Mathematical modeling of Interleukin-35 promoting tumor growth and angiogenesis

A mixture-like model for tumor-immune system interactions

We introduce a mathematical model based on mixture theory intended to describe the tumor-immune system interactions within the tumor microenvironment. The equations account for the geometry of the tumor expansion, and the displacement of the immune cells, driven by diffusion and chemotactic mechanisms. They also take into account the constraints in terms of nutrient and oxygen supply. The numerical investigations analyze the impact of the different modeling assumptions and parameters. Depending on the parameters, the model can reproduce elimination, equilibrium or escape phases and it identifies a critical role of oxygen/nutrient supply in shaping the tumor growth. In addition, antitumor immune cells are key factors in controlling tumor growth, maintaining an equilibrium while protumor cells favor escape and tumor expansion.

IL-27 in combination with anti-PD-1 can be anti-cancer or pro-cancer

Interleukin-27 (IL-27) is known to play opposing roles in immunology. The present paper considers, specifically, the role IL-27 plays in cancer immunotherapy when combined with immune checkpoint inhibitor anti-PD-1. We first develop a mathematical model for this combination therapy, by a system of Partial Differential Equations, and show agreement with experimental results in mice injected with melanoma cells. We then proceed to simulate tumor volume with IL-27 injection at a variable dose F and anti-PD-1 at a variable dose g. We show that in some range of "small" values of g, as f increases tumor volume decreases as long as fFc(g), where Fc(g) is a monotone increasing function of g. This demonstrates that IL-27 can be both anti-cancer and pro-cancer, depending on the ranges of both anti-PD-1 and IL-27.

IL-35: New Target for Immunotherapy Targeting the Tumor Microenvironment

Interleukin 35(IL-35) is a newly discovered inhibitory cytokine of the IL12 family. More recently, IL-35 was found to be increased in the tumor microenvironment (TME) and peripheral blood of many patients with cancer, indicating that it plays an important role in the TME. Tumors secrete cytokines that recruit myeloid-derived suppressor cells (MDSCs) and regulatory T cells (Treg) into the TME to promote malignant progression, which is a great challenge for cancer treatment. Radiotherapy causes serious adverse effects, and tumor resistance to immune checkpoint inhibitors is still an unsolved challenge. Thus, new cancer therapy approaches are urgently needed. Numerous studies have shown that IL-35 can recruit immunosuppressive cells to enable tumor immune escape by promoting the conversion of immune cells into a tumor growth-promoting phenotype as well as facilitating tumor angiogenesis. IL-35-neutralizing antibodies were found to boost the chemotherapeutic effect of gemcitabine and considerably reduce the microvascular density of pancreatic cancer in mice. Therefore, targeting IL-35 in the TME provides a promising cancer treatment target. In addition, IL-35 may be used as an independent prognostic factor for some tumors in the near future. This review intends to reveal the interplay of IL-35 with immune cells in the TME, which may provide new options for the treatment of cancer.

Cancer immunotherapy with enveloped self-amplifying mRNA CARG-2020 that modulates IL-12, IL-17 and PD-L1 pathways to prevent tumor recurrence

Targeting multiple immune mechanisms may overcome therapy resistance and further improve cancer immunotherapy for humans. Here, we describe the application of virus-like vesicles (VLV) for delivery of three immunomodulators alone and in combination, as a promising approach for cancer immunotherapy. VLV vectors were designed to deliver single chain interleukin (IL)-12, short-hairpin RNA (shRNA) targeting programmed death ligand 1 (PD-L1), and a dominant-negative form of IL-17 receptor A (dn-IL17RA) as a single payload or as a combination payload. Intralesional delivery of the VLV vector expressing IL-12 alone, as well as the trivalent vector (designated CARG-2020) eradicated large established tumors. However, only CARG-2020 prevented tumor recurrence and provided long-term survival benefit to the tumor-bearing mice, indicating a benefit of the combined immunomodulation. The abscopal effects of CARG-2020 on the non-injected contralateral tumors, as well as protection from the tumor cell re-challenge, suggest immune-mediated mechanism of protection and establishment of immunological memory. Mechanistically, CARG-2020 potently activates Th1 immune mechanisms and inhibits expression of genes related to T cell exhaustion and cancer-promoting inflammation. The ability of CARG-2020 to prevent tumor recurrence and to provide survival benefit makes it a promising candidate for its development for human cancer immunotherapy.

Global stability and parameter analysis reinforce therapeutic targets of PD-L1-PD-1 and MDSCs for glioblastoma

Glioblastoma (GBM) is an aggressive primary brain cancer that currently has minimally effective treatments. Like other cancers, immunosuppression by the PD-L1-PD-1 immune checkpoint complex is a prominent axis by which glioma cells evade the immune system. Myeloid-derived suppressor cells (MDSCs), which are recruited to the glioma microenviroment, also contribute to the immunosuppressed GBM microenvironment by suppressing T cell functions. In this paper, we propose a GBM-specific tumor-immune ordinary differential equations model of glioma cells, T cells, and MDSCs to provide theoretical insights into the interactions between these cells. Equilibrium and stability analysis indicates that there are unique tumorous and tumor-free equilibria which are locally stable under certain conditions. Further, the tumor-free equilibrium is globally stable when T cell activation and the tumor kill rate by T cells overcome tumor growth, T cell inhibition by PD-L1-PD-1 and MDSCs, and the T cell death rate. Bifurcation analysis suggests that a treatment plan that includes surgical resection and therapeutics targeting immune suppression caused by the PD-L1-PD1 complex and MDSCs results in the system tending to the tumor-free equilibrium. Using a set of preclinical experimental data, we implement the approximate Bayesian computation (ABC) rejection method to construct probability density distributions that estimate model parameters. These distributions inform an appropriate search curve for global sensitivity analysis using the extended fourier amplitude sensitivity test. Sensitivity results combined with the ABC method suggest that parameter interaction is occurring between the drivers of tumor burden, which are the tumor growth rate and carrying capacity as well as the tumor kill rate by T cells, and the two modeled forms of immunosuppression, PD-L1-PD-1 immune checkpoint and MDSC suppression of T cells. Thus, treatment with an immune checkpoint inhibitor in combination with a therapeutic targeting the inhibitory mechanisms of MDSCs should be explored.

Different mechanisms of CD200-CD200R induce diverse outcomes in cancer treatment

The CD200 is a cell membrane protein expressed by tumor cells, and its receptor CD200 receptor (CD200R) is expressed by immune cells including macrophages and dendritic cells. The formation of CD200-CD200R inhibits the cellular functions of the targeted immune cells, so CD200 is one type of the immune checkpoint and blockade CD200-CD200R formation is a potential cancer treatment. However, the CD200 blockade has opposite treatment outcomes in different types of cancers. For instance, the CD200R deficient mice have a higher tumor load than the wild type (WT) mice in melanoma suggesting that CD200-CD200R inhibits melanoma. On the other hand, the antibody anti-CD200 treatment in pancreatic ductal adenocarcinoma (PDAC) and head and neck squamous cell carcinoma (HNSCC) significantly reduces the tumor load indicating that CD200-CD200R promotes PDAC and HNSCC. In this work, we hypothesize that different mechanisms of CD200-CD200R in tumor microenvironment could be one of the reasons for the diverse treatment outcomes of CD200 blockade in different types of cancers. We create one Ordinary Differential Equations (ODEs) model for melanoma including the inhibition of CCL8 and regulatory T cells and the switching from M2 to M1 macrophages by CD200-CD200R to capture the tumor inhibition by CD200-CD200R. We also create another ODEs model for PDAC and HNSCC including the promotion of the polarization and suppressive activities of M2 macrophages by CD200-CD200R to generate the tumor promotion by CD200-CD200R. Furthermore, we use these two models to investigate the treatment efficacy of the combination treatment between the CD200-CD200R blockade and the other immune checkpoint inhibitor, anti-PD-1. Our result shows that different mechanisms of CD200-CD200R can induce different treatment outcomes in combination treatments, namely, only the CD200-CD200R blockade reduces tumor load in melanoma and only the anti-PD-1 and CD200 knockout decrease tumor load in PDAC and HNSCC. Moreover, in melanoma, the CD200-CD200R mainly utilizes the inhibitions on M1 macrophages and dendritic cells to inhibit tumor growth, instead of M2 macrophages.

Mathematical modeling for the combination treatment of IFN- γ and anti-PD-1 in cancer immunotherapy

When the immune-checkpoint programmed death-1 (PD-1) binds to its ligand programmed death ligand 1 (PD-L1) to form the complex PD-1-PD-L1, this complex inactivates immune cells resulting in cell apoptosis, downregulation of immune reaction, and tumor evasion. The antibody, anti-PD-1 or anti-PD-L1, blocks the PD-1-PD-L1 complex formation to restore the functions of T cells. Combination of anti-PD-1 with other treatment shows promising in different types of cancer treatments. Interferon-gamma (IFN-γ) plays an important role in immune responses. It is mainly regarded as a pro-inflammatory cytokine that promotes the proliferation of CD8⁺ T cell and cytotoxic T cell, enhances the activation of Th1 cells and CD8⁺ T cells, and enhances tumor elimination. However, recent studies have been discovering many anti-inflammatory functions of IFN-γ, such as promotion of the PD-L1 expression, T cell apoptosis, and tumor metastasis, as well as inhibition of the immune recognition and the killing rates by T cells. In this work, we construct a mathematical model incorporating pro-inflammatory and anti-inflammatory functions of IFN-γ to capture tumor growth under anti-PD-1 treatment in the wild type and IFN-γ null mutant melanoma. Our simulation results qualitatively fit experimental data that IFN-γ null mutant with anti-PD-1 obtains the highest tumor reduction comparing to IFN-γ null mutant without anti-PD-1 and wild type tumor with anti-PD-1 therapy. Moreover, our synergy analysis indicates that, in the combination treatment, the tumor volume decreases as either the dosage of anti-PD-1 increases or the IFN-γ production efficiency decreases. Thus, the combination of anti-PD-1 and IFN-γ blockade improves the tumor reduction comparing to the monotherapy of anti-PD-1 or the monotherapy of IFN-γ blockade. We also find a threshold curve of the minimal dosage of anti-PD-1 corresponding to the IFN-γ production efficiency to ensure the tumor reduction under the presence of IFN-γ.

A two-dimensional mathematical model of tumor angiogenesis with CD147

Tumor angiogenesis is the tumor’s inherent blood supply system which is crucial for the growth of tumor. Extracellular Matrix Metallo Proteinases Inducer (EMMPRIN)/Cluster of Differentiation 147 (CD147) is found in high levels on tumor surfaces. This study focuses on these elevated levels of CD147 and the effect it has on tumor angiogenesis. The present article develops a Two-Dimensional Mathematical Model of Tumor Angiogenesis taking into account the CD147 molecule. The effects of CD147 on Tumor Angiogenesis Factors (TAFs), fibronectin and Matrix Metallo Proteinases (MMPs) are also incorporated. The results have been obtained through COMSOL Multiphysics 5.4 software. The results show that CD147 is responsible for swifter angiogenesis, calling for targeting this molecule in anti-angiogenic strategies. The present model is validated with the existing theoretical and experimental results.

The Molecular Role of IL-35 in Non-Small Cell Lung Cancer

Non-small cell lung cancer (NSCLC) is the most common type of lung cancer and a common cause of cancer-related death. Better understanding of the molecular mechanisms, pathogenesis, and treatment of NSCLC can help improve patient outcomes. Significant progress has been made in the treatment of NSCLC, and immunotherapy can prolong patient survival. However, the overall cure and survival rates are low, especially in patients with advanced metastases. Interleukin-35 (IL-35), an immunosuppressive factor, is associated with the onset and prognosis of various cancers. Studies have shown that IL-35 expression is elevated in NSCLC, and it is closely related to the progression and prognosis of NSCLC. However, there are few studies on the mechanism of IL-35 in NSCLC. This study discusses the role of IL-35 and its downstream signaling pathways in the pathogenesis of NSCLC and provides new insights into its therapeutic potential.

A PDE Model of Breast Tumor Progression in MMTV-PyMT Mice

The evolution of breast tumors greatly depends on the interaction network among different cell types, including immune cells and cancer cells in the tumor. This study takes advantage of newly collected rich spatio-temporal mouse data to develop a data-driven mathematical model of breast tumors that considers cells' location and key interactions in the tumor. The results show that cancer cells have a minor presence in the area with the most overall immune cells, and the number of activated immune cells in the tumor is depleted over time when there is no influx of immune cells. Interestingly, in the case of the influx of immune cells, the highest concentrations of both T cells and cancer cells are in the boundary of the tumor, as we use the Robin boundary condition to model the influx of immune cells. In other words, the influx of immune cells causes a dominant outward advection for cancer cells. We also investigate the effect of cells' diffusion and immune cells' influx rates in the dynamics of cells in the tumor micro-environment. Sensitivity analyses indicate that cancer cells and adipocytes' diffusion rates are the most sensitive parameters, followed by influx and diffusion rates of cytotoxic T cells, implying that targeting them is a possible treatment strategy for breast cancer.

Systems biology of angiogenesis signaling: Computational models and omics

Angiogenesis is a highly regulated multiscale process that involves a plethora of cells, their cellular signal transduction, activation, proliferation, differentiation, as well as their intercellular communication. The coordinated execution and integration of such complex signaling programs is critical for physiological angiogenesis to take place in normal growth, development, exercise, and wound healing, while its dysregulation is critically linked to many major human diseases such as cancer, cardiovascular diseases, and ocular disorders; it is also crucial in regenerative medicine. Although huge efforts have been devoted to drug development for these diseases by investigation of angiogenesis‐targeted therapies, only a few therapeutics and targets have proved effective in humans due to the innate multiscale complexity and nonlinearity in the process of angiogenic signaling. As a promising approach that can help better address this challenge, systems biology modeling allows the integration of knowledge across studies and scales and provides a powerful means to mechanistically elucidate and connect the individual molecular and cellular signaling components that function in concert to regulate angiogenesis. In this review, we summarize and discuss how systems biology modeling studies, at the pathway‐, cell‐, tissue‐, and whole body‐levels, have advanced our understanding of signaling in angiogenesis and thereby delivered new translational insights for human diseases.

This article is categorized under:

Cardiovascular Diseases > Computational Models

Cancer > Computational Models

Computational modelling of modern cancer immunotherapy

Modern cancer immunotherapy has revolutionised oncology and carries the potential to radically change the approach to cancer treatment. However, numerous questions remain to be answered to understand immunotherapy response better and further improve the benefit for future cancer patients. Computational models are promising tools that can contribute to accelerated immunotherapy research by providing new clues and hypotheses that could be tested in future trials, based on preceding simulations in addition to the empirical rationale. In this topical review, we briefly summarise the history of cancer immunotherapy, including computational modelling of traditional cancer immunotherapy, and comprehensively review computational models of modern cancer immunotherapy, such as immune checkpoint inhibitors (as monotherapy and combination treatment), co-stimulatory agonistic antibodies, bispecific antibodies, and chimeric antigen receptor T cells. The modelling approaches are classified into one of the following categories: data-driven top-down vs mechanistic bottom-up, simplistic vs detailed, continuous vs discrete, and hybrid. Several common modelling approaches are summarised, such as pharmacokinetic/pharmacodynamic models, Lotka-Volterra models, evolutionary game theory models, quantitative systems pharmacology models, spatio-temporal models, agent-based models, and logic-based models. Pros and cons of each modelling approach are critically discussed, particularly with the focus on the potential for successful translation into immuno-oncology research and routine clinical practice. Specific attention is paid to calibration and validation of each model, which is a necessary prerequisite for any successful model, and at the same time, one of the main obstacles. Lastly, we provide guidelines and suggestions for the future development of the field.

Data Driven Mathematical Model of Colon Cancer Progression

Every colon cancer has its own unique characteristics, and therefore may respond differently to identical treatments. Here, we develop a data driven mathematical model for the interaction network of key components of immune microenvironment in colon cancer. We estimate the relative abundance of each immune cell from gene expression profiles of tumors, and group patients based on their immune patterns. Then we compare the tumor sensitivity and progression in each of these groups of patients, and observe differences in the patterns of tumor growth between the groups. For instance, in tumors with a smaller density of naive macrophages than activated macrophages, a higher activation rate of macrophages leads to an increase in cancer cell density, demonstrating a negative effect of macrophages. Other tumors however, exhibit an opposite trend, showing a positive effect of macrophages in controlling tumor size. Although the results indicate that for all patients the size of the tumor is sensitive to the parameters related to macrophages, such as their activation and death rate, this research demonstrates that no single biomarker could predict the dynamics of tumors.

IL35 predicts prognosis in gastric cancer and is associated with angiogenesis by altering TIMP1, PAI1 and IGFBP1

Tumor angiogenesis is required for tumor growth and metastasis. Interleukin‐35 (IL35), a member of the IL12 family, is a dimer composed of IL12A and EBV‐induced gene 3（EBI3）. Elevated plasma IL35 levels have been reported to be associated with the occurrence and development of tumors. However, the role of IL35 in the angiogenesis of gastric cancer (GC) is still unclear. Here, we report that expression of IL35 is correlated with higher microvessel density, distant metastasis and poor prognosis in GC. Moreover, in vitro tube formation assays were performed to show that IL35 may contribute to the tube formation abilities of human umbilical vein endothelial cells. IL12A was observed to be the dominant subunit in promotion of tube formation. IL12A also inhibited expression of tissue inhibitor of metalloproteinase 1 and enhanced expression of plasminogen activator inhibitor 1 and insulin‐like growth factor‐binding protein 1 in a GC cell line. In conclusion, our data suggest that IL35 is involved in angiogenesis and is associated with poor prognosis for GC.

Evaluation of Transforming Growth Factor-β1 and Interleukin-35 Serum Levels in Patients with Placenta Accreta

OBJECTIVE

Placenta accreta is a pregnancy-related disorder with extreme trophoblast invasion and the adherence of the placenta to the uterine wall. This study aimed to investigate the serum level of transforming growth factor-beta 1 (TGF-β1) and interleukin (IL)-35 in patients with placenta accreta.

METHODS

Thirty-one women with placenta accreta and 57 healthy pregnant women were enrolled. The serum levels of TGF-β1 and IL-35 were measured using the enzyme-linked immunosorbent assay method.

RESULTS

The serum levels of both TGF-β and IL-35 were significantly higher in the placenta accreta group compared with the group of healthy women (1082.48 pg/mL vs 497.33 pg/mL and 4541.14 pg/mL vs 1306.04 pg/mL; P <.001, respectively). Moreover, the level of TGF-β1 positively correlated with the IL-35 level but other factors such as age, gestations, live births, and abortions did not correlate with IL-35 and TGF-β1 levels.

CONCLUSION

The serum levels of IL-35 and TGF-β1 may contribute to the pathogenesis of placenta accreta and could be considered as potential targets in clinical and diagnostic approaches.

Implications of immune-mediated metastatic growth on metastatic dormancy, blow-up, early detection, and treatment

Metastatic seeding of distant organs can occur in the very early stages of primary tumor development. Once seeded, these micrometastases may enter a dormant phase that can last decades. Curiously, the surgical removal of the primary tumor can stimulate the accelerated growth of distant metastases, a phenomenon known as metastatic blow-up. Recent clinical evidence has shown that the immune response can have strong tumor promoting effects. In this work, we investigate if the pro-tumor effects of the immune response can have a significant contribution to metastatic dormancy and metastatic blow-up. We develop an ordinary differential equation model of the immune-mediated theory of metastasis. We include both anti- and pro-tumor immune effects, in addition to the experimentally observed phenomenon of tumor-induced immune cell phenotypic plasticity. Using geometric singular perturbation analysis, we derive a rather simple model that captures the main processes and, at the same time, can be fully analyzed. Literature-derived parameter estimates are obtained, and model robustness is demonstrated through a time dependent sensitivity analysis. We determine conditions under which the parameterized model can successfully explain both metastatic dormancy and blow-up. The results confirm the significant active role of the immune system in the metastatic process. Numerical simulations suggest a novel measure to predict the occurrence of future metastatic blow-up in addition to new potential avenues for treatment of clinically undetectable micrometastases.

Quantitative Systems Pharmacology Approaches for Immuno-Oncology: Adding Virtual Patients to the Development Paradigm

Drug development in oncology commonly exploits the tools of molecular biology to gain therapeutic benefit through reprograming of cellular responses. In immuno‐oncology (IO) the aim is to direct the patient’s own immune system to fight cancer. After remarkable successes of antibodies targeting PD1/PD‐L1 and CTLA4 receptors in targeted patient populations, the focus of further development has shifted toward combination therapies. However, the current drug‐development approach of exploiting a vast number of possible combination targets and dosing regimens has proven to be challenging and is arguably inefficient. In particular, the unprecedented number of clinical trials testing different combinations may no longer be sustainable by the population of available patients. Further development in IO requires a step change in selection and validation of candidate therapies to decrease development attrition rate and limit the number of clinical trials. Quantitative systems pharmacology (QSP) proposes to tackle this challenge through mechanistic modeling and simulation. Compounds’ pharmacokinetics, target binding, and mechanisms of action as well as existing knowledge on the underlying tumor and immune system biology are described by quantitative, dynamic models aiming to predict clinical results for novel combinations. Here, we review the current QSP approaches, the legacy of mathematical models available to quantitative clinical pharmacologists describing interaction between tumor and immune system, and the recent development of IO QSP platform models. We argue that QSP and virtual patients can be integrated as a new tool in existing IO drug development approaches to increase the efficiency and effectiveness of the search for novel combination therapies.

Analysis of a breast cancer mathematical model by a new method to find an optimal protocol for HER2-positive cancer

Treatment of breast cancer (positive for HER2, i.e., ERBB2) is described by a mathematical model involving non-linear ordinary differential equations with a hidden hierarchy. To reveal the hierarchy of dynamical variables of the system being considered, we applied the singular perturbed vector field (SPVF) method, where a system of equations can be decomposed to fast and slow sub-systems with explicit small parameters. This new form of the model, which is called a singular perturbed system, enables us to apply a semi-analytical method called the method of directly defining inverse mapping (MDDiM), which is based on the homotopy analysis asymptotic method. We introduced the treatment protocol in explicit form, through an analytical function that describes the exact dose and intervals between treatments in a cyclical manner. In addition, a new algorithm for the optimal dosage that causes tumour shrinkage is presented in this study. Furthermore, we took the concept of protocol optimisation a step further and derived a differential equation that represents vaccination depending on tumour size and yields an optimal protocol of different doses at every time point. We introduced the treatment protocol in explicit form, through an analytical function that describes the exact dose and intervals between treatments in a cyclical manner. In addition, a new algorithm for finding the optimal dosage that causes tumour shrinkage is presented in this study. Additionally, we took the concept of protocol optimisation a step further and derived a differential equation that represents vaccination depending on tumour size and yields an optimal protocol of different doses at every time point.

Modeling immune cell behavior across scales in cancer

Detailed, mechanistic models of immune cell behavior across multiple scales in the context of cancer provide clinically relevant insights needed to understand existing immunotherapies and develop more optimal treatment strategies. We highlight mechanistic models of immune cells and their ability to become activated and promote tumor cell killing. These models capture various aspects of immune cells: (a) single‐cell behavior by predicting the dynamics of intracellular signaling networks in individual immune cells, (b) multicellular interactions between tumor and immune cells, and (c) multiscale dynamics across space and different levels of biological organization. Computational modeling is shown to provide detailed quantitative insight into immune cell behavior and immunotherapeutic strategies. However, there are gaps in the literature, and we suggest areas where additional modeling efforts should be focused to more prominently impact our understanding of the complexities of the immune system in the context of cancer.

This article is categorized under:

Biological Mechanisms > Cell Signaling

Models of Systems Properties and Processes > Mechanistic Models

Models of Systems Properties and Processes > Cellular Models

Interleukin-35 as an Emerging Player in Tumor Microenvironment

IL-35 is the newest member of IL-12 family. A dimeric protein consisting of two separate subunits has manifested suppressive actions on immune system, which is counterproductive in the context of cancers. Various reports have confirmed its inhibitory role on immune system which is carried out via formation of IL-35-producing regulatory T cells (iTr35), increased Treg development and suppressive Th17 cells growth. Although last decade has seen a great deal of scientific interest on this subject, the exact role, precise signal transduction and elaborative functions of IL-35 in tumor microenvironment (TME) remained elusive. Search for anti-IL-35 therapies have exhibited limited success in animal models. Contrarily, few studies have denied the idea that IL-35 plays a role in cancer. The purpose of this review is to analyze the reported scientific data on continuous symphony of IL-35 in cancers since the inception of former.

Emerging role of IL-35 in inflammatory autoimmune diseases

Interleukin 35 (IL-35) is the recently identified member of the IL-12 family of cytokines and provides the possibility to be a target for new therapies for autoimmune, inflammatory diseases. It is composed of an α chain (p35) and a β chain (EBI3). IL-35 mediates signaling by binding to its receptors, activates subsequent signaling pathways, and therefore, regulates the differentiation, function of T, B cells, macrophages, dendritic cells. Recent findings have shown abnormal expression of IL-35 in inflammatory autoimmune diseases, such as systemic lupus erythematosus, rheumatoid arthritis, inflammatory bowel disease, multiple sclerosis, type 1 diabetes, psoriasis, multiple sclerosis, autoimmune hepatitis, experimental autoimmune uveitis. In addition, functional analysis suggested that IL-35 is critical in the onset and development of these diseases. Therefore, the present study will systematically review what had been occurred regarding IL-35 in inflammatory autoimmune disease. The information collected will help to understand the biologic role of IL-35 in immune cells, and give information about the therapeutic potential of IL-35 in these diseases.

The Role, Involvement and Function(s) of Interleukin-35 and Interleukin-37 in Disease Pathogenesis

The recently identified cytokines-interleukin (IL)-35 and interleukin (IL)-37-have been described for their anti-inflammatory and immune-modulating actions in numerous inflammatory diseases, auto-immune disorders, malignancies, infectious diseases and sepsis. Either cytokine has been reported to be reduced and in some cases elevated and consequently contributed towards disease pathogenesis. In view of the recent advances in utilizing cytokine profiles for the development of biological macromolecules, beneficial in the management of certain intractable immune-mediated disorders, these recently characterized cytokines (IL-35 and IL-37) offer potential as reasonable targets for the discovery of novel immune-modulating anti-inflammatory therapies. A detailed comprehension of their sophisticated regulatory mechanisms and patterns of expression may provide unique opportunities for clinical application as highly selective and target specific therapeutic agents. This review seeks to summarize the recent advancements in discerning the dynamics, mechanisms, immunoregulatory and anti-inflammatory actions of IL-35 and IL-37 as they relate to disease pathogenesis.

Elevated level of interleukin-35 in colorectal cancer induces conversion of T cells into iTr35 by activating STAT1/STAT3

IL-35 is a novel heterodimeric and inhibitory cytokine, composed of interleukin-12 subunit alpha (P35) and Epstein-Barr virus -induced gene 3 (EBI3). IL-35 has been reported to be produced by a range of cell types, especially regulatory T cells, and to exert immunosuppressive effects via the STATx signaling pathway. In this study, we demonstrated that IL-35 expression was elevated in both serum and tumors in patients with colorectal cancer. IL-35 mainly expressed in CD4⁺ T cells in human colorectal cancer tumors and adjacent tissues. Increased IL-35 expression in tumor-adjacent tissues was significantly associated with tumor metastasis. IL-35 inhibited the proliferation of CD4⁺CD25⁻ T effector cells in vitro in a dose-dependent manner, and its suppression was partially reversed by applying IL-35-neutralizing antibodies. IL-35 treatment activated the phosphorylation of both STAT1 and STAT3 in human CD4⁺ T cells. Meanwhile, IL-35 induced a positive feedback loop to promote its own production. We observed that Tregs obtained from colorectal cancer patients were capable of inducing more IL-35 production. In addition, EBI3 promoter-driven luciferase activity was higher than that of the mock plasmid after IL-35stimulation. Thus, our study indicates that the high level of IL-35 in colorectal cancer promotes the production of IL-35 via STAT1 and STAT3, which suppresses T cell proliferation and may participate in tumor immunotolerance.

The Role of Exosomes in Pancreatic Cancer Microenvironment

Exosomes are nanovesicles shed by cells as a means of communication with other cells. Exosomes contain mRNAs, microRNAs (miRs) and functional proteins. In the present paper, we develop a mathematical model of tumor-immune interaction by means of exosomes shed by pancreatic cancer cells and dendritic cells. Cancer cells' exosomes contain miRs that promote their proliferation and that inhibit immune response by dendritic cells, and by CD4+ and CD8+ T cells. Dendritic cells release exosomes with proteins that induce apoptosis of cancer cells and that block regulatory T cells. Simulations of the model show how the size of the pancreatic cancer can be determined by measurement of specific miRs (miR-21 and miR-203 in the case of pancreatic cancer), suggesting these miRs as biomarkers for cancer.

Demystifying the cytokine network: Mathematical models point the way

Cytokines provide the means by which immune cells communicate with each other and with parenchymal cells. There are over one hundred cytokines and many exist in families that share receptor components and signal transduction pathways, creating complex networks. Reductionist approaches to understanding the role of specific cytokines, through the use of gene-targeted mice, have revealed further complexity in the form of redundancy and pleiotropy in cytokine function. Creating an understanding of the complex interactions between cytokines and their target cells is challenging experimentally. Mathematical and computational modeling provides a robust set of tools by which complex interactions between cytokines can be studied and analyzed, in the process creating novel insights that can be further tested experimentally. This review will discuss and provide examples of the different modeling approaches that have been used to increase our understanding of cytokine networks. This includes discussion of knowledge-based and data-driven modeling approaches and the recent advance in single-cell analysis. The use of modeling to optimize cytokine-based therapies will also be discussed.

Could field cancerization be interpreted as a biochemical anomaly amplification due to transformed cells?

Field cancerization is a concept used to explain cellular and molecular alterations in tissue associated to neoplasia and cancer. This effect was proposed by Slaughter in order to explain the development of multiple primary tumors and locally recurrent cancer. The particular changes associated with this effect, in each type of cancer, have been detected even at distances greater than 10cm off the tumor, in areas classified as normal by histopathological studies. Early detection of lung, colon, and ovary cancer has been reported by the use of Partial Wave Microscopy Spectroscopy (PWS) and has been explained in terms of the field cancerization effect. Until now, field cancerization has been studied as a field effect and we hypothesize that it can be understood as an amplifying effect of biochemical abnormalities in cells, which leads us to ask the question: Could field cancerization be interpreted as a biochemical anomaly amplification due to transformed cells? We propose this question because the biochemical changes due to field cancerization alter the dynamics of molecules and cells in abnormal tissues in comparison to normal ones, these alterations modify the interaction of intracellular and extracellular medium, as well as cellular movement. We hypothesize that field cancerization when interpreted as an amplification effect can be used for the early detection of cancer by measuring the change of cell dynamics.

Circulating low IL-23: IL-35 cytokine ratio promotes progression associated with poor prognosisin breast cancer

The interleukin (IL)-12 family, composed of heterodimeric cytokines including IL-12 (formed by IL-12p35 and IL-12p40 subunits), IL-23 (formed by IL-23p19 and IL-12p40 subunits), IL-27 (formed by IL-27p28 and EBI3 subunits) and IL-35 (formed by IL-12p35 and EBI3 subunits), establishes a link between innate and adaptive immunity that involves different immune effector cells and cytokines to tumors. However, the role of IL-12 family in breast cancer (BC) progression and prognosis remains unclear. In the present study, we demonstrated evidence indicating that EBI3, IL-12p35 and IL-12p40 but not IL-23p19 or IL-27p28 were highly expressed in BC tissues, suggested that tumor derived EBI3, IL-12p35 and IL-12p40 were associated with tumor progression. Circulating IL-12 and IL-23 low expressed, but IL-27 and IL-35 high expressed in BC patients, especially circulating IL-23 associated with IL-35 to mediate BC tumor resection. Ki-67, p53 and EGFR expression on BC tissues, as well as CA125, CA153 and CA199 levels on BC bloods increased when circulating IL-23: IL-35 ratio decreased. Together, for the first time, our data suggest that circulating IL-23: IL-35 ratio may be an important indicator association with BC progression and prognosis. However, further research should be carried out to assess the implications of circulating IL-23: IL-35 ratio in a larger sample size.

[Current Research of the Roles of IL-35 in Tumor Progression]

Interleukin(IL)-35 is a new member of the interleukin-12 superfamily. Since its first report in 2007, IL-35 rapidly became a research highlight in the field of immunology. Like other IL-12 superfamily members, IL-35 was a heterodimer which was composed of an α chain P35 and a β chain Epstein-Barr virus induced gene 3 (EBI3). Recent research work revealed two distinct roles of IL-35. Firstly, IL-35 is highly expressed in some kinds of inflammatory diseases and autoimmune diseases and plays import roles in the pathogenesis. Secondly, IL-35 is positively expressed in some cancers and plays some roles in the process of tumor progression. Here we demonstrate the structure and the signalling of IL-35. We reviewed the the roles of IL-35 in promoting tumor progression.

Serum uPAR as Biomarker in Breast Cancer Recurrence: A Mathematical Model

There are currently over 2.5 million breast cancer survivors in the United States and, according to the American Cancer Society, 10 to 20 percent of these women will develop recurrent breast cancer. Early detection of recurrence can avoid unnecessary radical treatment. However, self-examination or mammography screening may not discover a recurring cancer if the number of surviving cancer cells is small, while biopsy is too invasive and cannot be frequently repeated. It is therefore important to identify non-invasive biomarkers that can detect early recurrence. The present paper develops a mathematical model of cancer recurrence. The model, based on a system of partial differential equations, focuses on tissue biomarkers that include the plasminogen system. Among them, only uPAR is known to have significant correlation to its concentration in serum and could therefore be a good candidate for serum biomarker. The model includes uPAR and other associated cytokines and cells. It is assumed that the residual cancer cells that survived primary cancer therapy are concentrated in the same location within a region with a very small diameter. Model simulations establish a quantitative relation between the diameter of the growing cancer and the total uPAR mass in the cancer. This relation is used to identify uPAR as a potential serum biomarker for breast cancer recurrence.

Elevated serum IL-35 and increased expression of IL-35-p35 or -EBI3 in CD4(+)CD25(+) T cells in patients with active tuberculosis

Despite the recent appreciation of interleukin 35 (IL-35) function in inflammatory diseases, little is known for IL-35 response in patients with active tuberculosis (ATB). In the current study, we demonstrated that ATB patients exhibited increases in serum IL-35 and in mRNA expression of both subunits of IL-35 (p35 and EBI3) in white blood cells and peripheral blood mononuclear cells. Consistently, anti-TB drug treatment led to reduction in serum IL-35 level and p35 or EBI3 expression. TB infection was associated with expression of p35 or EBI3 protein in CD4(+) but not CD8(+) T cells. Most p35(+)CD4(+) T cells and EBI3(+)CD4(+) T cells expressed Treg-associated marker CD25. Our findings may be important in understanding immune pathogenesis of TB. IL-35 in the blood may potentially serve as a biomarker for immune status and prognosis in TB.

A cancer treatment based on synergy between anti-angiogenic and immune cell therapies

A mathematical model integrating tumor angiogenesis and tumor-targeted cytotoxicity by immune cells was developed to identify the therapeutic window of two distinct modes to treat cancer: (1) an anti-angiogenesis treatment based on the monoclonal antibody bevacizumab that targets tumor vasculature, and (2) immunotherapy involving the injection of unlicensed dendritic cells to boost the anti-tumor adaptive response. The angiogenic cytokine Vascular Endothelial Growth Factor (VEGF) contributes to the immunosuppressive tumor microenvironment, which is responsible for the short-lived therapeutic effect of cancer-targeted immunotherapy. The effect of immunosuppression on the width of the therapeutic window of each treatment was quantified. Experimental evidence has shown that neutralizing immunosuppressive cytokines results in an enhanced immune response against infections and chronic diseases. The model was used to determine treatment protocols involving the combination of anti-VEGF and unlicensed dendritic cell injections that enhance tumor regression. The model simulations predicted that the most effective method to treat tumors involves administering a series of biweekly anti-VEGF injections to disrupt angiogenic processes and limit tumor growth. The simulations also verified the hypothesis that reducing the concentration of the immunosuppressive factor VEGF prior to an injection of unlicensed dendritic cells enhances the cytotoxicity of CD8+ T cells and results in complete tumor elimination. Feasible treatment protocols for tumors that are diagnosed late and have grown to a relatively large size were identified.

Interleukin-35 is upregulated in systemic sclerosis and its serum levels are associated with early disease

OBJECTIVES

IL-35 is a member of the IL-12 family consisting of p35/IL-12a and EBI3/IL-27b subunits. IL-35 exerts immunomodulatory activities in experimental and human autoimmune inflammatory conditions. Our aim was to assess IL-35 expression in the skin and circulation of SSc patients and to characterize its potential association with SSc-related features.

METHODS

Expression of IL-35 in skin and dermal fibroblasts was quantified by quantitative PCR, immunohistochemistry and immunofluorescence. Serum levels of IL-35 (by ELISA), CRP (by turbidimetry), ANA (by immunofluorescence) and autoantibodies of the ENA complex (by immunoblot) were measured in 40 SSc patients. Serum IL-35 was determined in 40 age- and sex-matched healthy controls.

RESULTS

IL-35 expression was increased in SSc skin and dermal fibroblasts in a TGF-β-dependent manner. IL-35 induced an activated phenotype in resting fibroblasts and enhanced the release of collagen. IL-35 serum levels were increased in patients with SSc compared with healthy controls [median 83.9 (interquartile range 45.1-146.1) vs 36.2 (interquartile range 17.2-49.4) pg/ml, P < 0.0001]. Serum IL-35 was negatively correlated with disease duration (r = -0.4339, P = 0.0052). In line with this finding, serum IL-35 was increased in patients with an early SSc pattern on capillaroscopy assessment compared with those with active and late SSc patterns.

CONCLUSION

The present study demonstrates overexpression of IL-35 in SSc skin, dermal fibroblasts and serum. TGF-β induces IL-35, which in turn activates resting fibroblasts and enhances the release of collagen, thereby contributing to aberrant TGF-β signalling in SSc. Increased serum IL-35 is associated with early, inflammatory stages of SSc.