMs with a bioenergetic profile, T-cell stimulatory properties, and loss of tumor-promoting capabilities that resemble M1 cells. We also provide evidence that IMiDs interfere epigenetically, via degradation of IKZF1, with IFN regulatory factors 4 and 5, which in turn alters the balance of M1/M2 polarization. We validated our observations in vivo using the CrbnI391V mouse model that recapitulates the IMiD-triggered IKZF1 degradation. These data show a role for IKZF1 in macrophage polarization and can provide explanations for the clinical benefits observed when combining IMiDs with therapeutic antibodies.See related Spotlight on p. 254.

Mathematical Modelling in Biomedicine: A Primer for the Curious and the Skeptic.

In most disciplines of natural sciences and engineering, mathematical and computational modelling are mainstay methods which are usefulness beyond doubt. These disciplines would not have reached today's level of sophistication without an intensive use of mathematical and computational models together with quantitative data. This approach has not been followed in much of molecular biology and biomedicine, however, where qualitative descriptions are accepted as a satisfactory replacement for mathematical rigor and the use of computational models is seen by many as a fringe practice rather than as a powerful scientific method. This position disregards mathematical thinking as having contributed key discoveries in biology for more than a century, e.g., in the connection between genes, inheritance, and evolution or in the mechanisms of enzymatic catalysis. Here, we discuss the role of computational modelling in the arsenal of modern scientific methods in biomedicine. We list frequent misconceptions about mathematical modelling found among biomedical experimentalists and suggest some good practices that can help bridge the cognitive gap between modelers and experimental researchers in biomedicine. This manuscript was written with two readers in mind. Firstly, it is intended for mathematical modelers with a background in physics, mathematics, or engineering who want to jump into biomedicine. We provide them with ideas to motivate the use of mathematical modelling when discussing with experimental partners. Secondly, this is a text for biomedical researchers intrigued with utilizing mathematical modelling to investigate the pathophysiology of human diseases to improve their diagnostics and treatment.

Network- and systems-based re-engineering of dendritic cells with non-coding RNAs for cancer immunotherapy.

Dendritic cells (DCs) are professional antigen-presenting cells that induce and regulate adaptive immunity by presenting antigens to T cells. Due to their coordinative role in adaptive immune responses, DCs have been used as cell-based therapeutic vaccination against cancer. The capacity of DCs to induce a therapeutic immune response can be enhanced by re-wiring of cellular signalling pathways with microRNAs (miRNAs). Methods: Since the activation and maturation of DCs is controlled by an interconnected signalling network, we deploy an approach that combines RNA sequencing data and systems biology methods to delineate miRNA-based strategies that enhance DC-elicited immune responses. Results: Through RNA sequencing of IKKß-matured DCs that are currently being tested in a clinical trial on therapeutic anti-cancer vaccination, we identified 44 differentially expressed miRNAs. According to a network analysis, most of these miRNAs regulate targets that are linked to immune pathways, such as cytokine and interleukin signalling. We employed a network topology-oriented scoring model to rank the miRNAs, analysed their impact on immunogenic potency of DCs, and identified dozens of promising miRNA candidates, with miR-15a and miR-16 as the top ones. The results of our analysis are presented in a database that constitutes a tool to identify DC-relevant miRNA-gene interactions with therapeutic potential (https://www.synmirapy.net/dc-optimization). Conclusions: Our approach enables the systematic analysis and identification of functional miRNA-gene interactions that can be experimentally tested for improving DC immunogenic potency.

Evidence for liver and peripheral immune cells secreting tumor-suppressive extracellular vesicles in melanoma patients.

BACKGROUND: Before and after surgery melanoma patients harbor elevated levels of extracellular vesicles in plasma (pEV), suppressing tumor cell activity. However, due to technical reasons and lack of cell-specific biomarkers, their cellular origin remains obscure. METHODS: We mimicked the interaction of tumor cells with liver cells and PBMC in vitro, and compared newly secreted EV-associated miRNAs and protein factors with those detected in melanoma patient`s pEV. FINDINGS: Our results suggest that pEV from melanoma patients are secreted in part by residual or relapsing tumor cells, but also by liver and peripheral blood mononuclear cells (PBMC). Our approach identified factors that were seemingly associated either with tumor cell activity, or the counteracting immune system, including liver cells. Notably, the presence/absence of these factors correlated with the clinical stage and tumor relapse. INTERPRETATION: Our study may provide new insights into the innate immune defense against tumor cells and implies that residual tumor cells could be more active than previously thought. In addition we provide some preliminary evidence that pEV marker patterns could be used to predict cancer relapse.

An integrative network-driven pipeline for systematic identification of lncRNA-associated regulatory network motifs in metastatic melanoma.

BACKGROUND: Melanoma phenotype and the dynamics underlying its progression are determined by a complex interplay between different types of regulatory molecules. In particular, transcription factors (TFs), microRNAs (miRNAs), and long non-coding RNAs (lncRNAs) interact in layers that coalesce into large molecular interaction networks. Our goal here is to study molecules associated with the cross-talk between various network layers, and their impact on tumor progression. RESULTS: To elucidate their contribution to disease, we developed an integrative computational pipeline to construct and analyze a melanoma network focusing on lncRNAs, their miRNA and protein targets, miRNA target genes, and TFs regulating miRNAs. In the network, we identified three-node regulatory loops each composed of lncRNA, miRNA, and TF. To prioritize these motifs for their role in melanoma progression, we integrated patient-derived RNAseq dataset from TCGA (SKCM) melanoma cohort, using a weighted multi-objective function. We investigated the expression profile of the top-ranked motifs and used them to classify patients into metastatic and non-metastatic phenotypes. CONCLUSIONS: The results of this study showed that network motif UCA1/AKT1/hsa-miR-125b-1 has the highest prediction accuracy (ACC = 0.88) for discriminating metastatic and non-metastatic melanoma phenotypes. The observation is also confirmed by the progression-free survival analysis where the patient group characterized by the metastatic-type expression profile of the motif suffers a significant reduction in survival. The finding suggests a prognostic value of network motifs for the classification and treatment of melanoma.

Locally renewing resident synovial macrophages provide a protective barrier for the joint.

Macrophages are considered to contribute to chronic inflammatory diseases such as rheumatoid arthritis1. However, both the exact origin and the role of macrophages in inflammatory joint disease remain unclear. Here we use fate-mapping approaches in conjunction with three-dimensional light-sheet fluorescence microscopy and single-cell RNA sequencing to perform a comprehensive spatiotemporal analysis of the composition, origin and differentiation of subsets of macrophages within healthy and inflamed joints, and study the roles of these macrophages during arthritis. We find that dynamic membrane-like structures, consisting of a distinct population of CX3CR1+ tissue-resident macrophages, form an internal immunological barrier at the synovial lining and physically seclude the joint. These barrier-forming macrophages display features that are otherwise typical of epithelial cells, and maintain their numbers through a pool of locally proliferating CX3CR1- mononuclear cells that are embedded into the synovial tissue. Unlike recruited monocyte-derived macrophages, which actively contribute to joint inflammation, these epithelial-like CX3CR1+ lining macrophages restrict the inflammatory reaction by providing a tight-junction-mediated shield for intra-articular structures. Our data reveal an unexpected functional diversification among synovial macrophages and have important implications for the general role of macrophages in health and disease.

Curatopes Melanoma: A Database of Predicted T-cell Epitopes from Overly Expressed Proteins in Metastatic Cutaneous Melanoma.

Therapeutic anticancer vaccination has been adapted as an immunotherapy in several solid tumors. However, the selection of promising candidates from the total quantity of possible epitopes poses a challenge to clinicians and bioinformaticians alike, and very few epitopes have been tested in experimental or clinical settings to validate their efficacy. Here, we present a comprehensive database of predicted nonmutated peptide epitopes derived from genes that are overly expressed in a group of 32 melanoma biopsies compared with healthy tissues and that were filtered against expression in a curated list of survival-critical tissues. We hypothesize that these "self-tolerant" epitopes have two desirable properties: they do not depend on mutations, being immediately applicable to a large patient collective, and they potentially cause fewer autoimmune reactions. To support epitope selection, we provide an aggregated score of expected therapeutic efficiency as a shortlist mechanism. The database has applications in facilitating epitope selection and trial design and is freely accessible at https://www.curatopes.com. SIGNIFICANCE: A database is presented that predicts and scores antitumor T-cell epitopes, with a focus on tolerability and avoidance of severe autoimmunity, offering a supplementary epitope set for further investigation in immunotherapy.

Systems biology-based investigation of cooperating microRNAs as monotherapy or adjuvant therapy in cancer.

MicroRNAs (miRNAs) are short, noncoding RNAs that regulate gene expression by suppressing mRNA translation and reducing mRNA stability. A miRNA can potentially bind many mRNAs, thereby affecting the expression of oncogenes and tumor suppressor genes as well as the activity of whole pathways. The promise of miRNA therapeutics in cancer is to harness this evolutionarily conserved mechanism for the coordinated regulation of gene expression, and thus restoring a normal cell phenotype. However, the promiscuous binding of miRNAs can provoke unwanted off-target effects, which are usually caused by high-dose single-miRNA treatments. Thus, it is desirable to develop miRNA therapeutics with increased specificity and efficacy. To achieve that, we propose the concept of miRNA cooperativity in order to exert synergistic repression on target genes, thus lowering the required total amount of miRNAs. We first review miRNA therapies in clinical application. Next, we summarize the knowledge on the molecular mechanism and biological function of miRNA cooperativity and discuss its application in cancer therapies. We then propose and discuss a systems biology approach to investigate miRNA cooperativity for the clinical setting. Altogether, we point out the potential of miRNA cooperativity to reduce off-target effects and to complement conventional, targeted, or immune-based therapies for cancer.

Transcription factor Fra-1 targets arginase-1 to enhance macrophage-mediated inflammation in arthritis.

The polarization of macrophages is regulated by transcription factors such as nuclear factor kappa B (NF-κB) and activator protein 1 (AP-1). In this manuscript, we delineated the role of the transcription factor Fos-related antigen 1 (Fra-1) during macrophage activation and development of arthritis. Network level interaction analysis of microarray data derived from Fra-1- or Fra-2-deficient macrophages revealed a central role of Fra-1, but not of Fra-2 in orchestrating the expression of genes related to wound response, toll-like receptor activation and interleukin signaling. Chromatin-immunoprecipitation (ChIP)-sequencing and standard ChIP analyses of macrophages identified arginase 1 (Arg1) as a target of Fra-1. Luciferase reporter assays revealed that Fra-1 down-regulated Arg1 expression by direct binding to the promoter region. Using macrophage-specific Fra-1- or Fra-2- deficient mice, we observed an enhanced expression and activity of Arg1 and a reduction of arthritis in the absence of Fra-1, but not of Fra-2. This phenotype was reversed by treatment with the arginase inhibitor Nω-hydroxy-nor-L-arginine, while ʟ-arginine supplementation increased arginase activity and alleviated arthritis, supporting the notion that reduced arthritis in macrophage-specific Fra-1-deficient mice resulted from enhanced Arg1 expression and activity. Moreover, patients with active RA showed increased Fra-1 expression in the peripheral blood and elevated Fra-1 protein in synovial macrophages compared to RA patients in remission. In addition, the Fra-1/ARG1 ratio in synovial macrophages was related to RA disease activity. In conclusion, these data suggest that Fra-1 orchestrates the inflammatory state of macrophages by inhibition of Arg1 expression and thereby impedes the resolution of inflammation.

Innate extracellular vesicles from melanoma patients suppress ß-catenin in tumor cells by miRNA-34a.

Upon tumor development, new extracellular vesicles appear in circulation. Our knowledge of their relative abundance, function, and overall impact on cancer development is still preliminary. Here, we demonstrate that plasma extracellular vesicles (pEVs) of non-tumor origin are persistently increased in untreated and post-excision melanoma patients, exhibiting strong suppressive effects on the proliferation of tumor cells. Plasma vesicle numbers, miRNAs, and protein levels were elevated two- to tenfold and detected many years after tumor resection. The vesicles revealed individual and clinical stage-specific miRNA profiles as well as active ADAM10. However, whereas pEV from patients preventing tumor relapse down-regulated ß-catenin and blocked tumor cell proliferation in an miR-34a-dependent manner, pEV from metastatic patients lost this ability and stimulated ß-catenin-mediated transcription. Cancer-induced pEV may constitute an innate immune mechanism suppressing tumor cell activity including that of residual cancer cells present after primary surgery.

A gene regulatory architecture that controls region-independent dynamics of oligodendrocyte differentiation.

Oligodendrocytes (OLs) facilitate information processing in the vertebrate central nervous system via axonal ensheathment. The structure and dynamics of the regulatory network that mediates oligodendrogenesis are poorly understood. We employed bioinformatics and meta-analysis of high-throughput datasets to reconstruct a regulatory network underpinning OL differentiation. From this network, we identified families of feedforward loops comprising the transcription factors (TFs) Olig2, Sox10, and Tcf7l2 and their targets. Among the targets, we found eight other TFs related to OL differentiation, suggesting a hierarchical architecture in which some TFs (Olig2, Sox10, and Tcf7l2) regulate via feedforward loops the expression of others (Sox2, Sox6, Sox11, Nkx2-2, Nkx6-2, Hes5, Myt1, and Myrf). Model simulations with a kinetic model reproduced the mechanisms of OL differentiation only when in the model, Sox10-mediated repression of Tcf7l2 by miR-338/miR-155 was introduced, a prediction confirmed in genetic functional experiments. Additional model simulations suggested that OLs from dorsal regions emerge through BMP/Sox9 signaling.

Bacterial Adherence and Dwelling Probability: Two Drivers of Early Alveolar Infection by Streptococcus pneumoniae Identified in Multi-Level Mathematical Modeling.

Pneumococcal infection is the most frequent cause of pneumonia, and one of the most prevalent diseases worldwide. The population groups at high risk of death from bacterial pneumonia are infants, elderly and immunosuppressed people. These groups are more vulnerable because they have immature or impaired immune systems, the efficacy of their response to vaccines is lower, and antibiotic treatment often does not take place until the inflammatory response triggered is already overwhelming. The immune response to bacterial lung infections involves dynamic interactions between several types of cells whose activation is driven by intracellular molecular networks. A feasible approach to the integration of knowledge and data linking tissue, cellular and intracellular events and the construction of hypotheses in this area is the use of mathematical modeling. For this paper, we used a multi-level computational model to analyse the role of cellular and molecular interactions during the first 10 h after alveolar invasion of Streptococcus pneumoniae bacteria. By "multi-level" we mean that we simulated the interplay between different temporal and spatial scales in a single computational model. In this instance, we included the intracellular scale of processes driving lung epithelial cell activation together with the scale of cell-to-cell interactions at the alveolar tissue. In our analysis, we combined systematic model simulations with logistic regression analysis and decision trees to find genotypic-phenotypic signatures that explain differences in bacteria strain infectivity. According to our simulations, pneumococci benefit from a high dwelling probability and a high proliferation rate during the first stages of infection. In addition to this, the model predicts that during the very early phases of infection the bacterial capsule could be an impediment to the establishment of the alveolar infection because it impairs bacterial colonization.

A web platform for the network analysis of high-throughput data in melanoma and its use to investigate mechanisms of resistance to anti-PD1 immunotherapy.

Cellular phenotypes are established and controlled by complex and precisely orchestrated molecular networks. In cancer, mutations and dysregulations of multiple molecular factors perturb the regulation of these networks and lead to malignant transformation. High-throughput technologies are a valuable source of information to establish the complex molecular relationships behind the emergence of malignancy, but full exploitation of this massive amount of data requires bioinformatics tools that rely on network-based analyses. In this report we present the Virtual Melanoma Cell, an online tool developed to facilitate the mining and interpretation of high-throughput data on melanoma by biomedical researches. The platform is based on a comprehensive, manually generated and expert-validated regulatory map composed of signaling pathways important in malignant melanoma. The Virtual Melanoma Cell is a tool designed to accept, visualize and analyze user-generated datasets. It is available at: https://www.vcells.net/melanoma. To illustrate the utilization of the web platform and the regulatory map, we have analyzed a large publicly available dataset accounting for anti-PD1 immunotherapy treatment of malignant melanoma patients.

An Interactive Macrophage Signal Transduction Map Facilitates Comparative Analyses of High-Throughput Data.

Macrophages (Mϕs) are key players in the coordination of the lifesaving or detrimental immune response against infections. The mechanistic understanding of the functional modulation of Mϕs by pathogens and pharmaceutical interventions at the signal transduction level is still far from complete. The complexity of pathways and their cross-talk benefits from holistic computational approaches. In the present study, we reconstructed a comprehensive, validated, and annotated map of signal transduction pathways in inflammatory Mϕs based on the current literature. In a second step, we selectively expanded this curated map with database knowledge. We provide both versions to the scientific community via a Web platform that is designed to facilitate exploration and analysis of high-throughput data. The platform comes preloaded with logarithmic fold changes from 44 data sets on Mϕ stimulation. We exploited three of these data sets-human primary Mϕs infected with the common lung pathogens Streptococcus pneumoniae, Legionella pneumophila, or Mycobacterium tuberculosis-in a case study to show how our map can be customized with expression data to pinpoint regulated subnetworks and druggable molecules. From the three infection scenarios, we extracted a regulatory core of 41 factors, including TNF, CCL5, CXCL10, IL-18, and IL-12 p40, and identified 140 drugs targeting 16 of them. Our approach promotes a comprehensive systems biology strategy for the exploitation of high-throughput data in the context of Mϕ signal transduction. In conclusion, we provide a set of tools to help scientists unravel details of Mϕ signaling. The interactive version of our Mϕ signal transduction map is accessible online at https://vcells.net/macrophage.