Modeling the inhibition of breast cancer growth by GM-CSF

Paracrine Activation of STAT3 Drives GM-CSF Expression in Breast Carcinoma Cells, Generating a Symbiotic Signaling Network with Breast Carcinoma-Associated Fibroblasts

This study evaluated the paracrine signaling between breast carcinoma-associated fibroblasts (CAFs) and breast cancer (BCa) cells. Resolving cell-cell communication in the BCa tumor microenvironment (TME) will aid the development of new therapeutics. Here, we utilized our patented TAME (tissue architecture and microenvironment engineering) 3D culture microphysiological system, which is a suitable pathomimetic avatar for the study of the BCa TME. We cultured in 3D BCa cells and CAFs either alone or together in cocultures and found that when cocultured, CAFs enhanced the invasive characteristics of tumor cells, as shown by increased proliferation and spread of tumor cells into the surrounding matrix. Secretome analysis from 3D cultures revealed a relatively high secretion of IL-6 by CAFs. A marked increase in the secretion of granulocyte macrophage-colony stimulating factor (GM-CSF) when carcinoma cells and CAFs were in coculture was also observed. We theorized that the CAF-secreted IL-6 functions in a paracrine manner to induce GM-CSF expression and secretion from carcinoma cells. This was confirmed by evaluating the activation of STAT3 and gene expression of GM-CSF in carcinoma cells exposed to CAF-conditioned media (CAF-CM). In addition, the treatment of CAFs with BCa cell-CM yielded a brief upregulation of GM-CSF followed by a marked decrease, indicating a tightly regulated control of GM-CSF in CAFs. Secretion of IL-6 from CAFs drives the activation of STAT3 in BCa cells, which in turn drives the expression and secretion of GM-CSF. As a result, CAFs exposed to BCa cell-secreted GM-CSF upregulate inflammation-associated genes such as IL-6, IL-6R and IL-8, thereby forming a positive feedback loop. We propose that the tight regulation of GM-CSF in CAFs may be a novel regulatory pathway to target for disrupting the CAF:BCa cell symbiotic relationship. These data provide yet another piece of the cell-cell communication network governing the BCa TME.

Predicting lung cancer's metastats' locations using bioclinical model

Background

Lung cancer is a global leading cause of cancer-related deaths, and metastasis profoundly influences treatment outcomes. The limitations of conventional imaging in detecting small metastases highlight the crucial need for advanced diagnostic approaches.

Methods

This study developed a bioclinical model using three-dimensional CT scans to predict the spatial spread of lung cancer metastasis. Utilizing a three-layer biological model, we identified regions with a high probability of metastasis colonization and validated the model on real-world data from 10 patients.

Findings

The validated bioclinical model demonstrated a promising 74% accuracy in predicting metastasis locations, showcasing the potential of integrating biophysical and machine learning models. These findings underscore the significance of a more comprehensive approach to lung cancer diagnosis and treatment.

Interpretation

This study's integration of biophysical and machine learning models contributes to advancing lung cancer diagnosis and treatment, providing nuanced insights for informed decision-making.

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.

Transmissible ER stress between macrophages and tumor cells configures tumor microenvironment

Endoplasmic reticulum (ER) stress initiates the unfolded protein response (UPR) and is decisive for tumor cell growth and tumor microenvironment (TME) maintenance. Tumor cells persistently undergo ER stress and could transmit it to the neighboring macrophages and surroundings. Tumor infiltrating macrophages can also adapt to the microenvironment variations to fulfill their highly energy-demanding and biological functions via ER stress. However, whether the different macrophage populations differentially sense ER stress and transmit ER stress to surrounding tumor cells has not yet been elucidated. Here, we aimed to investigate the role of transmissible ER stress, a novel regulator of intercellular communication in the TME. Murine bone marrow-derived macrophage (BMDM) can be polarized toward distinct functional endpoints termed classical (M1) and alternative (M2) activation, and their polarization status has been shown to be tightly correlated with their functional significance. We showed that tumor cells could receive the transmissible ER stress from two differentially polarized macrophage populations with different extent of ER stress activation. The proinflammatory M1-like macrophages respond to ER stress with less extent, however they could transmit more ER stress to tumor cells. Moreover, by analyzing the secreted components of two ER-stressed macrophage populations, we identified certain damage-associated molecular patterns (DAMPs), including S100A8 and S100A9, which are dominantly secreted by M1-like macrophages could lead to significant recipient tumor cells death in synergy with transferred ER stress.

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.

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.

CD73 induces GM-CSF/MDSC-mediated suppression of T cells to accelerate pancreatic cancer pathogenesis

Metabolic alterations regulate cancer aggressiveness and immune responses. Given the poor response of pancreatic ductal adenocarcinoma (PDAC) to conventional immunotherapies, we investigated the link between metabolic alterations and immunosuppression. Our metabolic enzyme screen indicated that elevated expression of CD73, an ecto-5'-nucleotidase that generates adenosine, correlates with increased aggressiveness. Correspondingly, we observed increased interstitial adenosine levels in tumors from spontaneous PDAC mouse models. Diminishing CD73 by genetic manipulations ablated in vivo tumor growth, and decreased myeloid-derived suppressor cells (MDSC) in orthotopic mouse models of PDAC. A high-throughput cytokine profiling demonstrated decreased GM-CSF in mice implanted with CD73 knockdowns. Furthermore, we noted increased IFN-γ expression by intratumoral CD4+ and CD8+ T cells in pancreatic tumors with CD73 knockdowns. Depletion of CD4+ T cells, but not CD8+ T cells abrogated the beneficial effects of decreased CD73. We also observed that splenic MDSCs from Nt5e knockdown tumor-bearing mice were incompetent in suppressing T cell activation in the ex vivo assays. Replenishing GM-CSF restored tumor growth in Nt5e knockout tumors, which was reverted by MDSC depletion. Finally, anti-CD73 antibody treatment significantly improved gemcitabine efficacy in orthotopic models. Thus, targeting the adenosine axis presents a novel therapeutic opportunity for improving the anti-tumoral immune response against PDAC.

Mathematical Modeling Shows That the Response of a Solid Tumor to Antiangiogenic Therapy Depends on the Type of Growth

It has been hypothesized that solid tumors with invasive type of growth should possess intrinsic resistance to antiangiogenic therapy, which is aimed at cessation of the formation of new blood vessels and subsequent shortage of nutrient inflow to the tumor. In order to investigate this effect, a continuous mathematical model of tumor growth is developed, which considers variables of tumor cells, necrotic tissue, capillaries, and glucose as the crucial nutrient. The model accounts for the intrinsic motility of tumor cells and for the convective motion, arising due to their proliferation, thus allowing considering two types of tumor growth—invasive and compact—as well as their combination. Analytical estimations of tumor growth speed are obtained for compact and invasive tumors. They suggest that antiangiogenic therapy may provide a several times decrease of compact tumor growth speed, but the decrease of growth speed for invasive tumors should be only modest. These estimations are confirmed by numerical simulations, which further allow evaluating the effect of antiangiogenic therapy on tumors with mixed growth type and highlight the non-additive character of the two types of growth.

How to schedule VEGF and PD-1 inhibitors in combination cancer therapy?

BACKGROUND

One of the questions in the design of cancer clinical trials with combination of two drugs is in which order to administer the drugs. This is an important question, especially in the case where one agent may interfere with the effectiveness of the other agent.

RESULTS

In the present paper we develop a mathematical model to address this scheduling question in a specific case where one of the drugs is anti-VEGF, which is known to affect the perfusion of other drugs. As a second drug we take anti-PD-1. Both drugs are known to increase the activation of anticancer T cells. Our simulations show that in the case where anti-VEGF reduces the perfusion, a non-overlapping schedule is significantly more effective than a simultaneous injection of the two drugs, and it is somewhat more beneficial to inject anti-PD-1 first.

CONCLUSION

The method and results of the paper can be extended to other combinations, and they could play an important role in the design of clinical trials with combination therapy, where scheduling strategies may significantly affect the outcome.

Human Mast Cells From Adipose Tissue Target and Induce Apoptosis of Breast Cancer Cells

Mast cells (MC) are important immune sentinels found in most tissue and widely recognized for their role as mediators of Type I hypersensitivity. However, they also secrete anti-cancer mediators such as tumor necrosis factor alpha (TNF-α) and granulocyte-macrophage colony-stimulating factor (GM-CSF). The purpose of this study was to investigate adipose tissue as a new source of MC in quantities that could be used to study MC biology focusing on their ability to bind to and kill breast cancer cells. We tested several cell culture media previously demonstrated to induce MC differentiation. We report here the generation of functional human MC from adipose tissue. The adipose-derived mast cells (ADMC) are phenotypically and functionally similar to connective tissue expressing tryptase, chymase, c-kit, and FcεRI and capable of degranulating after cross-linking of FcεRI. The ADMC, sensitized with anti-HER2/neu IgE antibodies with human constant regions (trastuzumab IgE and/or C6MH3-B1 IgE), bound to and released MC mediators when incubated with HER2/neu-positive human breast cancer cells (SK-BR-3 and BT-474). Importantly, the HER2/neu IgE-sensitized ADMC induced breast cancer cell (SK-BR-3) death through apoptosis. Breast cancer cell apoptosis was observed after the addition of cell-free supernatants containing mediators released from FcεRI-challenged ADMC. Apoptosis was significantly reduced when TNF-α blocking antibodies were added to the media. Adipose tissue represents a source MC that could be used for multiple research purposes and potentially as a cell-mediated cancer immunotherapy through the expansion of autologous (or allogeneic) MC that can be targeted to tumors through IgE antibodies recognizing tumor specific antigens.

Transient alleviation of tumor hypoxia during first days of antiangiogenic therapy as a result of therapy-induced alterations in nutrient supply and tumor metabolism - Analysis by mathematical modeling

A number of experiments on mouse tumor models, as well as certain clinical data, have demonstrated, that antiangiogenic therapy can lead to transient improvement in tumor oxygenation, that allows to increase efficiency of following radiotherapy. In the majority of works, this phenomenon has been explained by enhanced tumor perfusion due to normalization of capillaries' structure, that results in elevated oxygen inflow in tumor. However, changes in tumor perfusion often haven't been directly measured in relevant works, moreover, antiangiogenic therapy has been proven to have ambiguous effect on tumor perfusion both in mouse tumor models and in clinics. Herein, we suggest that elevation of blood perfusion may be not the only reason for transient alleviation of tumor hypoxia, and that it may manifest itself even under unchanged tumor blood flow. We propose that it may be as well caused by the decrease in tumor oxygen consumption rate (OCR) due to the reduction of tumor proliferation level, caused by nutrient shortage in result of antiangiogenic treatment. We provide detailed explanation of this hypothesis and visualize it using a specially developed mathematical model, which takes into account basic features of tumor growth and antiangiogenic therapy. We investigate the influence of the model parameters on oxygen dynamics; demonstrate, that transient alleviation of tumor hypoxia occurs in a fairly wide range of physiologically justified values of parameters; and point out the major factors, that determine oxygen dynamics during antiangiogenic therapy.

Modeling combination therapy for breast cancer with BET and immune checkpoint inhibitors

CTLA-4 is an immune checkpoint expressed on active anticancer T cells. When it combines with its ligand B7 on dendritic cells, it inhibits the activity of the T cells. The Bromo- and Extra-Terminal (BET) protein family includes proteins that regulate the expression of key oncogenes and antiapoptotic proteins. BET inhibitor (BETi) has been shown to reduce the expression of MYC by suppressing its transcription factors and to down-regulate the hypoxic transcriptome response to VEGF-A. This paper develops a mathematical model of the treatment of cancer by combination therapy of BETi and CTLA-4 inhibitor. The model shows that the two drugs are positively correlated in the sense that the tumor volume decreases as the dose of each of the drugs is increased. The model also considers the effect of the combined therapy on levels of myeloid-derived suppressor cells (MDSCs) and the overexpression of TNF-α, which may predict gastrointestinal side effects of the combination.

A Spatio-Temporal Model of Macrophage-Mediated Drug Resistance in Glioma Immunotherapy

The emergence of drug resistance is often an inevitable obstacle that limits the long-term effectiveness of clinical cancer chemotherapeutics. Although various forms of cancer cell-intrinsic mechanisms of drug resistance have been experimentally revealed, the role and the underlying mechanism of tumor microenvironment in driving the development of acquired drug resistance remain elusive, which significantly impedes effective clinical cancer treatment. Recent experimental studies have revealed a macrophage-mediated drug resistance mechanism in which the tumor microenvironment undergoes adaptation in response to macrophage-targeted colony-stimulating factor-1 receptor (CSF1R) inhibition therapy in gliomas. In this study, we developed a spatio-temporal model to quantitatively describe the interplay between glioma cells and CSF1R inhibitor-targeted macrophages through CSF1 and IGF1 pathways. Our model was used to investigate the evolutionary kinetics of the tumor regrowth and the associated dynamic adaptation of the tumor microenvironment in response to the CSF1R inhibitor treatment. The simulation result obtained using this model was in agreement with the experimental data. The sensitivity analysis revealed the key parameters involved in the model, and their potential impacts on the model behavior were examined. Moreover, we demonstrated that the drug resistance is dose-dependent. In addition, we quantitatively evaluated the effects of combined CSFR inhibition and IGF1 receptor (IGF1R) inhibition with the goal of designing more effective therapies for gliomas. Our study provides quantitative and mechanistic insights into the microenvironmental adaptation mechanisms that operate during macrophage-targeted immunotherapy and has implications for drug dose optimization and the design of more effective combination therapies. Mol Cancer Ther; 17(4); 814-24. ©2018 AACR.

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.

Exosomal miRs in Lung Cancer: A Mathematical Model

Lung cancer, primarily non-small-cell lung cancer (NSCLC), is the leading cause of cancer deaths in the United States and worldwide. While early detection significantly improves five-year survival, there are no reliable diagnostic tools for early detection. Several exosomal microRNAs (miRs) are overexpressed in NSCLC, and have been suggested as potential biomarkers for early detection. The present paper develops a mathematical model for early stage of NSCLC with emphasis on the role of the three highest overexpressed miRs, namely miR-21, miR-205 and miR-155. Simulations of the model provide quantitative relationships between the tumor volume and the total mass of each of the above miRs in the tumor. Because of the positive correlation between these miRs in the tumor tissue and in the blood, the results of the paper may be viewed as a first step toward establishing a combination of miRs 21, 205, 155 and possibly other miRs as serum biomarkers for early detection of NSCLC.

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.

The role of the cytokines IL-27 and IL-35 in cancer

The cancer-immune interaction is a fast growing field of research in biology, where the goal is to harness the immune system to fight cancer more effectively. In the present paper we review recent work of the interaction between T cells and cancer. CD8+ T cells are activated by IL-27 cytokine and they kill tumor cells. Regulatory T cells produce IL-35 which promotes cancer cells by enhancing angiogenesis, and inhibit CD8+ T cells via TGF-β production. Hence injections of IL-27 and anti-IL-35 are both potentially anti-tumor drugs. The models presented here are based on experimental mouse experiments, and their simulations agree with these experiments. The models are used to suggest effective schedules for drug treatment.

Mathematical modeling of Interleukin-35 promoting tumor growth and angiogenesis

Interleukin-35 (IL-35), a cytokine from the Interleukin-12 cytokine family, has been considered as an anti-inflammatory cytokine which promotes tumor progression and tumor immune evasion. It has also been demonstrated that IL-35 is secreted by regulatory T cells. Recent mouse experiments have shown that IL-35 produced by cancer cells promotes tumor growth via enhancing myeloid cell accumulation and angiogenesis, and reducing the infiltration of activated CD8[Formula: see text] T cells into tumor microenvironment. In the present paper we develop a mathematical model based on these experimental results. We include in the model an anti-IL-35 drug as treatment. The extended model (with drug) is used to design protocols of anti-IL-35 injections for treatment of cancer. We find that with a fixed total amount of drug, continuous injection has better efficacy than intermittent injections in reducing the tumor load while the treatment is ongoing. We also find that the percentage of tumor reduction under anti-IL-35 treatment improves when the production of IL-35 by cancer is increased.

Mathematical modeling of interleukin-27 induction of anti-tumor T cells response

Interleukin-12 is a pro-inflammatory cytokine which promotes Th1 and cytotoxic T lymphocyte activities, such as Interferon- secretion. For this reason Interleukin-12 could be a powerful therapeutic agent for cancer treatment. However, Interleukin-12 is also excessively toxic. Interleukin-27 is an immunoregulatory cytokine from the Interleukin-12 family, but it is not as toxic as Interleukin-12. In recent years, Interleukin-27 has been considered as a potential anti-tumor agent. Recent experiments in vitro and in vivo have shown that cancer cells transfected with IL-27 activate CD8⁺ T cells to promote the secretion of anti-tumor cytokines Interleukin-10, although, at the same time, IL-27 inhibits the secretion of Interferon- by CD8⁺ T cells. In the present paper we develop a mathematical model based on these experimental results. The model involves a dynamic network which includes tumor cells, CD8⁺ T cells and cytokines Interleukin-27, Interleukin-10 and Interferon-. Simulations of the model show how Interleukin-27 promotes CD8⁺ T cells to secrete Interleukin-10 to inhibit tumor growth. On the other hand Interleukin-27 inhibits the secretion of Interferon- by CD8⁺ T cells which somewhat diminishes the inhibition of tumor growth. Our numerical results are in qualitative agreement with experimental data. We use the model to design protocols of IL-27 injections for the treatment of cancer and find that, for some special types of cancer, with a fixed total amount of drug, within a certain range, continuous injection has better efficacy than intermittent injections in reducing the tumor load while the treatment is ongoing, although the decrease in tumor load is only temporary.

Effects of two different immunotherapies on triple negative breast cancer in animal model

The ability of immune system to react specifically against tumors inspirited the study of triple negative breast cancer (TNBC) immunotherapies. Sixty spontaneous breast cancer TA2 mice were randomly divided into three groups: GM-CSF group, with therapy of granulocyte-macrophage colony-stimulating factor (GM-CSF) combined with breast cancer stem cells associated antigens and cytosine-phosphorothioate-guanine oligodeoxynucleotides (CpG-ODNs); DC-CIK group, with infusions of dendritic cells/cytokine-induced killer (DC/CIK) cells; and PBS group as controls. After therapy, the cellular immunity of mice in GM-CSF group and DC-CIK group was obviously increased, especially for GM-CSF group (P<0.05), tumor regression was obviously observed in GM-CSF group. The survival rate of mice in GM-CSF group was significantly higher compared to DC-CIK group and PBS group. These results indicated that tumor immunotherapy manifested strong killing activity against TNBC. The therapeutic effect of GM-CSF combined with antigens and CpG was better than DC-CIK cells.

The role of CD200-CD200R in tumor immune evasion

CD200 is a cell membrane protein that interacts with CD200 receptor (CD200R) of myeloid lineage cells. During tumor initiation and progression, CD200-positive tumor cells can interact with M1 and M2 macrophages through CD200-CD200R-compex, and downregulate IL-10 and IL-12 productions secreted primarily by M2 and M1 macrophages, respectively. In the tumor microenvironment, IL-10 inhibits the activation of cytotoxic T lymphocytes (CTL), while IL-12 enhances CTL activation. In this paper, we used a system approach to determine the combined effect of CD200-CD200R interaction on tumor proliferation by developing a mathematical model. We demonstrate that blocking CD200 on tumor cells may have opposite effects on tumor proliferation depending on the "affinity" of the macrophages to form the CD200-CD200R-complex with tumor cells. Our results help understanding the complexities of tumor microenvironment.

Hypoxia inducible factors-mediated inhibition of cancer by GM-CSF: a mathematical model

Under hypoxia, tumor cells, and tumor-associated macrophages produce VEGF (vascular endothelial growth factor), a signaling molecule that induces angiogenesis. The same macrophages, when treated with GM-CSF (granulocyte/macrophage colony-stimulating factor), produce sVEGFR-1 (soluble VEGF receptor-1), a soluble protein that binds with VEGF and inactivates its function. The production of VEGF by macrophages is regulated by HIF-1α (hypoxia inducible factor-1α), and the production of sVEGFR-1 is mediated by HIF-2α. Recent experiments measured the effect of inhibiting tumor growth by GM-CSF treatment in mice with HIF-1α-deficient or HIF-2α-deficient macrophages. In the present paper, we represent these experiments by a mathematical model based on a system of partial differential equations. We show that the model simulations agree with the above experiments. The model can then be used to suggest strategies for inhibiting tumor growth. For example, the model qualitatively predicts the extent to which GM-CSF treatment in combination with a small molecule inhibitor that stabilizes HIF-2α will reduce tumor volume and angiogenesis.