The re-polarisation of M2 and M1 macrophages and its role on cancer outcomes

An agent-based model of monocyte differentiation into tumour-associated macrophages in chronic lymphocytic leukemia

Monocyte-derived macrophages help maintain tissue homeostasis and defend the organism against pathogens. In tumors, recent studies have uncovered complex macrophage populations, including tumor-associated macrophages, which support tumorigenesis through cancer hallmarks such as immunosuppression, angiogenesis, or matrix remodeling. In the case of chronic lymphocytic leukemia, these macrophages are known as nurse-like cells (NLCs) and they protect leukemic cells from spontaneous apoptosis, contributing to their chemoresistance. We propose an agent-based model of monocyte differentiation into NLCs upon contact with leukemic B cells in vitro. We performed patient-specific model optimization using cultures of peripheral blood mononuclear cells from patients. Using our model, we were able to reproduce the temporal survival dynamics of cancer cells in a patient-specific manner and to identify patient groups related to distinct macrophage phenotypes. Our results show a potentially important role of phagocytosis in the polarization process of NLCs and in promoting cancer cells' enhanced survival.

Quantification of spatial and phenotypic heterogeneity in an agent-based model of tumour-macrophage interactions

We introduce a new spatial statistic, the weighted pair correlation function (wPCF). The wPCF extends the existing pair correlation function (PCF) and cross-PCF to describe spatial relationships between points marked with combinations of discrete and continuous labels. We validate its use through application to a new agent-based model (ABM) which simulates interactions between macrophages and tumour cells. These interactions are influenced by the spatial positions of the cells and by macrophage phenotype, a continuous variable that ranges from anti-tumour to pro-tumour. By varying model parameters that regulate macrophage phenotype, we show that the ABM exhibits behaviours which resemble the 'three Es of cancer immunoediting': Equilibrium, Escape, and Elimination. We use the wPCF to analyse synthetic images generated by the ABM. We show that the wPCF generates a 'human readable' statistical summary of where macrophages with different phenotypes are located relative to both blood vessels and tumour cells. We also define a distinct 'PCF signature' that characterises each of the three Es of immunoediting, by combining wPCF measurements with the cross-PCF describing interactions between vessels and tumour cells. By applying dimension reduction techniques to this signature, we identify its key features and train a support vector machine classifier to distinguish between simulation outputs based on their PCF signature. This proof-of-concept study shows how multiple spatial statistics can be combined to analyse the complex spatial features that the ABM generates, and to partition them into interpretable groups. The intricate spatial features produced by the ABM are similar to those generated by state-of-the-art multiplex imaging techniques which distinguish the spatial distribution and intensity of multiple biomarkers in biological tissue regions. Applying methods such as the wPCF to multiplex imaging data would exploit the continuous variation in biomarker intensities and generate more detailed characterisation of the spatial and phenotypic heterogeneity in tissue samples.

PRR11 is a prognostic biomarker and correlates with immune infiltrates in bladder urothelial carcinoma

Abnormal proline-rich protein 11 (PRR11) expression is associated with various tumors. However, there are few reports concerning PRR11 with prognostic risk, immune infiltration, or immunotherapy of bladder urothelial carcinoma (BLCA). This study is based on online databases, such as Oncomine, GEPIA, HPA, LinkedOmics, TIMER, ESTIMATE and TISIDB, and BLCA data downloaded from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus, we employed an array of bioinformatics methods to explore the potential oncogenic roles of PRR11, including analyzing the relationship between PRR11 and prognosis, tumor mutational burden (TMB), microsatellite instability, and immune cell infiltration in BLCA. The results depict that PRR11 is highly expressed in BLCA, and BLCA patients with higher PRR11 expression have worse outcomes. In addition, there was a significant correlation between PRR11 expression and TMB and tumor immune infiltration. These findings suggest that PRR11 can be used as a potential marker for BLCA patient assessment and risk stratification to improve clinical prognosis, and its potential regulatory mechanism in the BLCA tumor microenvironment and targeted therapy is worthy of further investigation.

Role of Patient-Derived Models of Cancer in Translational Oncology

Cancer is a heterogeneous disease. Each individual tumor is unique and characterized by structural, cellular, genetic and molecular features. Therefore, patient-derived cancer models are indispensable tools in cancer research and have been actively introduced into the healthcare system. For instance, patient-derived models provide a good reproducibility of susceptibility and resistance of cancer cells against drugs, allowing personalized therapy for patients. In this article, we review the advantages and disadvantages of the following patient-derived models of cancer: (1) PDC-patient-derived cell culture, (2) PDS-patient-derived spheroids and PDO-patient-derived organoids, (3) PDTSC-patient-derived tissue slice cultures, (4) PDX-patient-derived xenografts, humanized PDX, as well as PDXC-PDX-derived cell cultures and PDXO-PDX-derived organoids. We also provide an overview of current clinical investigations and new developments in the area of patient-derived cancer models. Moreover, attention is paid to databases of patient-derived cancer models, which are collected in specialized repositories. We believe that the widespread use of patient-derived cancer models will improve our knowledge in cancer cell biology and contribute to the development of more effective personalized cancer treatment strategies.

The impact of tumor associated macrophages on tumor biology under the lens of mathematical modelling: A review

In this article, we review the role of mathematical modelling to elucidate the impact of tumor-associated macrophages (TAMs) in tumor progression and therapy design. We first outline the biology of TAMs, and its current application in tumor therapies, and their experimental methods that provide insights into tumor cell-macrophage interactions. We then focus on the mechanistic mathematical models describing the role of macrophages as drug carriers, the impact of macrophage polarized activation on tumor growth, and the role of tumor microenvironment (TME) parameters on the tumor-macrophage interactions. This review aims to identify the synergies between biological and mathematical approaches that allow us to translate knowledge on fundamental TAMs biology in addressing current clinical challenges.

A Mathematical Study of the Role of tBregs in Breast Cancer

A model for the mathematical study of immune response to breast cancer is proposed and studied, both analytically and numerically. It is a simplification of a complex one, recently introduced by two of the present authors. It serves for a compact study of the dynamical role in cancer promotion of a relatively recently described subgroup of regulatory B cells, which are evoked by the tumour.

Mathematical Modeling and Analysis of CD200-CD200R in Cancer Treatment

CD200 is a cell membrane protein that binds to its receptor, CD200 receptor (CD200R). The CD200 positive tumor cells inhibit the cellular functions of M1 and M2 macrophages and dendritic cells (DCs) through the CD200-CD200R complex, resulting in downregulation of Interleukin-10 and Interleukin-12 productions and affecting the activation of cytotoxic T lymphocytes. In this work, we provide two ordinary differential equation models, one complete model and one simplified model, to investigate how the binding affinities of CD200R and the populations of M1 and M2 macrophages affect the functions of the CD200-CD200R complex in tumor growth. Our simulations demonstrate that (i) the impact of the CD200-CD200R complex on tumor promotion or inhibition highly depends on the binding affinity of the CD200R on M2 macrophages and DCs to the CD200 on tumor cells, and (ii) a stronger binding affinity of the CD200R on M1 macrophages or DCs to the CD200 on tumor cells induces a higher tumor cell density in the CD200 positive tumor. Thus, the CD200 blockade would be an efficient treatment method in this case. Moreover, the simplified model shows that the binding affinity of CD200R on macrophages is the major factor to determine the treatment efficacy of CD200 blockade when the binding affinities of CD200R on M1 and M2 macrophages are significantly different to each other. On the other hand, both the binding affinity of CD200R and the population of macrophages are the major factors to determine the treatment efficacy of CD200 blockade when the binding affinities of CD200R on M1 and M2 macrophages are close to each other. We also analyze the simplified model to investigate the dynamics of the positive and trivial equilibria of the CD200 positive tumor case and the CD200 deficient tumor case. The bifurcation diagrams show that when M1 macrophages dominate the population, the tumor cell density of the CD200 positive tumor is higher than the one of CD200 deficient tumor. Moreover, the dynamics of tumor cell density change from tumor elimination to tumor persistence to oscillation, as the maximal proliferation rate of tumor cells increases.

Mathematical investigation into the role of macrophage heterogeneity on the temporal and spatio-temporal dynamics of non-small cell lung cancers

Non Small Cell Lung Cancer (NSCLC) is the most common type of lung cancer, and represents the leading cause of cancer-related deaths worldwide. Experimental studies have shown that these solid cancers are heavily infiltrated with macrophages: anti-tumour M1 macrophages, pro-tumour M2 macrophages, and macrophage subtypes sharing both M1 and M2 properties. In this study we aim to investigate qualitatively the role of macrophages with different functional phenotypes (especially those with mixed phenotypes) on cancer dynamics and the success of different immunotherapies for cancer. To this end, we start with two time-evolving mathematical models for cancer-immune interactions that consider: (i) the effect of the two extreme phenotypes, M1 and M2 cells; (ii) the effect of M1 and M2 cells, as well as a macrophage sub-population with a mixed phenotype (throughout this theoretical study we call these cells "M12 cells"). We compare the dynamics of the two models using computational approaches, paying particular attention to the effect of different anti-cancer immunotherapies that focus on macrophages. Since data available for NSCLC and macrophage interactions are incomplete, we perform a global sensitivity analysis to see the influence of input parameters on model outcomes. Finally, we consider extensions of the previous two models to include also the spatial movement of cells, and investigate the role of macrophages with extreme phenotypes and with mixed phenotypes, on the invasion of cancer cells into the surrounding extracellular matrix (ECM). We use numerical simulations to investigate the macrophages phenotypes at the tumour center versus the invasive margin. Again, we examine the impact of immunotherapies for cancer on the spatial dynamics of cancers and immune cells, and observe a shift in the phenotype of macrophages distributed at the tumour center and invasive margin.

Increased prognostic value of clinical–reproductive model in Chinese female patients with esophageal squamous cell carcinoma

BACKGROUND

In China, it has been well recognized that some female patients with esophageal squamous cell carcinoma (ESCC) have different overall survival (OS) time, even with the same tumor-node-metastasis (TNM) stage, challenging the prognostic value of the TNM system alone. An effective predictive model is needed to accurately evaluate the prognosis of female ESCC patients.

AIM

To construct a novel prognostic model with clinical and reproductive data for Chinese female patients with ESCC, and to assess the incremental prognostic value of the full model compared with the clinical model and TNM stage.

METHODS

A new prognostic nomogram incorporating clinical and reproductive features was constructed based on univariatie and Cox proportional hazards survival analysis from a training cohort (n = 175). The results were recognized using the internal (n = 111) and independent external (n = 85) validation cohorts. The capability of the clinical–reproductive model was evaluated by Harrell’s concordance index (C-index), Kaplan–Meier curve, time-dependent receiver operating characteristic (ROC), calibration curve and decision curve analysis. The correlations between estrogen response and immune-related pathways and some gene markers of immune cells were analyzed using the TIMER 2.0 database.

RESULTS

A clinical–reproductive model including incidence area, age, tumor differentiation, lymph node metastasis (N) stage, estrogen receptor alpha (ESR1) and beta (ESR2) expression, menopausal age, and pregnancy number was constructed to predict OS in female ESCC patients. Compared to the clinical model and TNM stage, the time-dependent ROC and C-index of the clinical–reproductive model showed a good discriminative ability for predicting 1-, 3-, and 5-years OS in the primary training, internal and external validation sets. Based on the optimal cut-off value of total prognostic scores, patients were classified into high- and low-risk groups with significantly different OS. The estrogen response was significantly associated with p53 and apoptosis pathways in esophageal cancer.

CONCLUSION

The clinical–reproductive prognostic nomogram has an incremental prognostic value compared with the clinical model and TNM stage in predicting OS in Chinese female ESCC patients.

Anticancer nanomedicines harnessing tumor microenvironmental components

INTRODUCTION

Small-molecular drugs are extensively used in cancer therapy, while they have issues of nonspecific distribution and consequent side effects. Nanomedicines that incorporate chemotherapeutic drugs have been developed to enhance the therapeutic efficacy of these drugs and reduce their side effects. One of the promising strategies is to prepare nanomedicines by harnessing the unique tumor microenvironment (TME).

AREAS COVERED

The TME contains numerous cell types that specifically express specific antibodies on the surface including tumor vascular endothelial cells, tumor-associated adipocytes, tumor-associated fibroblasts, tumor-associated immune cells and cancer stem cells. The physicochemical environment is characterized with a low pH, hypoxia, and a high redox potential resulting from tumor-specific metabolism. The intelligent nanomedicines can be categorized into two groups: the first group which is rapidly responsive to extracellular chemical/biological factors in the TME and the second one which actively and/or specifically targets cellular components in the TME.

EXPERT OPINION

In this paper, we review recent progress of nanomedicines by harnessing the TME and illustrate the principles and advantages of different strategies for designing nanomedicines, which are of great significance for exploring novel nanomedicines or translating current nanomedicines into clinical practice. We will discuss the challenges and prospects of preparing nanomedicines to utilize or alter the TME for achieving effective, safe anticancer treatment.

Algorithmic asymptotic analysis: Extending the arsenal of cancer immunology modeling

The recent advances in cancer immunotherapy boosted the development of tumor-immune system models, with the aim to indicate more efficient treatments. Physical understanding is however difficult to be acquired, due to the complexity and the multi-scale dynamics of these models. In this work, the dynamics of a fundamental model formulating the interactions of tumor cells with natural killer cells, CD8⁺ T cells and circulating lymphocytes is examined. It is first shown that the long-term evolution of the system towards high-tumor or tumor-free equilibria is determined by the dynamics of an initial explosive stage of tumor progression. Focusing on this stage, the algorithmic Computational Singular Perturbation methodology is employed to identify the underlying mechanisms confining the system's evolution and the governing slow dynamics along them. These insights are preserved along different tumor-immune system and patient-dependent realizations. On top of these identifications, a novel reduced model is algorithmically constructed, which accurately predicts the dynamics of the system during the explosive stage and includes half of the parameters of the detailed model. The present analysis demonstrates the potential of algorithmic asymptotic analysis for acquiring physical understanding and for simplifying the complexity of cancer immunology models. Along with the current techniques on the field, this analysis can provide guidelines for more effective treatment development.

Mathematically modelling inflammation as a promoter of tumour growth

Inflammation is now known to play a significant role in tumour growth and progression. It is also difficult to adequately quantify systemic inflammation and the resulting localized effects in cancer. Here, we use experimental data to infer the possible contributions of inflammation in a mouse model of cancer. The model is validated by predicting tumour growth under anti-inflammatory treatments, and combination cancer therapies are explored. We then extend the model to consider simultaneous tumour implants at two distinct sites, which experimentally was shown to result in one large and one small tumour. We use this model to examine the role inflammation may play in the growth rate separation. Finally, we use this predictive two-tumour model to explore implications of inflammation on metastases, surgical removal of the primary and adjuvant anti-inflammatory treatments. This work suggests that improved tumour control can be obtained by targeting both the cancer and host, through anti-inflammatory treatments, including reduced metastatic burden post-surgical removal of primary tumours.

Investigating Optimal Chemotherapy Options for Osteosarcoma Patients through a Mathematical Model

Simple Summary

Osteosarcoma is a rare type of cancer with poor prognoses. However, to the best of our knowledge, there are no mathematical models that study the impact of chemotherapy treatments on the osteosarcoma microenvironment. In this study, we developed a data driven mathematical model to analyze the dynamics of the important players in three groups of osteosarcoma tumors with distinct immune patterns in the presence of the most common chemotherapy drugs. The results indicate that the treatments’ start times and optimal dosages depend on the unique growth rate of the tumor, which implies the necessity of personalized medicine. Furthermore, the developed model can be extended by others to build models that can recommend individual-specific optimal dosages.

Abstract

Since all tumors are unique, they may respond differently to the same treatments. Therefore, it is necessary to study their characteristics individually to find their best treatment options. We built a mathematical model for the interactions between the most common chemotherapy drugs and the osteosarcoma microenvironments of three clusters of tumors with unique immune profiles. We then investigated the effects of chemotherapy with different treatment regimens and various treatment start times on the behaviors of immune and cancer cells in each cluster. Saliently, we suggest the optimal drug dosages for the tumors in each cluster. The results show that abundances of dendritic cells and HMGB1 increase when drugs are given and decrease when drugs are absent. Populations of helper T cells, cytotoxic cells, and IFN-γ grow, and populations of cancer cells and other immune cells shrink during treatment. According to the model, the MAP regimen does a good job at killing cancer, and is more effective than doxorubicin and cisplatin combined or methotrexate alone. The results also indicate that it is important to consider the tumor’s unique growth rate when deciding the treatment details, as fast growing tumors need early treatment start times and high dosages.

TNFSF9 Is a Prognostic Biomarker and Correlated with Immune Infiltrates in Pancreatic Cancer

BACKGROUND

TNFSF9 gene has been found to play an anti-tumor role and regulate the function of immune cells. However, the prognostic role of TNFSF9 in pancreatic cancer and its relationship with immune cell infiltration have not been studied.

METHODS

We used Oncomine, UALCAN, and GEPIA databases to analyze the expression of TNFSF9 in pancreatic cancer. We used Kaplan-Meier plotters, GEPIA, and UALCAN to evaluate the effect of TNFSF9 on clinical prognosis. We further used TIMER to study the correlation between TNFSF9 and cancer immune infiltrate cells. In addition, we used GEPIA to analyze the correlation between TNFSF9 expression and gene markers of immune infiltrate cells.

RESULTS

TNFSF9 mRNA expression level was remarkably increased in pancreatic cancer than that in normal tissues (both P < 0.05). In addition, high TNFSF9 expression was significantly related to poor overall survival (OS) and relapse-free survival (RFS) in pancreatic cancer (OS HR = 2.02, P = 0.0012; RFS HR = 2.63, P = 0.022). Moreover, high TNFSF9 expression in pancreatic cancer patients was associated with worse OS in stage 1 to 2 but not stage 3 and stage 4. Specifically, TNFSF9 expression and CD8⁺ T cell infiltration of pancreatic cancer was negatively correlated. TNFSF9 expression showed strong correlations with M1 macrophages in pancreatic cancer.

CONCLUSIONS

Our results suggest that TNFSF9 is associated with prognosis and CD8⁺ T cell infiltration levels in patients with pancreatic cancer. Further, TNFSF9 expression potentially contributes to the modulation of M1 polarization of macrophages. These findings indicate that TNFSF9 can be serves as a prognostic biomarker in determining the prognosis of pancreatic cancer and is associated with different types of phenotypes of immune cell infiltration.

Data-Driven Mathematical Model of Osteosarcoma

As the immune system has a significant role in tumor progression, in this paper, we develop a data-driven mathematical model to study the interactions between immune cells and the osteosarcoma microenvironment. Osteosarcoma tumors are divided into three clusters based on their relative abundance of immune cells as estimated from their gene expression profiles. We then analyze the tumor progression and effects of the immune system on cancer growth in each cluster. Cluster 3, which had approximately the same number of naive and M2 macrophages, had the slowest tumor growth, and cluster 2, with the highest population of naive macrophages, had the highest cancer population at the steady states. We also found that the fastest growth of cancer occurred when the anti-tumor immune cells and cytokines, including dendritic cells, helper T cells, cytotoxic cells, and IFN-γ, switched from increasing to decreasing, while the dynamics of regulatory T cells switched from decreasing to increasing. Importantly, the most impactful immune parameters on the number of cancer and total cells were the activation and decay rates of the macrophages and regulatory T cells for all clusters. This work presents the first osteosarcoma progression model, which can be later extended to investigate the effectiveness of various osteosarcoma treatments.

Modeling of tumor response to macrophage and T lymphocyte interactions in the liver metastatic microenvironment

The dynamic interactions between macrophages and T-lymphocytes in the tumor microenvironment exert both antagonistic and synergistic functions affecting tumor growth. Extensive experimental effort has been expended to investigate immunotherapeutic strategies targeting macrophage polarization as well as T-cell activation with the goal to promote tumor cell killing and cancer elimination. However, these interactions remain poorly understood, and cancer immunotherapeutic strategies are often disappointing. The complex system encompassing innate and adaptive immune cell activity in response to tumor growth could benefit from a systems perspective built upon mathematical modeling. This study develops a modeling system to help evaluate the effects of macrophage and T-lymphocyte interactions on tumor growth. The system enables simulating the combined cytotoxic and tumor-promoting interactions of these two immune cell populations in a vascularized organ microenvironment, such as in liver metastases. A hypothetical immunotherapeutic strategy is simulated to increase the number of tumor-suppressive (M1-phenotype) vs. tumor-promoting (M2-phenotype) macrophages to gauge their effects on CD8+ T-cells and CD4+ T-helper cells, which in turn affect the macrophage functions. The results highlight the dynamic interactions between macrophages and T-lymphocytes in the tumor microenvironment and show that with the chosen set of parameter values, the overall cytotoxic effect from macrophages and T-lymphocytes obtained by driving the M1:M2 ratio higher could saturate and fail to achieve tumor regression. Further expansion of this modeling platform to include additional tumor-immune cell interactions, coupled with parameters representing particular tumor characteristics, could enable systematic evaluation of immunotherapeutic strategies tailored to patient-tumor specific conditions, including metastatic disease.

Mathematical investigation of innate immune responses to lung cancer: The role of macrophages with mixed phenotypes

Macrophages' role in the evolution of solid tumours is a well accepted fact, with the M1-like macrophages having an anti-tumour role and the M2-like macrophages having a pro-tumour role. Despite the fact that some clinical studies on lung tumours have emphasised also the presence of macrophages with mixed M1 and M2 phenotypes in addition to macrophages with distinct phenotypes, the majority of studies still use the distinct M1-M2 classification to predict the evolution of tumours and patient survival. In this theoretical study we use a mathematical modelling and computational approach to investigate the role of macrophages with mixed phenotype on growth/control/elimination of lung tumours. We show that tumour control in the presence of M2→M1 re-polarising treatments is mainly the result of macrophages with mixed phenotypes (due to the assumption of short half-life of M1-like macrophages). We also show that the half-life of various macrophage phenotypes (distinct M1 or mixed M1/M2 phenotypes) impacts the outcome of various therapeutic strategies targeting tumour-associated macrophages. All these results suggest the need for a better experimental understanding of the kinetics of macrophages inside solid tumours.

CLEC3B as a Potential Prognostic Biomarker in Hepatocellular Carcinoma

C-Type Lectin Domain Family 3 Member B (CLEC3B) encodes proteins associated with tumor invasion and metastasis. However, the interrelation between CLEC3B gene expression, tumor immunity, and prognosis of patients with hepatocellular carcinoma (HCC) is unclear. This study was conducted to investigate the prognostic potential of CLEC3B and its association with tumor tissue infiltration markers. CLEC3B expression was examined using the TIMER and Oncomine databases, with its prognostic potential assessed using the GEPIA and Kaplan–Meier plotter databases. The relationship between CLEC3B and tumor immune cell infiltration biomarkers was analyzed using TIMER. Here, we revealed that CLEC3B expression was decreased in HCC and was correlated with a poor survival rate in patients with HCC. Additionally, the expression of CLEC3B was negatively correlated with differential immune cell infiltration and various immune biomarkers. These results indicate a potential mechanism by which the expression of CLEC3B might adjust tumor immunity by modulating the infiltration of HCC immune cells. Our study demonstrated that CLEC3B could be a potential prognostic biomarker and might be involved in tumor immune cell infiltration in HCC.

Effect of Liposome-Encapsulated Zoledronic Acid on Microenvironment of Hepatocellular Carcinoma May Depend on the Ratio Between M1 and M2 Polarized Macrophages

We studied the effect of zoledronic acid encapsulated into liposomes (L-ZOL) on tumorassociated macrophages in the stroma of hepatocellular carcinoma xenograft. Liposomes were prepared from 1,2-dipalmitoyl-sn-glycero-3-phosphocholine, 1,2-dipalmitoyl-snglycero-3-phospho-sn-1-glycerol, and 1,2-distearoyl-sn-glycero-3-phosphoethanolamine-N-[carboxy(polyethylene glycol)-2000] using thin film method and loaded with zoledronic acid. It was shown that L-ZOL promoted apoptosis of RAW264.7 cells, eliminate much more protumoral M2 macrophages than antitumoral M1 macrophages in the tumor xenograft, and did not significantly reduce the size of xenograft in 6 days. Thus, the effect of treatment depends on the ratio between antitumoral M1 and protumoral M2 polarized macrophages in the tumor.

Role of Tumor-Associated Myeloid Cells in Breast Cancer

Stromal immune cells constitute the tumor microenvironment. These immune cell subsets include myeloid cells, the so-called tumor-associated myeloid cells (TAMCs), which are of two types: tumor-associated macrophages (TAMs) and myeloid-derived suppressor cells (MDSCs). Breast tumors, particularly those in human epidermal growth factor receptor 2 (HER-2)-positive breast cancer and triple-negative breast cancer, are solid tumors containing immune cell stroma. TAMCs drive breast cancer progression via immune mediated, nonimmune-mediated, and metabolic interactions, thus serving as a potential therapeutic target for breast cancer. TAMC-associated breast cancer treatment approaches potentially involve the inhibition of TAM recruitment, modulation of TAM polarization/differentiation, reduction of TAM products, elimination of MDSCs, and reduction of MDSC products. Furthermore, TAMCs can enhance or restore immune responses during cancer immunotherapy. This review describes the role of TAMs and MDSCs in breast cancer and elucidates the clinical implications of TAMs and MDSCs as potential targets for breast cancer treatment.

Computational models of melanoma

Genes, proteins, or cells influence each other and consequently create patterns, which can be increasingly better observed by experimental biology and medicine. Thereby, descriptive methods of statistics and bioinformatics sharpen and structure our perception. However, additionally considering the interconnectivity between biological elements promises a deeper and more coherent understanding of melanoma. For instance, integrative network-based tools and well-grounded inductive in silico research reveal disease mechanisms, stratify patients, and support treatment individualization. This review gives an overview of different modeling techniques beyond statistics, shows how different strategies align with the respective medical biology, and identifies possible areas of new computational melanoma research.

CD86+/CD206+ tumor-associated macrophages predict prognosis of patients with intrahepatic cholangiocarcinoma

Background

As the main cellular ingredients of tumor microenvironment, tumor-associated macrophages (TAMs) play a vital role in tumor development and progression. Recent studies have suggested that TAMs are sensitive and specific prognostic factors in numerous cancers. The primary purpose of this study is to determine the prognostic significance of TAMs in intrahepatic cholangiocarcinoma (ICC).

Methods

Immunohistochemical staining of CD68, CD86 and CD206 were performed in tissue microarrays containing 322 patients, who underwent surgical resection and were pathologically diagnosed with ICC. The prognostic value of CD68, CD86 and CD206 were evaluated by Kaplan–Meier analysis (log-rank test) and nomogram models.

Results

We demonstrated that the CD86⁺/CD206⁺ TAMs model was an independent prognostic index for ICC patients. Patients with low CD86⁺ TAMs and high CD206⁺ TAMs infiltration had a markedly worse prognosis and increased risk of post-operative recurrence when compared to high CD86⁺ TAMs and low CD206⁺ TAMs intratumoral infiltration. Furthermore, subgroup analysis indicated that the CD86⁺/CD206⁺ TAMs model predicted prognosis of ICC patients more powerfully than single macrophage immunomarker. Interestingly, the CD86⁺/CD206⁺ TAMs model could further distinguish prognosis of CA-199 negative ICC patients, who were generally presumed to have a more favorable outcome. In order to further perfect the prognostic value of the CD86⁺/CD206⁺ TAMs model, we constructed and validated a postoperative nomogram to predict overall survival and recurrence-free survival time in ICC patients.

Conclusions

These findings indicate that the CD86⁺/CD206⁺ TAMs model possess potential value as a novel prognostic indicator for ICC patients.

A mathematical model for the immune-mediated theory of metastasis

Accumulating experimental and clinical evidence suggest that the immune response to cancer is not exclusively anti-tumor. Indeed, the pro-tumor roles of the immune system  -  as suppliers of growth and pro-angiogenic factors or defenses against cytotoxic immune attacks, for example  -  have been long appreciated, but relatively few theoretical works have considered their effects. Inspired by the recently proposed "immune-mediated" theory of metastasis, we develop a mathematical model for tumor-immune interactions at two anatomically distant sites, which includes both anti- and pro-tumor immune effects, and the experimentally observed tumor-induced phenotypic plasticity of immune cells (tumor "education" of the immune cells). Upon confrontation of our model to experimental data, we use it to evaluate the implications of the immune-mediated theory of metastasis. We find that tumor education of immune cells may explain the relatively poor performance of immunotherapies, and that many metastatic phenomena, including metastatic blow-up, dormancy, and metastasis to sites of injury, can be explained by the immune-mediated theory of metastasis. Our results suggest that further work is warranted to fully elucidate the pro-tumor effects of the immune system in metastatic cancer.

Investigating Macrophages Plasticity Following Tumour-Immune Interactions During Oncolytic Therapies

Over the last few years, oncolytic virus therapy has been recognised as a promising approach in cancer treatment, due to the potential of these viruses to induce systemic anti-tumour immunity and selectively killing tumour cells. However, the effectiveness of these viruses depends significantly on their interactions with the host immune responses, both innate (e.g., macrophages, which accumulate in high numbers inside solid tumours) and adaptive (e.g., \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\hbox {CD8}^{+}$$\end{document}CD8+ T cells). In this article, we consider a mathematical approach to investigate the possible outcomes of the complex interactions between two extreme types of macrophages (M1 and M2 cells), effector \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\hbox {CD8}^{+}$$\end{document}CD8+ T cells and an oncolytic Vesicular Stomatitis Virus (VSV), on the growth/elimination of B16F10 melanoma. We discuss, in terms of VSV, \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\hbox {CD8}^{+}$$\end{document}CD8+ and macrophages levels, two different types of immune responses which could ensure tumour control and eventual elimination. We show that both innate and adaptive anti-tumour immune responses, as well as the oncolytic virus, could be very important in delaying tumour relapse and eventually eliminating the tumour. Overall this study supports the use mathematical modelling to increase our understanding of the complex immune interaction following oncolytic virotherapies. However, the complexity of the model combined with a lack of sufficient data for model parametrisation has an impact on the possibility of making quantitative predictions.

Quantifying Cancer Epithelial-Mesenchymal Plasticity and its Association with Stemness and Immune Response

Cancer cells can acquire a spectrum of stable hybrid epithelial/mesenchymal (E/M) states during epithelial-mesenchymal transition (EMT). Cells in these hybrid E/M phenotypes often combine epithelial and mesenchymal features and tend to migrate collectively commonly as small clusters. Such collectively migrating cancer cells play a pivotal role in seeding metastases and their presence in cancer patients indicates an adverse prognostic factor. Moreover, cancer cells in hybrid E/M phenotypes tend to be more associated with stemness which endows them with tumor-initiation ability and therapy resistance. Most recently, cells undergoing EMT have been shown to promote immune suppression for better survival. A systematic understanding of the emergence of hybrid E/M phenotypes and the connection of EMT with stemness and immune suppression would contribute to more effective therapeutic strategies. In this review, we first discuss recent efforts combining theoretical and experimental approaches to elucidate mechanisms underlying EMT multi-stability (i.e., the existence of multiple stable phenotypes during EMT) and the properties of hybrid E/M phenotypes. Following we discuss non-cell-autonomous regulation of EMT by cell cooperation and extracellular matrix. Afterwards, we discuss various metrics that can be used to quantify EMT spectrum. We further describe possible mechanisms underlying the formation of clusters of circulating tumor cells. Last but not least, we summarize recent systems biology analysis of the role of EMT in the acquisition of stemness and immune suppression.

Coculture with monocytes/macrophages modulates osteogenic differentiation of adipose-derived mesenchymal stromal cells on poly(lactic-co-glycolic) acid/polycaprolactone scaffolds

The effects of immune cells, in particular macrophages, on the behaviour of mesenchymal stromal cells (MSCs) have recently gained much attention for MSCs‐based tissue‐engineered constructs. This study aimed to evaluate the effect of monocytes/macrophages on the osteogenic differentiation of adipose‐derived mesenchymal stromal cells (ADMSCs) in three‐dimensional (3D) cocultures. For this, we cocultured THP‐1 monocytes, M1 macrophages, or M2 macrophages with ADMSCs on 3D poly(lactic‐co‐glycolic) acid (PLGA)/polycaprolactone (PCL) scaffolds using osteogenic medium for up to 42 days. We found that osteogenic differentiation of ADMSCs was inhibited by monocytes and both macrophage subtypes in 3D scaffolds. Furthermore, coculture of monocytes/macrophages with ADMSCs resulted in downregulated secretion of oncostatin M (OSM) and bone morphogenetic protein 2 (BMP‐2) and inhibited expression of osteogenic markers alkaline phosphatase (ALP), bone sialoprotein (BSP), and runt‐related transcription factor 2 (RUNX2). Compared with both macrophage subtypes, monocytes inhibited osteogenic differentiation of ADMSCs more significantly. These data suggest that the mutual interactions between monocytes/macrophages and ADMSCs negatively affect MSC osteogenic differentiation and thus possibly bone healing capacity, which highlights the importance of the micro‐environment in influencing cell‐based constructs to treat bone defects and the potential to improve their performance by resolving the inflammation ahead of treatment.

Effect of silkworm peptide on inducting M1 type polarization and Th1 activation via TLR2-induced MyD88-dependent pathway

The aim of this study was to explore immune activity and molecular mechanism of silkworm peptide. The cell subsets induced by silkworm peptides were detected by flow cytometry. The IFN-γ and IL-4 levels in CD4+ cells were measured by ELISA. TLR2 mRNA expression in mouse CD4+ T cells was detected by qRT-PCR. Western blot was used to detect the protein expression levels of MyD88 and p-IκB. The growth rate of Lewis lung cancer xenografts in mice of the medium-dose group was significantly reduced, and the tumor volume was significantly smaller than that of the control group on the 14th day. The relative vitality values of spleen lymphocytes in the medium-dose and high-dose groups were higher than the control group. The IFN-γ levels in the medium-dose and high-dose groups were significantly higher than the control group. The levels of IL-4 were no significant change among different groups. Compared with the control group, different doses of silkworm peptide groups could increase the levels of NO, IL-6, IL-12, and IL-1β. Compared with the control group, the protein expression levels of MyD88 and p-IκB in 10 μg/ml group and 20 μg/ml groups were significantly increased compared with the control group. Silkworm peptide could induce Th1 activation and M1 type polarization, which was dose-dependent and was relative to the effect of silkworm peptide on inhibiting tumor growth. Silkworm peptide could directly induce M1 type polarization and Th1 activation via TLR2-induced MyD88-dependent pathway in vitro.

First-in-class immune-modulating small molecule Icaritin in advanced hepatocellular carcinoma: preliminary results of safety, durable survival and immune biomarkers

Background

With poor prognosis and limited treatment options for advanced hepatocellular carcinoma (HCC), development of novel therapeutic agents is urgently needed. This single-arm phase I study sought to assess the safety and preliminary efficacy of icaritin in human as a potential oral immunotherapy in addition to the immune-checkpoint inhibitors.

Methods

Eligible advanced HCC patients with Child-Pugh Class A or B were administered with a fixed oral dose of icaritin at either 600 or 800 mg b.i.d. The primary endpoint was safety, and the secondary endpoints included time-to-progression (TTP), overall survival (OS) and the clinical benefit rate (CBR). Icaritin treatment induced immune biomarkers and immune-modulating activities in myeloid cells were also explored.

Results

No drug-related adverse events ≥ Grade 3 were observed in all 20 enrolled HCC patients. Among the 15 evaluable patients, 7 (46.7%) achieved clinical benefit, representing one partial response (PR, 6.7%) and 6 stable disease (SD, 40%). The median TTP was 141 days (range: 20-343 days), and the median OS was 192 days (range: 33-1036 days). Durable survival was observed in PR/SD patients with a median OS of 488 days (range: 72-773). TTP was significantly associated with the dynamic changes of peripheral neutrophils (p = 0.0067) and lymphocytes (p = 0.0337). Icaritin treatment induced changes in immune biomarkers-and immune-suppressive myeloid cells were observed.

Conclusions

Icaritin demonstrated safety profiles and preliminary durable survival benefits in advanced HCC patients, which were correlated with its immune-modulation activities and immune biomarkers. These results suggested the potential of icaritin as a novel oral immunotherapy for advanced HCC in addition to antibody-based PD-1/PD-L1 blockade therapies.

Trial registration

Clinicaltrial.govidentifier.

NCT02496949 (retrospectively registered, July 14, 2015).

Electronic supplementary material

The online version of this article (10.1186/s12885-019-5471-1) contains supplementary material, which is available to authorized users.

Mathematical modeling of tumor-immune cell interactions

The anti-tumor activity of the immune system is increasingly recognized as critical for the mounting of a prolonged and effective response to cancer growth and invasion, and for preventing recurrence following resection or treatment. As the knowledge of tumor-immune cell interactions has advanced, experimental investigation has been complemented by mathematical modeling with the goal to quantify and predict these interactions. This succinct review offers an overview of recent tumor-immune continuum modeling approaches, highlighting spatial models. The focus is on work published in the past decade, incorporating one or more immune cell types and evaluating immune cell effects on tumor progression. Due to their relevance to cancer, the following immune cells and their combinations are described: macrophages, Cytotoxic T Lymphocytes, Natural Killer cells, dendritic cells, T regulatory cells, and CD4+ T helper cells. Although important insight has been gained from a mathematical modeling perspective, the development of models incorporating patient-specific data remains an important goal yet to be realized for potential clinical benefit.

Non-local Parabolic and Hyperbolic Models for Cell Polarisation in Heterogeneous Cancer Cell Populations

Tumours consist of heterogeneous populations of cells. The sub-populations can have different features, including cell motility, proliferation and metastatic potential. The interactions between clonal sub-populations are complex, from stable coexistence to dominant behaviours. The cell–cell interactions, i.e. attraction, repulsion and alignment, processes critical in cancer invasion and metastasis, can be influenced by the mutation of cancer cells. In this study, we develop a mathematical model describing cancer cell invasion and movement for two polarised cancer cell populations with different levels of mutation. We consider a system of non-local hyperbolic equations that incorporate cell–cell interactions in the speed and the turning behaviour of cancer cells, and take a formal parabolic limit to transform this model into a non-local parabolic model. We then investigate the possibility of aggregations to form, and perform numerical simulations for both hyperbolic and parabolic models, comparing the patterns obtained for these models.

A structural methodology for modeling immune-tumor interactions including pro- and anti-tumor factors for clinical applications

The immune system turns out to have both stimulatory and inhibitory factors influencing on tumor growth. In recent years, the pro-tumor role of immunity factors such as regulatory T cells and TGF-β cytokines has specially been considered in mathematical modeling of tumor-immune interactions. This paper presents a novel structural methodology for reviewing these models and classifies them into five subgroups on the basis of immune factors included. By using our experimental data due to immunotherapy experimentation in mice, these five modeling groups are evaluated and scored. The results show that a model with a small number of variables and coefficients performs efficiently in predicting the tumor-immune system interactions. Though immunology theorems suggest to employ a larger number of variables and coefficients, more complicated models are here shown to be inefficient due to redundant parallel pathways. So, these models are trapped in local minima and restricted in prediction capability. This paper investigates the mathematical models that were previously developed and proposes variables and pathways that are essential for modeling tumor-immune. Using these variables and pathways, a minimal structure for modeling tumor-immune interactions is proposed for future studies.

Antitumor immunostimulatory activity of polysaccharides from Panax japonicus C. A. Mey: Roles of their effects on CD4+ T cells and tumor associated macrophages

In this study, chemical properties of polysaccharides from rhizomes of Panax japonicus C. A. Mey (PSPJ) were investigated and the antitumor immunostimulatory activity of PSPJ was assessed in mice bearing H22 hepatoma cells. Chemical properties of PSPJ were determined by GC, FT-IR, ¹H NMR and ¹³C NMR analysis. Furthermore, we showed that PSPJ repressed H22 tumor growth in vivo with undetectable toxic effects on tumor-bearing mice. PSPJ upregulated host thymus/spleen indexes and ConA/LPS-induced splenocyte proliferation. Cytotoxic activities of natural killer and CD8+ T cells against H22 hepatoma cells were also elevated. Tumor transplantation led to substantial apoptosis of CD4+ T cells and dysregulation of the cytokine profile secreted by CD4+ T cells. These abnormalities were alleviated by PSPJ in a dose-dependent manner. In tumor-associated macrophages (TAMs), PSPJ reduced the production of immunosuppressive factors such as TGF-β, IL-10 and PEG2. In addition, M2-like polarization of TAMs was also considerably declined in response to PSPJ. Our findings clearly demonstrated the antitumor immunostimulatory activity of PSPJ and supported considering PSPJ as an adjuvant reagent in clinical treatment of malignant diseases.

Modeling Macrophage Polarization and Its Effect on Cancer Treatment Success

Positive feedback loops drive immune cell polarization toward a pro-tumor phenotype that accentuates immunosuppression and tumor angiogenesis. This phenotypic switch leads to the escape of cancer cells from immune destruction. These positive feedback loops are generated by cytokines such as TGF-β, Interleukin-10 and Interleukin-4, which are responsible for the polarization of monocytes and M1 macrophages into pro-tumor M2 macrophages, and the polarization of naive helper T cells intopro-tumor Th2 cells. In this article, we present a deterministic ordinary differential equation (ODE) model that includes key cellular interactions and cytokine signaling pathways that lead to immune cell polarization in the tumor microenvironment. The model was used to simulate various cancer treatments in silico. We identified combination therapies that consist of M1 macrophages or Th1 helper cells, coupled with an anti-angiogenic treatment, that are robust with respect to immune response strength, initial tumor size and treatment resistance. We also identified IL-4 and IL-10 as the targets that should be neutralized in order to make these combination treatments robust with respect to immune cell polarization. The model simulations confirmed a hypothesis based on published experimental evidence that a polarization into the M1 and Th1 phenotypes to increase the M1-to-M2 and Th1-to-Th2 ratios plays a significant role in treatment success. Our results highlight the importance of immune cell reprogramming as a viable strategy to eradicate a highly vascularized tumor when the strength of the immune response is characteristically weak and cell polarization to the pro-tumor phenotype has occurred.

C-Type lectin receptor(s)-targeted nanoliposomes: an intelligent approach for effective cancer immunotherapy

Aim: The purpose of present approach is to target C-Type lectin (CTL) receptors for preferential uptake by the macrophages/dendritic cells and improving the cross-presentation of ovalbumin. Materials & methods: Conventional and engineered nanoliposomes (MPNLs) were fabricated and extensively characterized. The nanoliposome(s) was spherical in shape; and their ζ potential, size and ovalbumin loading efficiency were recorded to be 268 ± 4.15 nm, 23.4 ± 0.35 mV, 46.65 ± 1.84%, respectively. Results: The findings demonstrate that MPNLs significantly improved the antigen uptake and its cross-presentation to evoke Th CD8+ cell-mediated cellular immunity. Conclusion: In a nutshell, this engineered approach mannose surface modification for active targeting to dendritic cells/macrophages and pH-dependent quick endosomal antigen release is a promising system for efficient cancer immunotherapy.

Modeling the Dichotomy of the Immune Response to Cancer: Cytotoxic Effects and Tumor-Promoting Inflammation

Although the immune response is often regarded as acting to suppress tumor growth, it is now clear that it can be both stimulatory and inhibitory. The interplay between these competing influences has complex implications for tumor development, cancer dormancy, and immunotherapies. In fact, early immunotherapy failures were partly due to a lack in understanding of the nonlinear growth dynamics these competing immune actions may cause. To study this biological phenomenon theoretically, we construct a minimally parameterized framework that incorporates all aspects of the immune response. We combine the effects of all immune cell types, general principles of self-limited logistic growth, and the physical process of inflammation into one quantitative setting. Simulations suggest that while there are pro-tumor or antitumor immunogenic responses characterized by larger or smaller final tumor volumes, respectively, each response involves an initial period where tumor growth is stimulated beyond that of growth without an immune response. The mathematical description is non-identifiable which allows an ensemble of parameter sets to capture inherent biological variability in tumor growth that can significantly alter tumor-immune dynamics and thus treatment success rates. The ability of this model to predict non-intuitive yet clinically observed patterns of immunomodulated tumor growth suggests that it may provide a means to help classify patient response dynamics to aid identification of appropriate treatments exploiting immune response to improve tumor suppression, including the potential attainment of an immune-induced dormant state.

Pro-tumor activities of macrophages in the progression of melanoma

Macrophages are located in essentially all tissues due to their “janitor” function. Macrophages can exert either anti- or pro-tumor activities depending upon the specific tumor microenvironment they inhabit. Substantial evidence indicates that macrophages, owing to their plasticity, can be reeducated to adopt a protumoral phenotype within a tumor microenvironment through the help of growth factors in the microenvironment and intercellular interactions. As the lethality of malignant melanoma is due to its aggressive capacity for metastasis and resistance to therapy, considerable effort has gone toward treatment of metastatic melanoma. In the present review, we focus on the pro-tumor activities of macrophages in melanoma. Based upon the information presented in this review it is anticipated that new therapies will soon be developed that target pro-tumor activities of macrophages for use in the treatment of melanoma.

CD86⁺/CD206⁺, Diametrically Polarized Tumor-Associated Macrophages, Predict Hepatocellular Carcinoma Patient Prognosis

Tumor-associated macrophages (TAMs), the most abundant infiltrating immune cells in tumor microenvironment, have distinct functions in hepatocellular carcinoma (HCC) progression. CD68⁺ TAMs represent multiple polarized immune cells mainly containing CD86⁺ antitumoral M1 macrophages and CD206⁺ protumoral M2 macrophages. TAMs expression and density were assessed by immunohistochemical staining of CD68, CD86, and CD206 in tissue microarrays from 253 HCC patients. Clinicopathologic features and prognostic value of these markers were evaluated. We found that CD68⁺ TAMs were not associated with clinicopathologic characteristics and prognosis in HCC. Low presence of CD86⁺ TAMs and high presence of CD206⁺ TAMs were markedly correlated with aggressive tumor phenotypes, such as multiple tumor number and advanced tumor-node-metastasis (TNM) stage; and were associated with poor overall survival (OS) (p = 0.027 and p = 0.024, respectively) and increased time to recurrence (TTR) (p = 0.037 and p = 0.031, respectively). In addition, combined analysis of CD86 and CD206 provided a better indicator for OS (p = 0.011) and TTR (p = 0.024) in HCC than individual analysis of CD86 and CD206. Moreover, CD86⁺/CD206⁺ TAMs predictive model also had significant prognosis value in α-fetoprotein (AFP)-negative patients (OS: p = 0.002, TTR: p = 0.005). Thus, these results suggest that combined analysis of immune biomarkers CD86 and CD206 could be a promising HCC prognostic biomarker.