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.

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.

Melanoma 2.0. Skin cancer as a paradigm for emerging diagnostic technologies, computational modelling and artificial intelligence.

We live in an unprecedented time in oncology. We have accumulated samples and cases in cohorts larger and more complex than ever before. New technologies are available for quantifying solid or liquid samples at the molecular level. At the same time, we are now equipped with the computational power necessary to handle this enormous amount of quantitative data. Computational models are widely used helping us to substantiate and interpret data. Under the label of systems and precision medicine, we are putting all these developments together to improve and personalize the therapy of cancer. In this review, we use melanoma as a paradigm to present the successful application of these technologies but also to discuss possible future developments in patient care linked to them. Melanoma is a paradigmatic case for disruptive improvements in therapies, with a considerable number of metastatic melanoma patients benefiting from novel therapies. Nevertheless, a large proportion of patients does not respond to therapy or suffers from adverse events. Melanoma is an ideal case study to deploy advanced technologies not only due to the medical need but also to some intrinsic features of melanoma as a disease and the skin as an organ. From the perspective of data acquisition, the skin is the ideal organ due to its accessibility and suitability for many kinds of advanced imaging techniques. We put special emphasis on the necessity of computational strategies to integrate multiple sources of quantitative data describing the tumour at different scales and levels.

Blockade of the CD47/SIRPα checkpoint axis potentiates the macrophage-mediated anti-tumor efficacy of tafasitamab.

Macrophages are one of the key mediators of the therapeutic effects exerted by monoclonal antibodies, such as the anti-CD19 antibody tafasitamab, approved in combination with lenalidomide for the treatment of relapsed or refractory (r/r) diffuse large B cell lymphoma (DLBCL). However, antibody-dependent cellular phagocytosis (ADCP) in the tumor microenvironment can be counteracted by increased expression of the inhibitory receptor SIRPα on macrophages and its ligand, the immune checkpoint molecule CD47 on tumor cells. The aim of this study was to investigate the impact of the CD47-SIRPα axis on tafasitamabmediated phagocytosis and explore the potential of anti-CD47 blockade to enhance its antitumor activity. Elevated expression of both SIRPα and CD47 was observed in DLBCL patient-derived lymph node biopsies compared to healthy controls. CRISPR-mediated CD47 overexpression impacted tafasitamab-mediated ADCP in vitro and increased expression of SIRPα on macrophages correlated with decreased ADCP activity of tafasitamab against DLBCL cell lines. Combination of tafasitamab and an anti-CD47 blocking antibody enhanced ADCP activity of in vitro generated macrophages. Importantly, tafasitamab-mediated phagocytosis was elevated in combination with CD47 blockade using primary DLBCL cells and patient-derived lymphoma-associated macrophages (LAMs) in an autologous setting. Furthermore, lymphoma cells with low CD19 expression were efficiently eliminated by the combination treatment. Finally, combined treatment of tafasitamab and an anti-CD47 antibody resulted in enhanced tumor volume reduction and survival benefit in lymphoma xenograft mouse models. These findings provide evidence that CD47 blockade can enhance the phagocytic potential of tumor targeting immunotherapies such as tafasitamab and suggest there is value in exploring the combination in the clinic.

Identifying biomarkers and novel therapeutic targets in uveal melanoma.

Uveal melanoma (UM) is an orphan cancer despite being the most common eye tumor in adults. Patients often present to skin cancer centers for treatment of metastatic disease although there are significant genetic, biological, and clinical differences from cutaneous melanoma. The treatments most commonly used for metastatic UM are tebentafusp and combined immune checkpoint blockade, both of which yield low response rates and may be accompanied by high treatment costs and significant immune-related toxicities. Thus, it is of paramount importance to identify biomarkers and clinical profiles predictive of treatment response and to find novel therapeutic targets. The use of immune checkpoint blockade showed more favorable outcomes in patients with extrahepatic disease and normal levels of serum lactate dehydrogenase in a panel of retrospective studies, making its use more reasonable in this subgroup. To identify novel drug targets, we will analyze the expression and relevance of neural crest transcription factors in patient bio-specimens using next-generation nanopore sequencing. Computer algorithms and network-based analysis will facilitate the identification of druggable targets which will subsequently be validated in patient-derived short-term cell cultures. This approach will help to find novel and personalized treatments for UM.

A disease network-based deep learning approach for characterizing melanoma.

Multiple types of genomic variations are present in cutaneous melanoma and some of the genomic features may have an impact on the prognosis of the disease. The access to genomics data via public repositories such as The Cancer Genome Atlas (TCGA) allows for a better understanding of melanoma at the molecular level, therefore making characterization of substantial heterogeneity in melanoma patients possible. Here, we proposed an approach that integrates genomics data, a disease network, and a deep learning model to classify melanoma patients for prognosis, assess the impact of genomic features on the classification and provide interpretation to the impactful features. We integrated genomics data into a melanoma network and applied an autoencoder model to identify subgroups in TCGA melanoma patients. The model utilizes communities identified in the network to effectively reduce the dimensionality of genomics data into a patient score profile. Based on the score profile, we identified three patient subtypes that show different survival times. Furthermore, we quantified and ranked the impact of genomic features on the patient score profile using a machine-learning technique. Follow-up analysis of the top-ranking features provided us with the biological interpretation of them at both pathway and molecular levels, such as their mutation and interactome profiles in melanoma and their involvement in pathways associated with signaling transduction, immune system and cell cycle. Taken together, we demonstrated the ability of the approach to identify disease subgroups using a deep learning model that captures the most relevant information of genomics data in the melanoma network.

Clinical determinants of long-term survival in metastatic uveal melanoma.

This study aimed to identify prognostic factors in patients with metastatic uveal melanoma (UM) that were associated with long-term survival in a real-world setting. A total of 94 patients with metastatic UM were included from German skin cancer centers and the German national skin cancer registry (ADOReg). Data were analyzed for the response to treatment, progression-free survival, and overall survival (OS). Prognostic factors were explored with univariate Cox regression, log-rank, and χ2-tests. Identified factors were subsequently validated after the population was divided into two cohorts of short-term survival (< 2 years OS, cohort A, n = 50) and long-term survival (> 2 years OS, cohort B, n = 44). A poor ECOG performance status (hazard ratio [HR] 2.0, 95% confidence interval [CI] 1.0-3.9) and elevated serum LDH (HR 2.0, 95% CI 1.0-3.8) were associated with a poor OS, whereas a good response to immune checkpoint blockade (ICB, p < 0.001), radiation therapy (p < 0.001), or liver-directed treatments (p = 0.01) were associated with a prolonged OS. Long-term survivors (cohort B) showed a higher median number of organs affected by metastasis (p < 0.001), while patients with liver metastases only were more common in cohort A (40% vs. 9%; p = 0.002). A partial response to ICB was observed in 16% (12/73), being 21% (8/38) for combined ICB, 17% (1/6) for single CTLA4 inhibition, and 10% (3/29) for single PD1 inhibition. One complete response occurred in cohort B with combined ICB. We conclude that the response to ICB and the presence of extrahepatic disease were favorable prognostic factors for long-term survival.

Mycobacterial Cord Factor Reprograms the Macrophage Response to IFN-γ towards Enhanced Inflammation yet Impaired Antigen Presentation and Expression of GBP1.

Mycobacteria survive in macrophages despite triggering pattern recognition receptors and T cell-derived IFN-γ production. Mycobacterial cord factor trehalose-6,6-dimycolate (TDM) binds the C-type lectin receptor MINCLE and induces inflammatory gene expression. However, the impact of TDM on IFN-γ-induced macrophage activation is not known. In this study, we have investigated the cross-regulation of the mouse macrophage transcriptome by IFN-γ and by TDM or its synthetic analogue trehalose-6,6-dibehenate (TDB). As expected, IFN-γ induced genes involved in Ag presentation and antimicrobial defense. Transcriptional programs induced by TDM and TDB were highly similar but clearly distinct from the response to IFN-γ. The glycolipids enhanced expression of a subset of IFN-γ-induced genes associated with inflammation. In contrast, TDM/TDB exerted delayed inhibition of IFN-γ-induced genes, including pattern recognition receptors, MHC class II genes, and IFN-γ-induced GTPases, with antimicrobial function. TDM downregulated MHC class II cell surface expression and impaired T cell activation by peptide-pulsed macrophages. Inhibition of the IFN-γ-induced GTPase GBP1 occurred at the level of transcription by a partially MINCLE-dependent mechanism that may target IRF1 activity. Although activation of STAT1 was unaltered, deletion of Socs1 relieved inhibition of GBP1 expression by TDM. Nonnuclear Socs1 was sufficient for inhibition, suggesting a noncanonical, cytoplasmic mechanism. Taken together, unbiased analysis of transcriptional reprogramming revealed a significant degree of negative regulation of IFN-γ-induced Ag presentation and antimicrobial gene expression by the mycobacterial cord factor that may contribute to mycobacterial persistence.

The Role of MicroRNAs in Cancer Biology and Therapy from a Systems Biology Perspective.

Since the discovery of microRNAs (miRNAs) in Caenorhabditis elegans, our understanding of their cellular function has progressed continuously. Today, we have a good understanding of miRNA-mediated gene regulation, miRNA-mediated cross talk between genes including competing endogenous RNAs, and miRNA-mediated signaling transduction both in normal human physiology and in diseases.Besides, these noncoding RNAs have shown their value for clinical applications, especially in an oncological context. They can be used as reliable biomarkers for cancer diagnosis and prognosis and attract increasing attention as potential therapeutic targets. Many achievements made in the miRNA field are based on joint efforts from computational and molecular biologists. Systems biology approaches, which integrate computational and experimental methods, have played a fundamental role in uncovering the cellular functions of miRNAs.In this chapter, we review and discuss the role of miRNAs in oncology from a system biology perspective. We first describe biological facts about miRNA genetics and function. Next, we discuss the role of miRNAs in cancer progression and review the application of miRNAs in cancer diagnostics and therapy. Finally, we elaborate on the role that miRNAs play in cancer gene regulatory networks. Taken together, we emphasize the importance of systems biology approaches in our continued efforts to study miRNA cancer regulation.

Community effort endorsing multiscale modelling, multiscale data science and multiscale computing for systems medicine.

Systems medicine holds many promises, but has so far provided only a limited number of proofs of principle. To address this road block, possible barriers and challenges of translating systems medicine into clinical practice need to be identified and addressed. The members of the European Cooperation in Science and Technology (COST) Action CA15120 Open Multiscale Systems Medicine (OpenMultiMed) wish to engage the scientific community of systems medicine and multiscale modelling, data science and computing, to provide their feedback in a structured manner. This will result in follow-up white papers and open access resources to accelerate the clinical translation of systems medicine.

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.

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.

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.

Heterogeneous fate choice of genetically modulated adult neural stem cells in gray and white matter of the central nervous system.

Apart from dedicated oligodendroglial progenitor cells, adult neural stem cells (aNSCs) can also give rise to new oligodendrocytes in the adult central nervous system (CNS). This process mainly confers myelinating glial cell replacement in pathological situations and can hence contribute to glial heterogeneity. Our previous studies demonstrated that the p57kip2 gene encodes an intrinsic regulator of glial fate acquisition and we here investigated to what degree its modulation can affect stem cell-dependent oligodendrogenesis in different CNS environments. We therefore transplanted p57kip2 knockdown aNSCs into white and gray matter (WM and GM) regions of the mouse brain, into uninjured spinal cords as well as in the vicinity of spinal cord injuries and evaluated integration and differentiation in vivo. Our experiments revealed that under healthy conditions intrinsic suppression of p57kip2 as well as WM localization promote differentiation toward myelinating oligodendrocytes at the expense of astrocyte generation. Moreover, p57kip2 knockdown conferred a strong benefit on cell survival augmenting net oligodendrocyte generation. In the vicinity of hemisectioned spinal cords, the gene knockdown led to a similar induction of oligodendroglial features; however, newly generated oligodendrocytes appeared to suffer more from the hostile environment. This study contributes to our understanding of mechanisms of adult oligodendrogenesis and glial heterogeneity and further reveals critical factors when considering aNSC mediated cell replacement in injury and disease.

Exosomal lncRNAs and cancer: connecting the missing links.

Motivation: Extracellular vesicles (EVs), including exosomes and microvesicles, are potent and clinically valuable tools for early diagnosis, prognosis and potentially the targeted treatment of cancer. The content of EVs is closely related to the type and status of the EV-secreting cell. Circulating exosomes are a source of stable RNAs including mRNAs, microRNAs and long non-coding RNAs (lncRNAs). Results: This review outlines the links between EVs, lncRNAs and cancer. We highlight communication networks involving the tumor microenvironment, the immune system and metastasis. We show examples supporting the value of exosomal lncRNAs as cancer biomarkers and therapeutic targets. We demonstrate how a system biology approach can be used to model cell-cell communication via exosomal lncRNAs and to simulate effects of therapeutic interventions. In addition, we introduce algorithms and bioinformatics resources for the discovery of tumor-specific lncRNAs and tools that are applied to determine exosome content and lncRNA function. Finally, this review provides a comprehensive collection and guide to databases for exosomal lncRNAs. Supplementary information: Supplementary data are available at Bioinformatics online.

The role of cooperativity in a p53-miR34 dynamical mathematical model.

The objective of this study is to evaluate the role of cooperativity, captured by the Hill coefficient, in a minimal mathematical model describing the interactions between p53 and miR-34a. The model equations are analyzed for negative, none and normal cooperativity using a specific version of bifurcation theory and they are solved numerically. Special attention is paid to the sign of so-called first Lyapunov value. Interpretations of the results are given, both according to dynamic theory and in biological terms. In terms of cell signaling, we propose the hypothesis that when the outgoing signal of a system spends a physiologically significant amount of time outside of its equilibrium state, then the value of that signal can be sampled at any point along the trajectory towards that equilibrium and indeed, at multiple points. Coupled with non-linear behavior, such as that caused by cooperativity, this feature can account for a complex and varied response, which p53 is known for. From dynamical point of view, we found that when cooperativity is negative, the system has only one stable equilibrium point. In the absence of cooperativity, there is a single unstable equilibrium point with a critical boundary of stability. In the case with normal cooperativity, the system can have one, two, or three steady states with both, bi-stability and bi-instability occurring.

The Role of Age, Neutrophil Infiltration and Antibiotics Timing in the Severity of Streptococcus pneumoniae Pneumonia. Insights from a Multi-Level Mathematical Model Approach.

Bacterial pneumonia is one of the most prevalent infectious diseases and has high mortality in sensitive patients (children, elderly and immunocompromised). Although an infection, the disease alters the alveolar epithelium homeostasis and hinders normal breathing, often with fatal consequences. A special case is hospitalized aged patients, which present a high risk of infection and death because of the community acquired version of the Streptococcus pneumoniae pneumonia. There is evidence that early antibiotics treatment decreases the inflammatory response during pneumonia. Here, we investigate mechanistically this strategy using a multi-level mathematical model, which describes the 24 first hours after infection of a single alveolus from the key signaling networks behind activation of the epithelium to the dynamics of the local immune response. With the model, we simulated pneumonia in aged and young patients subjected to different antibiotics timing. The results show that providing antibiotics to elderly patients 8 h in advance compared to young patients restores in aged individuals the effective response seen in young ones. This result suggests the use of early, probably prophylactic, antibiotics treatment in aged hospitalized people with high risk of pneumonia.

The Role of Immune Checkpoint Blockade in Uveal Melanoma.

Uveal melanoma (UM) represents the most common intraocular malignancy in adults and accounts for about 5% of all melanomas. Primary disease can be effectively controlled by several local therapy options, but UM has a high potential for metastatic spread, especially to the liver. Despite its clinical and genetic heterogeneity, therapy of metastatic UM has largely been adopted from cutaneous melanoma (CM) with discouraging results until now. The introduction of antibodies targeting CTLA-4 and PD-1 for immune checkpoint blockade (ICB) has revolutionized the field of cancer therapy and has achieved pioneering results in metastatic CM. Thus, expectations were high that patients with metastatic UM would also benefit from these new therapy options. This review provides a comprehensive and up-to-date overview on the role of ICB in UM. We give a summary of UM biology, its clinical features, and how it differs from CM. The results of several studies that have been investigating ICB in metastatic UM are presented. We discuss possible reasons for the lack of efficacy of ICB in UM compared to CM, highlight the pitfalls of ICB in this cancer entity, and explain why other immune-modulating therapies could still be an option for future UM therapies.

Emerging functional markers for cancer stem cell-based therapies: Understanding signaling networks for targeting metastasis.

Metastasis is one of the most challenging issues in cancer patient management, and effective therapies to specifically target disease progression are missing, emphasizing the urgent need for developing novel anti-metastatic therapeutics. Cancer stem cells (CSCs) gained fast attention as a minor population of highly malignant cells within liquid and solid tumors that are responsible for tumor onset, self-renewal, resistance to radio- and chemotherapies, and evasion of immune surveillance accelerating recurrence and metastasis. Recent progress in the identification of their phenotypic and molecular characteristics and interactions with the tumor microenvironment provides great potential for the development of CSC-based targeted therapies and radical improvement in metastasis prevention and cancer patient prognosis. Here, we report on newly uncovered signaling mechanisms controlling CSC's aggressiveness and treatment resistance, and CSC-specific agents and molecular therapeutics, some of which are currently under investigation in clinical trials, gearing towards decisive functional CSC intrinsic or surface markers. One special research focus rests upon subverted regulatory pathways such as insulin-like growth factor 1 receptor signaling and its interactors in metastasis-initiating cell populations directly related to the gain of stem cell- and EMT-associated properties, as well as key components of the E2F transcription factor network regulating metastatic progression, microenvironmental changes, and chemoresistance. In addition, the study provides insight into systems biology tools to establish complex molecular relationships behind the emergence of aggressive phenotypes from high-throughput data that rely on network-based analysis and their use to investigate immune escape mechanisms or predict clinical outcome-relevant CSC receptor signaling signatures. We further propose that customized vector technologies could drastically enhance systemic drug delivery to target sites, and summarize recent progress and remaining challenges. This review integrates available knowledge on CSC biology, computational modeling approaches, molecular targeting strategies, and delivery techniques to envision future clinical therapies designed to conquer metastasis-initiating cells.