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

Alterations of ceramide synthesis induce PD-L1 internalization and signaling to regulate tumor metastasis and immunotherapy response

Programmed death ligand 1, PD-L1 (CD274), facilitates immune evasion and exerts pro-survival functions in cancer cells. Here, we report a mechanism whereby internalization of PD-L1 in response to alterations of bioactive lipid/ceramide metabolism by ceramide synthase 4 (CerS4) induces sonic hedgehog (Shh) and transforming growth factor β receptor signaling to enhance tumor metastasis in triple-negative breast cancers (TNBCs), exhibiting immunotherapy resistance. Mechanistically, data showed that internalized PD-L1 interacts with an RNA-binding protein, caprin-1, to stabilize Shh/TGFBR1/Wnt mRNAs to induce β-catenin signaling and TNBC growth/metastasis, consistent with increased infiltration of FoxP3+ regulatory T cells and resistance to immunotherapy. While mammary tumors developed in MMTV-PyMT/CerS4-/- were highly metastatic, targeting the Shh/PD-L1 axis using sonidegib and anti-PD-L1 antibody vastly decreased tumor growth and metastasis, consistent with the inhibition of PD-L1 internalization and Shh/Wnt signaling, restoring anti-tumor immune response. These data, validated in clinical samples and databases, provide a mechanism-based therapeutic strategy to improve immunotherapy responses in metastatic TNBCs.

Mathematical Modeling of Tumor Immune Interactions: The Role of Anti-FGFR and Anti-PD-1 in the Combination Therapy

Bladder cancer poses a significant global health burden with high incidence and recurrence rates. This study addresses the therapeutic challenges in advanced bladder cancer, focusing on the competitive mechanisms of ligand or drug binding to receptors. We developed a refined mathematical model that integrates the dynamics of tumor cells and immune responses, particularly targeting fibroblast growth factor receptor 3 (FGFR3) and immune checkpoint inhibitors (ICIs). This study contributes to understanding combination therapies by elucidating the competitive binding dynamics and quantifying the synergistic effects. The findings highlight the importance of personalized immunotherapeutic strategies, considering factors such as drug dosage, dosing schedules, and patient-specific parameters. Our model further reveals that ligand-independent activated-state receptors are the most essential drivers of tumor proliferation. Moreover, we found that PD-L1 expression rate was more important than PD-1 in driving the dynamic evolution of tumor and immune cells. The proposed mathematical model provides a comprehensive framework for unraveling the complexities of combination therapies in advanced bladder cancer. As research progresses, this multidisciplinary approach contributes valuable insights toward optimizing therapeutic strategies and advancing cancer treatment paradigms.

Polydopamine-Based Nanoparticles for Synergistic Chemotherapy of Prostate Cancer

Introduction

Immune regulatory small molecule JQ1 can block its downstream effector PD-L1 pathway and effectively reverse the PD-L1 upregulation induced by doxorubicin (DOX). So the synergistic administration of chemotherapeutic drug DOX and JQ1 is expected to increase the sensitivity of tumors to immune checkpoint therapy and jointly enhance the body's own immunity, thus effectively killing tumor cells. Therefore, a drug delivery system loaded with DOX and JQ1 was devised in this study.

Methods

Polydopamine nanoparticles (PDA NPs) were synthesized through spontaneous polymerization. Under appropriate pH conditions, DOX and JQ1 were loaded onto the surface of PDA NPs, and the release of DOX and JQ1 were measured using UV-Vis or high performance liquid chromatography (HPLC). The mechanism of fabricated nanocomplex in vitro was investigated by cell uptake experiment, cell viability assays, apoptosis assays, and Western blot analysis. Finally, the tumor-bearing mouse model was used to evaluate the tumor-inhibiting efficacy and the biosafety in vivo.

Results

JQ1 and DOX were successfully loaded onto PDA NPs. PDA-DOX/JQ1 NPs inhibited the growth of prostate cancer cells, reduced the expression of apoptosis related proteins and induced apoptosis in vitro. The in vivo biodistribution indicated that PDA-DOX/JQ1 NPs could accumulated at the tumor sites through the EPR effect. In tumor-bearing mice, JQ1 delivered with PDA-DOX/JQ1 NPs reduced PD-L1 expression at tumor sites, generating significant tumor suppression. Furthermore, PDA-DOX/JQ1 NPs could reduce the side effects, and produce good synergistic treatment effect in vivo.

Conclusion

We have successfully prepared a multifunctional platform for synergistic prostate cancer therapy.

Epigenetic modulations in triple-negative breast cancer: Therapeutic implications for tumor microenvironment

Triple-negative breast cancer (TNBC) is an aggressive subtype lacking estrogen receptors, progesterone receptors and lacks HER2 overexpression. This absence of critical molecular targets poses significant challenges for conventional therapies. Immunotherapy, remarkably immune checkpoint blockade, offers promise for TNBC treatment, but its efficacy remains limited. Epigenetic dysregulation, including altered DNA methylation, histone modifications, and imbalances in regulators such as BET proteins, plays a crucial role in TNBC development and resistance to treatment. Hypermethylation of tumor suppressor gene promoters and the imbalance of histone methyltransferases such as EZH2 and histone deacetylases (HDACs) profoundly influence tumor cell proliferation, survival, and metastasis. In addition, epigenetic alterations critically shape the tumor microenvironment (TME), including immune cell composition, cytokine signaling, and immune checkpoint expression, ultimately contributing to immune evasion. Targeting these epigenetic mechanisms with specific inhibitors such as EZH2 and HDAC inhibitors in combination with immunotherapy represents a compelling strategy to remodel the TME, potentially overcoming immune evasion and enhancing therapeutic outcomes in TNBC. This review aims to comprehensively elucidate the current understanding of epigenetic modulation in TNBC, its influence on the TME, and the potential of combining epigenetic therapies with immunotherapy to overcome the challenges posed by this aggressive breast cancer subtype.

Modeling of Mouse Experiments Suggests that Optimal Anti-Hormonal Treatment for Breast Cancer is Diet-Dependent

Estrogen receptor positive breast cancer is frequently treated with anti-hormonal treatment such as aromatase inhibitors (AI). Interestingly, a high body mass index has been shown to have a negative impact on AI efficacy, most likely due to disturbances in steroid metabolism and adipokine production. Here, we propose a mathematical model based on a system of ordinary differential equations to investigate the effect of high-fat diet on tumor growth. We inform the model with data from mouse experiments, where the animals are fed with high-fat or control (normal) diet. By incorporating AI treatment with drug resistance into the model and by solving optimal control problems we found differential responses for control and high-fat diet. To the best of our knowledge, this is the first attempt to model optimal anti-hormonal treatment for breast cancer in the presence of drug resistance. Our results underline the importance of considering high-fat diet and obesity as factors influencing clinical outcomes during anti-hormonal therapies in breast cancer patients.

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.

Optimal dosage protocols for mathematical models of synergy of chemo- and immunotherapy

The release of tumor antigens during traditional cancer treatments such as radio- or chemotherapy leads to a stimulation of the immune response which provides synergistic effects these treatments have when combined with immunotherapies. A low-dimensional mathematical model is formulated which, depending on the values of its parameters, encompasses the 3 E's (elimination, equilibrium, escape) of tumor immune system interactions. For the escape situation, optimal control problems are formulated which aim to revert the process to the equilibrium scenario. Some numerical results are included.

Epigenetic modulation of antitumor immunity and immunotherapy response in breast cancer: biological mechanisms and clinical implications

Breast cancer (BC) is the most common non-skin cancer and the second leading cause of cancer death in American women. The initiation and progression of BC can proceed through the accumulation of genetic and epigenetic changes that allow transformed cells to escape the normal cell cycle checkpoint control. Unlike nucleotide mutations, epigenetic changes such as DNA methylation, histone posttranslational modifications (PTMs), nucleosome remodeling and non-coding RNAs are generally reversible and therefore potentially responsive to pharmacological intervention. Epigenetic dysregulations are critical mechanisms for impaired antitumor immunity, evasion of immune surveillance, and resistance to immunotherapy. Compared to highly immunogenic tumor types, such as melanoma or lung cancer, breast cancer has been viewed as an immunologically quiescent tumor which displays a relatively low population of tumor-infiltrating lymphocytes (TIL), low tumor mutational burden (TMB) and modest response rates to immune checkpoint inhibitors (ICI). Emerging evidence suggests that agents targeting aberrant epigenetic modifiers may augment host antitumor immunity in BC via several interrelated mechanisms such as enhancing tumor antigen presentation, activation of cytotoxic T cells, inhibition of immunosuppressive cells, boosting response to ICI, and induction of immunogenic cell death (ICD). These discoveries have established a highly promising basis for using combinatorial approaches of epigenetic drugs with immunotherapy as an innovative paradigm to improve outcomes of BC patients. In this review, we summarize the current understanding of how epigenetic processes regulate immune cell function and antitumor immunogenicity in the context of the breast tumor microenvironment. Moreover, we discuss the therapeutic potential and latest clinical trials of the combination of immune checkpoint blockers with epigenetic agents in breast cancer.

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.

Bromodomain and extraterminal (BET) proteins: biological functions, diseases, and targeted therapy

BET proteins, which influence gene expression and contribute to the development of cancer, are epigenetic interpreters. Thus, BET inhibitors represent a novel form of epigenetic anticancer treatment. Although preliminary clinical trials have shown the anticancer potential of BET inhibitors, it appears that these drugs have limited effectiveness when used alone. Therefore, given the limited monotherapeutic activity of BET inhibitors, their use in combination with other drugs warrants attention, including the meaningful variations in pharmacodynamic activity among chosen drug combinations. In this paper, we review the function of BET proteins, the preclinical justification for BET protein targeting in cancer, recent advances in small-molecule BET inhibitors, and preliminary clinical trial findings. We elucidate BET inhibitor resistance mechanisms, shed light on the associated adverse events, investigate the potential of combining these inhibitors with diverse therapeutic agents, present a comprehensive compilation of synergistic treatments involving BET inhibitors, and provide an outlook on their future prospects as potent antitumor agents. We conclude by suggesting that combining BET inhibitors with other anticancer drugs and innovative next-generation agents holds great potential for advancing the effective targeting of BET proteins as a promising anticancer strategy.

BET in hematologic tumors: Immunity, pathogenesis, clinical trials and drug combinations

The bromodomain and extra-terminal (BET) proteins act as "readers" for lysine acetylation and facilitate the recruitment of transcriptional elongation complexes. BET protein is associated with transcriptional elongation of genes such as c-MYC and BCL-2, and is involved in the regulation of cell cycle and apoptosis. Meanwhile, BET inhibitors (BETi) have regulatory effects on immune checkpoints, immune cells, and cytokine expression. The role of BET proteins and BETi in a variety of tumors has been studied. This paper reviews the recent research progress of BET and BETi in hematologic tumors (mainly leukemia, lymphoma and multiple myeloma) from cellular level studies, animal studies, clinical trials, drug combination, etc. BETi has a promising future in hematologic tumors, and future research directions may focus on the combination with other drugs to improve the efficacy.

Data-based modeling of breast cancer and optimal therapy

Excessive accumulation of β-catenin proteins is a vital driver in the development of breast cancer. Many clinical assessments incorporating immunotherapy with targeted mRNA of β-catenin are costly endeavor. This paper develops novel mathematical models for different treatments by invoking available clinical data to calibrate models, along with the selection and evaluation of therapy strategies in a faster manner with lower cost. Firstly, in order to explore the interactions between cancer cells and the immune system within the tumor microenvironment, we construct different types of breast cancer treatment models based on RNA interference technique and immune checkpoint inhibitors, which have been proved to be an effective combined therapy in pre-clinical trials associated with the inhibition of β-catenin proteins to enhance intrinsic anti-tumor immune response. Secondly, various techniques including MCMC are adopted to estimate multiple parameters and thus simulations in agreement with experimental results sustain the validity of our models. Furthermore, the gradient descent method and particle swarm algorithm are designed to optimize therapy schemes to inhibit the growth of tumor and lower the treatment cost. Considering the mechanisms of drug resistance in vivo, simulations exhibit that therapies are ineffective resulting in cancer relapse in the prolonged time. For this reason, parametric sensitivity analysis sheds light on the choice of new treatments which indicate that, in addition to inhibiting β-catenin proteins and improving self-immunity, the injection of dendritic cells promoting immunity may provide a novel vision for the future of cancer treatment. Overall, our study provides witness of principle from a mathematical perspective to guide clinical trials and the selection of treatment regimens.

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 (eFAST). 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.

Multiple colonies of cancer involved in mutual suppression with the immune system

We study the effects of the immune system on multiple cancer colonies. When cancer cells proliferate, cytotoxic T lymphocytes (CTLs) reactive to the cancer-specific antigens are activated, suppressing the growth of cancer colonies. The immune reaction activated by a large cancer colony may suppress and eliminate smaller colonies. However, cancer cells mitigate immune reactions by slowing down the activation of CTLs in dendritic cells with regulatory T cells and by inactivating CTLs attacking cancer cells with immune checkpoints. If cancer cells strongly suppress the immune reaction, the system may become bistable, where both the cancer-dominated and immunity-dominated states are locally stable. We study several models differing in the distance between colonies and the migration speeds of CTLs and regulatory T cells. We examine how the domains of attraction for multiple equilibria change with parameters. Nonlinear cancer-immunity dynamics may produce a sharp transition from a state with a small number of colonies and strong immunity to one with many colonies and weak immunity, resulting in the rapid emergence of many cancer colonies in the same organ or metastatic sites.

Epigenetics behind tumor immunology: a mini review

Immunogenic- and immune-therapies have become hot spots in the treatment of cancer. Although promising, these strategies are frequently associated with innate or acquired resistance, calling for combined targeting of immune inhibitory signals. Epigenetic therapy is attracting considerable attention as a combination partner for immune-based therapies due to its role in molding the state and fate of cancer and immune cells in the tumor microenvironment. Here, we describe epigenetic dysregulations in cancer, with a particular focus on those related to innate immune signaling and Type I interferons, and emphasize opportunities and current efforts to translate this knowledge into treatment regimens with improved clinical benefit.

Targeting myeloid-derived suppressor cells in combination with tumor cell vaccination predicts anti-tumor immunity and breast cancer dormancy: an in silico experiment

Among the different breast cancer subsets, triple-negative breast cancer (TNBC) has the worst prognosis and limited options for targeted therapies. Immunotherapies are emerging as novel treatment opportunities for TNBC. However, the surging immune response elicited by immunotherapies to eradicate cancer cells can select resistant cancer cells, which may result in immune escape and tumor evolution and progression. Alternatively, maintaining the equilibrium phase of the immune response may be advantageous for keeping a long-term immune response in the presence of a small-size residual tumor. Myeloid-derived suppressor cells (MDSCs) are activated, expanded, and recruited to the tumor microenvironment by tumor-derived signals and can shape a pro-tumorigenic micro-environment by suppressing the innate and adaptive anti-tumor immune responses. We recently proposed a model describing immune-mediated breast cancer dormancy instigated by a vaccine consisting of dormant, immunogenic breast cancer cells derived from the murine 4T1 TNBC-like cell line. Strikingly, these 4T1-derived dormant cells recruited fewer MDSCs compared to aggressive 4T1 cells. Recent experimental studies demonstrated that inactivating MDSCs has a profound impact on reconstituting immune surveillance against the tumor. Here, we developed a deterministic mathematical model for simulating MDSCs depletion from mice bearing aggressive 4T1 tumors resulting in immunomodulation. Our computational simulations indicate that a vaccination strategy with a small number of tumor cells in combination with MDSC depletion can elicit an effective immune response suppressing the growth of a subsequent challenge with aggressive tumor cells, resulting in sustained tumor dormancy. The results predict a novel therapeutic opportunity based on the induction of effective anti-tumor immunity and tumor dormancy.

Combinatorial targeting of immune checkpoints and epigenetic regulators for hepatocellular carcinoma therapy

Hepatocellular carcinoma (HCC) is the second leading cause of cancer-related mortality worldwide. The five-year survival rate of patients with unresectable HCC is about 12%. The liver tumor microenvironment (TME) is immune tolerant and heavily infiltrated with immunosuppressive cells. Immune checkpoint inhibitors (ICIs), in some cases, can reverse tumor cell immune evasion and enhance antitumor immunity. Rapidly evolving ICIs have expanded systemic treatment options in advanced HCC; however, single-agent ICIs achieve a limited 15-20% objective response rate in advanced HCC. Therefore, other combinatorial approaches that amplify the efficacy of ICIs or suppress other tumor-promoting pathways may enhance clinical outcomes. Epigenetic alterations (e.g., changes in chromatin states and non-genetic DNA modifications) have been shown to drive HCC tumor growth and progression as well as their response to ICIs. Recent studies have combined ICIs and epigenetic inhibitors in preclinical and clinical settings to contain several cancers, including HCC. In this review, we outline current ICI treatments for HCC, the mechanism behind their successes and failures, and how ICIs can be combined with distinct epigenetic inhibitors to increase the durability of ICIs and potentially treat "immune-cold" HCC.

Modeling tumour heterogeneity of PD-L1 expression in tumour progression and adaptive therapy

Although PD-1/PD-L1 inhibitors show potent and durable anti-tumour effects in some refractory tumours, the response rate in overall patients is unsatisfactory, which in part due to the inherent heterogeneity of PD-L1. In order to establish an approach for predicting and estimating the dynamic alternation of PD-L1 heterogeneity during cancer progression and treatment, this study establishes a comprehensive modelling and computational framework based on a mathematical model of cancer cell evolution in the tumour-immune microenvironment, and in combination with epigenetic data and overall survival data of clinical patients from The Cancer Genome Atlas. Through PD-L1 heterogeneous virtual patients obtained by the computational framework, we explore the adaptive therapy of administering anti-PD-L1 according to the dynamic of PD-L1 state among cancer cells. Our results show that in contrast to the continuous maximum tolerated dose treatment, adaptive therapy is more effective for PD-L1 positive patients, in that it prolongs the survival of patients by administration of drugs at lower dosage.

Cancer therapy with immune checkpoint inhibitor and CSF-1 blockade: A mathematical model

Immune checkpoint inhibitors (ICIs) introduced in recent years have revolutionized the treatment of many metastatic cancers. However, data suggest that treatment has benefits only in a limited percentage of patients, and that this is due to immune suppression of the tumor microenvironment (TME). Anti-tumor inflammatory macrophages (M1), which are attracted to the TME, are converted by tumor secreted cytokines, such as CSF-1, to pro-tumor anti-inflammatory macrophages (M2), or tumor associated macrophages (TAMs), which block the anti-tumor T cells. In the present paper we develop a mathematical model that represents the interactions among the immune cells and cancer in terms of differential equations. The model can be used to assess treatments of combination therapy of anti-PD-1 with anti-CSF-1. Examples are given in comparing the efficacy among different strategies for anti-CSF-1 dosing in a setup of clinical trials.

Optimal timing of steroid initiation in response to CTLA-4 antibody in metastatic cancer: A mathematical model

Immune checkpoint inhibitors, introduced in recent years, have revolutionized the treatment of many cancers. However, the toxicity associated with this therapy may cause severe adverse events. In the case of advanced lung cancer or metastatic melanoma, a significant number (10%) of patients treated with CTLA-4 inhibitor incur damage to the pituitary gland. In order to reduce the risk of hypophysitis and other severe adverse events, steroids may be combined with CTLA-4 inhibitor; they reduce toxicity, but they also diminish the anti-cancer effect of the immunotherapy. This trade-off between tumor reduction and the risk of severe adverse events poses the following question: What is the optimal time to initiate treatment with steroid. We address this question with a mathematical model from which we can also evaluate the comparative benefits of each schedule of steroid administration. In particular, we conclude that treatment with steroid should not begin too early, but also not very late, after immunotherapy began; more precisely, it should start as soon as tumor volume, under the effect of CTLA-4 inhibitor alone, begins to decrease. We can also compare the benefits of short term treatment of steroid at high doses to a longer term treatment with lower doses.

Distinct Dynamics of Migratory Response to PD-1 and CTLA-4 Blockade Reveals New Mechanistic Insights for Potential T-Cell Reinvigoration following Immune Checkpoint Blockade

Cytotoxic T-lymphocyte-associated antigen 4 (CTLA-4) and programmed cell death protein 1 (PD-1), two clinically relevant targets for the immunotherapy of cancer, are negative regulators of T-cell activation and migration. Optimizing the therapeutic response to CTLA-4 and PD-1 blockade calls for a more comprehensive insight into the coordinated function of these immune regulators. Mathematical modeling can be used to elucidate nonlinear tumor-immune interactions and highlight the underlying mechanisms to tackle the problem. Here, we investigated and statistically characterized the dynamics of T-cell migration as a measure of the functional response to these pathways. We used a previously developed three-dimensional organotypic culture of patient-derived tumor spheroids treated with anti-CTLA-4 and anti-PD-1 antibodies for this purpose. Experiment-based dynamical modeling revealed the delayed kinetics of PD-1 activation, which originates from the distinct characteristics of PD-1 and CTLA-4 regulation, and followed through with the modification of their contributions to immune modulation. The simulation results show good agreement with the tumor cell reduction and active immune cell count in each experiment. Our findings demonstrate that while PD-1 activation provokes a more exhaustive intracellular cascade within a mature tumor environment, the time-delayed kinetics of PD-1 activation outweighs its preeminence at the individual cell level and consequently confers a functional dominance to the CTLA-4 checkpoint. The proposed model explains the distinct immunostimulatory pattern of PD-1 and CTLA-4 blockade based on mechanisms involved in the regulation of their expression and may be useful for planning effective treatment schemes targeting PD-1 and CTLA-4 functions.

Patient-Specific Mathematical Model of the Clear Cell Renal Cell Carcinoma Microenvironment

The interactions between cells and molecules in the tumor microenvironment can give insight into the initiation and progression of tumors and their optimal treatment options. In this paper, we developed an ordinary differential equation (ODE) mathematical model of the interaction network of key players in the clear cell renal cell carcinoma (ccRCC) microenvironment. We then performed a global gradient-based sensitivity analysis to investigate the effects of the most sensitive parameters of the model on the number of cancer cells. The results indicate that parameters related to IL-6 have high a impact on cancer cell growth, such that decreasing the level of IL-6 can remarkably slow the tumor's growth.

Injectable pH-responsive hydrogel for combinatorial chemoimmunotherapy tailored to the tumor microenvironment

Although combination chemoimmunotherapy shows promising clinical results for cancer treatment, this approach is largely restricted by variable objective response rate and severe systemic adverse effects of immunotherapeutic antibody and chemotherapeutic drugs. Therefore, an in situ-formed therapeutic silk-chitosan composite scaffold is fabricated in this study to allow local release of the chemotherapeutic drug doxorubicin (DOX) and JQ1 (small molecular inhibitor used for the extraterminal protein BRD4 and bromodomain) with control release kinetics. DOX-JQ1@Gel contains a pH-degradable group that releases therapeutics in a weak acidic tumor microenvironment. The released DOX could directly kill tumor cells or lead to immunogenic cell death, thereby triggering the response of antitumor immunity. Meanwhile, chemotherapy-triggered antigen release and JQ1-mediated PD-L1 checkpoint blockade cumulatively contribute to trigger the response of antitumor immunity. Finally, the DOX-JQ1@Gel is locally injected to evaluate its synergistic cancer therapeutic effect, which is expected to improve objective response rate of immunotherapy and minimize systemic side effects.

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.

Analysis of the Equilibrium Phase in Immune-Controlled Tumors Provides Hints for Designing Better Strategies for Cancer Treatment

When it comes to improving cancer therapies, one challenge is to identify key biological parameters that prevent immune escape and maintain an equilibrium state characterized by a stable subclinical tumor mass, controlled by the immune cells. Based on a space and size structured partial differential equation model, we developed numerical methods that allow us to predict the shape of the equilibrium at low cost, without running simulations of the initial-boundary value problem. In turn, the computation of the equilibrium state allowed us to apply global sensitivity analysis methods that assess which and how parameters influence the residual tumor mass. This analysis reveals that the elimination rate of tumor cells by immune cells far exceeds the influence of the other parameters on the equilibrium size of the tumor. Moreover, combining parameters that sustain and strengthen the antitumor immune response also proves more efficient at maintaining the tumor in a long-lasting equilibrium state. Applied to the biological parameters that define each type of cancer, such numerical investigations can provide hints for the design and optimization of cancer treatments.

Modeling the effect of gut microbiome on therapeutic efficacy of immune checkpoint inhibitors against cancer

Immune checkpoint inhibitors have been shown to be highly successful against some solid metastatic malignancies, but only for a subset of patients who show durable clinical responses. The overall patient response rate is limited due to the interpatient heterogeneity. Preclinical and clinical studies have recently shown that the therapeutic responses can be improved through the modulation of gut microbiome. However, the underlying mechanisms are not fully understood. In this paper, we explored the effect of favorable and unfavorable gut bacteria on the therapeutic efficacy of anti-PD-1 against cancer by modeling the tumor-immune-gut microbiome interactions, and further examined the predictive markers of responders and non-responders to anti-PD-1. The dynamics of the gut bacteria was fitted to the clinical data of melanoma patients, and virtual patients data were generated based on the clinical patient survival data. Our simulation results show that low initial growth rate and low level of favorable bacteria at the initiation of anti-PD-1 therapy are predictive of non-responders, while high level of favorable bacteria at the initiation of anti-PD-1 therapy is predictive of responders. Simulation results also confirmed that it is possible to promote patients' response rate to anti-PD-1 by manipulating the gut bacteria composition of non-responders, whereby achieving long-term progression-free survival.

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.

Tumor immunotherapies by immune checkpoint inhibitors (ICIs); the pros and cons

The main breakthrough in tumor immunotherapy was the discovery of immune checkpoint (IC) proteins, which act as a potent suppressor of the immune system by a myriad of mechanisms. After that, scientists focused on the immune checkpoint molecules mainly. Thereby, much effort was spent to progress novel strategies for suppressing these inhibitory axes, resulting in the evolution of immune checkpoint inhibitors (ICIs). Then, ICIs have become a promising approach and shaped a paradigm shift in tumor immunotherapies. CTLA-4 plays an influential role in attenuation of the induction of naïve and memory T cells by engagement with its responding ligands like B7-1 (CD80) and B7-2 (CD86). Besides, PD-1 is predominantly implicated in adjusting T cell function in peripheral tissues through its interaction with programmed death-ligand 1 (PD-L1) and PD-L2. Given their suppressive effects on anti-tumor immunity, it has firmly been documented that ICIs based therapies can be practical and rational therapeutic approaches to treat cancer patients. Nonetheless, tumor inherent or acquired resistance to ICI and some treatment-related toxicities restrict their application in the clinic. The current review will deliver a comprehensive overview of the ICI application to treat human tumors alone or in combination with other modalities to support more desired outcomes and lower toxicities in cancer patients. Video Abstract.

Mathematical Modeling to Simulate the Effect of Adding Radiation Therapy to Immunotherapy and Application to Hepatocellular Carcinoma

PURPOSE

To develop a comprehensive framework to simulate the response to immune checkpoint inhibitors (ICIs) in combination with radiation therapy (RT) and to apply the framework for investigating ICI-RT combination regimen in patients with hepatocellular carcinoma (HCC).

METHODS AND MATERIALS

The mechanistic mathematical model is based on dynamic biological interactions between the immune system and the tumor using input data from patient blood samples and outcomes of clinical trials. The cell compartments are described by ordinary differential equations and represent irradiated and nonirradiated tumor cells and lymphocytes. The effect of ICI is modeled using an immune activation term that is based on tumor size changes observed in a phase 1/2 clinical trial for HCC. Simulated combination regimen are based on ongoing ICI-RT trials.

RESULTS

The proposed framework successfully describes tumor volume trajectories observed in early-stage clinical trials of durvalumab monotherapy in patients with HCC. For ICI-RT treatment regimen the irradiated tumor fraction is the most important parameter for the efficacy. For 90% of the tumor cells being irradiated, adding RT to ICI yields an increase in clinical benefit from 33% to 71% in nonirradiated tumor sites. The model agrees with clinical data showing an association of outcome with initial tumor volume and lymphocyte counts. We demonstrate model application in clinical trial design to predict progression-free survival curves, showing that the cohort size to show significant improvement heavily depends on the irradiated tumor fraction.

CONCLUSIONS

We present a framework extending radiation cell kill models to include circulating lymphocytes and the effect of ICIs and enable simulation of combination strategies. The simulations predict that a significant amount of the benefit from RT in combination with ICI stems from the reduction in irradiated tumor burden and associated immune suppression. This aspect needs to be included in the interpretation of outcomes and the design of novel combination trials.

Investigating key cell types and molecules dynamics in PyMT mice model of breast cancer through a mathematical model

The most common kind of cancer among women is breast cancer. Understanding the tumor microenvironment and the interactions between individual cells and cytokines assists us in arriving at more effective treatments. Here, we develop a data-driven mathematical model to investigate the dynamics of key cell types and cytokines involved in breast cancer development. We use time-course gene expression profiles of a mouse model to estimate the relative abundance of cells and cytokines. We then employ a least-squares optimization method to evaluate the model's parameters based on the mice data. The resulting dynamics of the cells and cytokines obtained from the optimal set of parameters exhibit a decent agreement between the data and predictions. We perform a sensitivity analysis to identify the crucial parameters of the model and then perform a local bifurcation on them. The results reveal a strong connection between adipocytes, IL6, and the cancer population, suggesting them as potential targets for therapies.

Interlocked feedback loops balance the adaptive immune response

Adaptive immune responses can be activated by harmful stimuli. Upon activation, a cascade of biochemical events ensues the proliferation and the differentiation of T cells, which can remove the stimuli and undergo cell death to maintain immune cell homeostasis. However, normal immune processes can be disrupted by certain dysregulations, leading to pathological responses, such as cytokine storms and immune escape. In this paper, a qualitative mathematical model, composed of key feedback loops within the immune system, was developed to study the dynamics of various response behaviors. First, simulation results of the model well reproduce the results of several immune response processes, particularly pathological immune responses. Next, we demonstrated how the interaction of positive and negative feedback loops leads to irreversible bistable, reversible bistable and monostable, which characterize different immune response processes: cytokine storm, normal immune response, immune escape. The stability analyses suggest that the switch-like behavior is the basis of rapid activation of the immune system, and a balance between positive and negative regulation loops is necessary to prevent pathological responses. Furthermore, we have shown how the treatment moves the system back to a healthy state from the pathological immune response. The bistable mechanism that revealed in this work is helpful to understand the dynamics of different immune response processes.

A graph model of combination therapies

The simultaneous use of multiple medications causes drug-drug interactions (DDI) that impact therapeutic efficacy. Here, we argue that graph theory, in conjunction with game theory and ecosystem theory, can address this issue. We treat the coexistence of multiple drugs as a system in which DDI is modeled by game theory. We develop an ordinary differential equation model to characterize how the concentration of a drug changes as a result of its independent capacity and the dependent influence of other drugs through the metabolic response of the host. We coalesce all drugs into personalized and context-specific networks, which can reveal key DDI determinants of therapeutical efficacy. Our model can quantify drug synergy and antagonism and test the translational success of combination therapies to the clinic.

Combination therapy for mCRPC with immune checkpoint inhibitors, ADT and vaccine: A mathematical model

Metastatic castration resistant prostate cancer (mCRPC) is commonly treated by androgen deprivation therapy (ADT) in combination with chemotherapy. Immune therapy by checkpoint inhibitors, has become a powerful new tool in the treatment of melanoma and lung cancer, and it is currently being used in clinical trials in other cancers, including mCRPC. However, so far, clinical trials with PD-1 and CTLA-4 inhibitors have been disappointing. In the present paper we develop a mathematical model to assess the efficacy of any combination of ADT with cancer vaccine, PD-1 inhibitor, and CTLA-4 inhibitor. The model is represented by a system of partial differential equations (PDEs) for cells, cytokines and drugs whose density/concentration evolves in time within the tumor. Efficacy of treatment is determined by the reduction in tumor volume at the endpoint of treatment. In mice experiments with ADT and various combinations of PD-1 and CTLA-4 inhibitors, tumor volume at day 30 was always larger than the initial tumor. Our model, however, shows that we can decrease tumor volume with large enough dose; for example, with 10 fold increase in the dose of anti-PD-1, initial tumor volume will decrease by 60%. Although the treatment with ADT in combination with PD-1 inhibitor or CTLA-4 inhibitor has been disappointing in clinical trials, our simulations suggest that, disregarding negative effects, combinations of ADT with checkpoint inhibitors can be effective in reducing tumor volume if larger doses are used. This points to the need for determining the optimal combination and amounts of dose for individual patients.

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

Data-driven multi-scale mathematical modeling of SARS-CoV-2 infection reveals heterogeneity among COVID-19 patients

Patients with coronavirus disease 2019 (COVID-19) often exhibit diverse disease progressions associated with various infectious ability, symptoms, and clinical treatments. To systematically and thoroughly understand the heterogeneous progression of COVID-19, we developed a multi-scale computational model to quantitatively understand the heterogeneous progression of COVID-19 patients infected with severe acute respiratory syndrome (SARS)-like coronavirus (SARS-CoV-2). The model consists of intracellular viral dynamics, multicellular infection process, and immune responses, and was formulated using a combination of differential equations and stochastic modeling. By integrating multi-source clinical data with model analysis, we quantified individual heterogeneity using two indexes, i.e., the ratio of infected cells and incubation period. Specifically, our simulations revealed that increasing the host antiviral state or virus induced type I interferon (IFN) production rate can prolong the incubation period and postpone the transition from asymptomatic to symptomatic outcomes. We further identified the threshold dynamics of T cell exhaustion in the transition between mild-moderate and severe symptoms, and that patients with severe symptoms exhibited a lack of naïve T cells at a late stage. In addition, we quantified the efficacy of treating COVID-19 patients and investigated the effects of various therapeutic strategies. Simulations results suggested that single antiviral therapy is sufficient for moderate patients, while combination therapies and prevention of T cell exhaustion are needed for severe patients. These results highlight the critical roles of IFN and T cell responses in regulating the stage transition during COVID-19 progression. Our study reveals a quantitative relationship underpinning the heterogeneity of transition stage during COVID-19 progression and can provide a potential guidance for personalized therapy in COVID-19 patients.

Coronavirus disease 2019 (COVID-19) is currently destroying both lives and economies. However, patients infected with severe acute respiratory syndrome (SARS)-like coronavirus (SARS-CoV-2) usually present heterogeneous and complicated progressions, such as different incubation periods (short and long), symptoms (asymptomatic and symptomatic) and severity (mild-moderate and severe). Currently, various clinical data and experimental data are available from different countries, which has great significance for integrating different types of data to comprehensively understand the diverse disease progression in COVID-19 patients and guide individual treatment strategies. Here, we developed a multi-scale computational model to describe the dynamical process of patients infected with SARS-CoV-2, including intracellular viral dynamics, multicellular infection process, and immune responses. By combining data integration, stochastic simulation and quantitative analysis based on the multi-scale mathematical model, we addressed an important question regarding how IFN response and T cell exhaustion quantitatively affect heterogeneous progression in patients with respect to incubation periods, symptoms and severity. Furthermore, the efficacy of various therapeutic strategies for treating COVID-19 patients with different severity degrees was evaluated and validated. The computational framework in this study can also be extended to explore the dynamical process of other coronavirus infections.

A Mathematical Model of Breast Tumor Progression Based on Immune Infiltration

Breast cancer is the most prominent type of cancer among women. Understanding the microenvironment of breast cancer and the interactions between cells and cytokines will lead to better treatment approaches for patients. In this study, we developed a data-driven mathematical model to investigate the dynamics of key cells and cytokines involved in breast cancer development. We used gene expression profiles of tumors to estimate the relative abundance of each immune cell and group patients based on their immune patterns. Dynamical results show the complex interplay between cells and molecules, and sensitivity analysis emphasizes the direct effects of macrophages and adipocytes on cancer cell growth. In addition, we observed the dual effect of IFN-γ on cancer proliferation, either through direct inhibition of cancer cells or by increasing the cytotoxicity of CD8+ T-cells.

Targeting cancer epigenetic pathways with small-molecule compounds: Therapeutic efficacy and combination therapies

Epigenetics mainly refers to covalent modifications to DNA or histones without affecting genomes, which ultimately lead to phenotypic changes in cells or organisms. Given the abundance of regulatory targets in epigenetic pathways and their pivotal roles in tumorigenesis and drug resistance, the development of epigenetic drugs holds a great promise for the current cancer therapy. However, lack of potent, selective, and clinically tractable small-molecule compounds makes the strategy to target cancer epigenetic pathways still challenging. Therefore, this review focuses on epigenetic pathways, small molecule inhibitors targeting DNA methyltransferase (DNMT) and small molecule inhibitors targeting histone modification (the main regulatory targets are histone acetyltransferases (HAT), histone deacetylases (HDACs) and histone methyltransferases (HMTS)), as well as the combination strategies of the existing epigenetic therapeutic drugs and more new therapies to improve the efficacy, which will shed light on a new clue on discovery of more small-molecule drugs targeting cancer epigenetic pathways as promising strategies in the future.

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.

Combinatorial Epigenetic and Immunotherapy in Breast Cancer Management: A Literature Review

Breast cancer is one of the leading causes of death among cancer patients worldwide. To date, there are several drugs that have been developed for breast cancer therapy. In the 21st century, immunotherapy is considered a pioneering method for improving the management of malignancies; however, breast cancer is an exception. According to the immunoediting model, many immunosuppressive cells contribute to immunological quiescence. Therefore, there is an urgent need to enhance the therapeutic efficacy of breast cancer treatments. In the last few years, numerous combinatorial therapies involving immune checkpoint blockade have been demonstrated that effectively improve clinical outcomes in breast cancer and combining these with methods of targeting epigenetic regulators is also an innovative strategy. Nevertheless, few studies have discussed the benefits of epi-drugs in non-cancerous cells. In this review, we give a brief overview of ongoing clinical trials involving combinatorial immunotherapy with epi-drugs in breast cancer and discuss the role of epi-drugs in the tumor microenvironment, including the results of recent research.

Combining epigenetic and immune therapy to overcome cancer resistance

Cancer undergoes "immune editing" to evade destruction by cells of the host immune system including natural killer (NK) cells and cytotoxic T lymphocytes (CTLs). Current adoptive cellular immune therapies include CAR T cells and dendritic cell vaccines, strategies that have yet to show success for a wide range of tumors. Cancer resistance to immune therapy is driven by extrinsic factors and tumor cell intrinsic factors that contribute to immune evasion. These extrinsic factors include immunosuppressive cell populations such as regulatory T cells (Tregs), tumor-associated macrophages (TAMS), and myeloid-derived suppressor cells (MDSCs). These cells produce and secrete immunosuppressive factors and express inhibitory ligands that interact with receptors on T cells including PD-1 and CTLA-4. Immune checkpoint blockade (ICB) therapies such as anti-PD-1 and anti-CTLA-4 have shown success by increasing immune activation to eradicate cancer, though both primary and acquired resistance remain a problem. Tumor cell intrinsic factors driving primary and acquired resistance to these immune therapies include genetic and epigenetic mechanisms. Epigenetic therapies for cancer including DNA methyltransferase inhibitors (DNMTi), histone deacetylase inhibitors (HDACi), and histone methyltransferase inhibitors (HMTi) can stimulate anti-tumor immunity in both tumor cells and host immune cells. Here we discuss in detail tumor mechanisms of immune evasion and how common epigenetic therapies for cancer may be used to reverse immune evasion. Lastly, we summarize current clinical trials combining epigenetic therapies with immune therapies to reverse cancer immune resistance mechanisms.

QSP-IO: A Quantitative Systems Pharmacology Toolbox for Mechanistic Multiscale Modeling for Immuno-Oncology Applications

Immunotherapy has shown great potential in the treatment of cancer; however, only a fraction of patients respond to treatment, and many experience autoimmune‐related side effects. The pharmaceutical industry has relied on mathematical models to study the behavior of candidate drugs and more recently, complex, whole‐body, quantitative systems pharmacology (QSP) models have become increasingly popular for discovery and development. QSP modeling has the potential to discover novel predictive biomarkers as well as test the efficacy of treatment plans and combination therapies through virtual clinical trials. In this work, we present a QSP modeling platform for immuno‐oncology (IO) that incorporates detailed mechanisms for important immune interactions. This modular platform allows for the construction of QSP models of IO with varying degrees of complexity based on the research questions. Finally, we demonstrate the use of the platform through two example applications of immune checkpoint therapy.

Bromodomain 4 is a potent prognostic marker associated with immune cell infiltration in breast cancer

Bromodomain 4 (BRD4), a member of the bromodomain and extra-terminal domain protein family, has become a promising epigenetic target in cancer and inflammatory diseases; however, the detailed biological role of BRD4 in breast cancer (BRCA) remains undetermined. We analysed the BRD4 expression levels using the Oncomine and TIMER databases and evaluated the clinical impact of BRD4 on BRCA prognosis using Kaplan-Meier plot and PrognoScan. The correlation between BRD4 and tumour-infiltrating immune cells was investigated using TIMER. Furthermore, the correlation between BRD4 expression levels was also analysed using TIMER in addition to the GEPIA database for immune cell gene markers. BRD4 expression was significantly higher in BRCA tissues than in normal tissues, which was significantly correlated with poor overall survival (OS). Specifically, high BRD4 expression was correlated with worse OS and progression-free survival in patients with BRCA. In addition, BRD4 expression was correlated with levels of infiltrating monocytes (CSF1R, cor = 0.204, P = 9.19e-12), tumour-associated macrophages (CD68, cor = 0.129, P = 1.81e-05), M1/M2 macrophages and different effector T cells (including Th1/Th2/Treg) in BRCA. These findings suggest that BRD4 could be used as a prognostic biomarker for determining prognosis and immune cell infiltration levels in BRCA.

The emerging role of BET inhibitors in breast cancer

Bromodomain and extraterminal domain (BET) proteins are epigenetic molecules that regulate the expression of multiple genes involved in carcinogenesis. Breast cancer is an heterogenous disease emerging from aberrant gene expression and epigenetic alteration patterns. Amplification or overexpression of BET proteins has been identified in breast tumors highlighting their clinical significance. Development of BET inhibitors that disrupt BET protein binding to acetylated lysine residues of chromatin and suppress transcription of various oncogenes has shown promising results in breast cancer cells and xenograft models. Currently, Phase I/II clinical trials explore safety and efficacy of BET inhibitors in solid tumors and breast cancer. Treatment-emergent toxicities have been reported, including thrombocytopenia and gastrointestinal disorders. Preliminary results demonstrated greater response rates to BET inhibitors in combination with already approved anticancer agents. Consistently, BET inhibition sensitized breast tumors to chemotherapy drugs, hormone therapy and PI3K inhibitors in vitro. This article aims to review all existing preclinical and clinical evidence regarding BET inhibitors in breast cancer.

PD-1/PD-L1 counterattack alliance: multiple strategies for treating triple-negative breast cancer

Despite extensive research into adjuvant and neoadjuvant chemotherapy, triple-negative breast cancer (TNBC) remains a common breast cancer (BC) subtype with poor prognosis. Given that it has higher immune cell infiltration, theoretically, it should be a protagonist of potential BC immunotherapies. However, only mild responses have been observed in monotherapy with anti-programmed death receptor-1/programmed death ligand-1 (PD-1/PD-L1) antibodies. In this review, we reappraise PD-1/PD-L1 inhibitor combination immunotherapy and effective experimental compounds, focusing the level of PD-L1 expression, neoantigens, abnormal signaling pathways, and tumor microenvironment signatures, to provide guidance for future clinical trials based on the molecular mechanisms involved.

Macrophages confer resistance to BET inhibition in triple-negative breast cancer by upregulating IKBKE

BET inhibitors (BETi) exhibit a strong anti-tumor activity in triple-negative breast cancer (TNBC). However, BETi resistance has been reported in TNBC. The mechanisms of resistance have not been demonstrated. Tumor-associated macrophages (TAMs) are frequently involved in cancer cells resistance to chemotherapy, also associated with poor prognosis in TNBC. However, the role of TAMs in BETi resistance remains unknown. Here, we found that BETi JQ1 and I-BET151 exerted anti-tumor effects in TNBC by decreasing IKBKE expression to attenuate NF-κB signaling. TAMs have been reported to associate with chemoresistance in breast cancer. Here, we firstly found that TNBC-stimulated TAMs activated NF-κB signaling by upregulating IKBKE expression to enhance breast cancer cells resistance to BETi. The IKBKE levels were also proved to be higher in clinical TNBC tissues than Non-TNBC tissues, suggesting feedback induction of IKBKE expression by TNBC-stimulated TAMs in TNBC. Moreover, the induction of IKBKE by TAMs in TNBC cells was identified to be associated with STAT3 signaling, which was activated by TAM-secreted IL-6 and IL-10. Lastly, the combination of inhibitors of BET and STAT3 exerted a synergistic inhibition effects in TAM-cocultured or TAM CM-treated TNBC cells in vitro and in vivo. Altogether, our findings illustrated TNBC-activated macrophages conferred TNBC cells resistance to BETi via IL-6 or IL-10/STAT3/IKBKE/NF-κB axis. Blockade of IKBKE or double inhibition of BET and STAT3 might be a novel strategy for treatment of TNBC.

Towards a more precise therapy in cancer: Exploring epigenetic complexity

A plethora of preclinical evidences suggests that pharmacological targeting of epigenetic dysregulation is a potent strategy to combat human diseases. Nevertheless, the implementation of epidrugs in clinical practice is very scarce and mainly limited to haematological malignancies. In this review, we discuss cutting-edge strategies to foster the chemical design, the biological rationale and the clinical trial development of epidrugs. Specifically, we focus on the development of dual hybrids to exploit multitargeting of key epigenetic molecules deregulated in cancer; the study of epigenetic-synthetic lethality interactions as a mechanism to address loss-of-function mutations, and the combination of epidrugs with other therapies such as immunotherapy to avoid acquired chemoresistance and increase therapy sensitivity. By exploring these challenges, among others, the field of epigenetic chemical biology will increase its potential for clinical benefit, and more effective strategies targeting the aberrant epigenome in cancer are likely to be developed both in haematological and solid tumours.

Small Molecular Immune Modulators as Anticancer Agents

After decades of intense effort, immune checkpoint inhibitors have been conclusively demonstrated to be effective in cancer treatments and thus are revolutionizing the concepts in the treatment of cancers. Immuno-oncology has arrived and will play a key role in cancer treatment in the foreseeable future. However, efforts to find novel methods to improve the immune response to cancer have not ceased. Small-molecule approaches offer inherent advantages over biologic immunotherapies since they can cross cell membranes, penetrate into tumor tissue and tumor microenvironment more easily, and are amenable to be finely controlled than biological agents, which may help reduce immune-related adverse events seen with biologic therapies and provide more flexibility for the combination use with other therapies and superior clinical benefit. On the one hand, small-molecule therapies can modulate the immune response to cancer by restoring the antitumor immunity, promoting more effective cytotoxic lymphocyte responses, and regulating tumor microenvironment, either directly or epigenetically. On the other hand, the combination of different mechanisms of small molecules with antibodies and other biologics demonstrated admirable synergistic effect in clinical settings for cancer treatment and may expand antibodies' usefulness for broader clinical applications. This chapter provides an overview of small-molecule immunotherapeutic approaches either as monotherapy or in combination for the treatment of cancer.

TNF-α inhibitor reduces drug-resistance to anti-PD-1: A mathematical model

Drug resistance is a primary obstacle in cancer treatment. In many patients who at first respond well to treatment, relapse occurs later on. Various mechanisms have been explored to explain drug resistance in specific cancers and for specific drugs. In this paper, we consider resistance to anti-PD-1, a drug that enhances the activity of anti-cancer T cells. Based on results in experimental melanoma, it is shown, by a mathematical model, that resistances to anti-PD-1 can be significantly reduced by combining it with anti-TNF-α. The model is used to simulate the efficacy of the combined therapy with different range of doses, different initial tumor volume, and different schedules. In particular, it is shown that under a course of treatment with 3-week cycles where each drug is injected in the first day of either week 1 or week 2, injecting anti-TNF-α one week after anti-PD-1 is the most effective schedule in reducing tumor volume.

Role of BET Inhibitors in Triple Negative Breast Cancers

Bromodomain and extraterminal domain (BET) proteins have evolved as key multifunctional super-regulators that control gene expression. These proteins have been shown to upregulate transcriptional machinery leading to over expression of genes involved in cell proliferation and carcinogenesis. Based on favorable preclinical evidence of BET inhibitors in various cancer models; currently, 26 clinical trials are underway in various stages of study on various hematological and solid organ cancers. Unfortunately, preliminary evidence for these clinical studies does not support the application of BET inhibitors as monotherapy in cancer treatment. Furthermore, the combinatorial efficiency of BET inhibitors with other chemo-and immunotherapeutic agents remain elusive. In this review, we will provide a concise summary of the molecular basis and preliminary clinical outcomes of BET inhibitors in cancer therapy, with special focus on triple negative breast cancer.