TGF-β inhibition can overcome cancer primary resistance to PD-1 blockade: A mathematical model

A landscape of checkpoint blockade resistance in cancer: underlying mechanisms and current strategies to overcome resistance

The discovery of immune checkpoints and the development of immune checkpoint inhibitors (ICI) have achieved a durable response in advanced-stage cancer patients. However, there is still a high proportion of patients who do not benefit from ICI therapy due to a lack of response when first treated (primary resistance) or detection of disease progression months after objective response is observed (acquired resistance). Here, we review the current FDA-approved ICI for the treatment of certain solid malignancies, evaluate the contrasting responses to checkpoint blockade in different cancer types, explore the known mechanisms associated with checkpoint blockade resistance (CBR), and assess current strategies in the field that seek to overcome these mechanisms. In order to improve current therapies and develop new ones, the immunotherapy field still has an unmet need in identifying other molecules that act as immune checkpoints, and uncovering other mechanisms that promote CBR.

Regulating tumor innervation by nanodrugs potentiates cancer immunochemotherapy and relieve chemotherapy-induced neuropathic pain

Sympathetic nerves play a pivotal role in promoting tumor growth through crosstalk with tumor and stromal cells. Chemotherapy exacerbates the infiltration of sympathetic nerves into tumors, thereby providing a rationale for inhibiting sympathetic innervation to enhance chemotherapy. Here, we discovered that doxorubicin increases the density and activity of sympathetic nerves in breast cancer mainly by upregulating the expression of nerve growth factors (NGFs) in cancer cells. To address this, we developed a combination therapy by co-encapsulating small interfering RNA (siRNA) and doxorubicin within breast cancer-targeted poly (lactic-co-glycolic acid) (PLGA) nanoparticles, aiming to suppress NGF expression post-chemotherapy. Incorporating NGF blockade into the nanoplatform for chemotherapy effectively mitigated the chemotherapy-induced proliferation of sympathetic nerves. This not only bolstered the tumoricidal activity of chemotherapy, but also amplified its stimulatory impact on the antitumor immune response by increasing the infiltration of immunostimulatory cells into tumors while concurrently reducing the frequency of immunosuppressive cells. Consequently, the combined nanodrug approach, when coupled with anti-PD-L1 treatment, exhibited a remarkable suppression of primary and deeply metastatic tumors with minimal systematic toxicity. Importantly, the nanoplatform relieved chemotherapy-induced peripheral neuropathic pain (CIPNP) by diminishing the expression of pain mediator NGFs. In summary, this research underscores the significant potential of NGF knockdown in enhancing immunochemotherapy outcomes and presents a nanoplatform for the highly efficient and low-toxicity treatment of breast cancer.

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.

Osteoporosis induced by cellular senescence: A mathematical model

Osteoporosis is a disease characterized by loss of bone mass, where bones become fragile and more likely to fracture. Bone density begins to decrease at age 50, and a state of osteoporosis is defined by loss of more than 25%. Cellular senescence is a permanent arrest of normal cell cycle, while maintaining cell viability. The number of senescent cells increase with age. Since osteoporosis is an aging disease, it is natural to consider the question to what extend senescent cells induce bone density loss and osteoporosis. In this paper we use a mathematical model to address this question. We determine the percent of bone loss for men and women during age 50 to 100 years, and the results depend on the rate η of net formation of senescent cell, with η = 1 being the average rate. In the case η = 1, the model simulations are in agreement with empirical data. We also consider senolytic drugs, like fisetin and quercetin, that selectively eliminate senescent cells, and assess their efficacy in terms of reducing bone loss. For example, at η = 1, with estrogen hormonal therapy and early treatment with fisetin, bone density loss for women by age 75 is 23.4% (below osteoporosis), while with no treatment with fisetin it is 25.8% (osteoporosis); without even a treatment with estrogen hormonal therapy, bone loss of 25.3% occurs already at age 65.

Biomarkers and computational models for predicting efficacy to tumor ICI immunotherapy

Numerous studies have shown that immune checkpoint inhibitor (ICI) immunotherapy has great potential as a cancer treatment, leading to significant clinical improvements in numerous cases. However, it benefits a minority of patients, underscoring the importance of discovering reliable biomarkers that can be used to screen for potential beneficiaries and ultimately reduce the risk of overtreatment. Our comprehensive review focuses on the latest advancements in predictive biomarkers for ICI therapy, particularly emphasizing those that enhance the efficacy of programmed cell death protein 1 (PD-1)/programmed cell death-ligand 1 (PD-L1) inhibitors and cytotoxic T-lymphocyte antigen-4 (CTLA-4) inhibitors immunotherapies. We explore biomarkers derived from various sources, including tumor cells, the tumor immune microenvironment (TIME), body fluids, gut microbes, and metabolites. Among them, tumor cells-derived biomarkers include tumor mutational burden (TMB) biomarker, tumor neoantigen burden (TNB) biomarker, microsatellite instability (MSI) biomarker, PD-L1 expression biomarker, mutated gene biomarkers in pathways, and epigenetic biomarkers. TIME-derived biomarkers include immune landscape of TIME biomarkers, inhibitory checkpoints biomarkers, and immune repertoire biomarkers. We also discuss various techniques used to detect and assess these biomarkers, detailing their respective datasets, strengths, weaknesses, and evaluative metrics. Furthermore, we present a comprehensive review of computer models for predicting the response to ICI therapy. The computer models include knowledge-based mechanistic models and data-based machine learning (ML) models. Among the knowledge-based mechanistic models are pharmacokinetic/pharmacodynamic (PK/PD) models, partial differential equation (PDE) models, signal networks-based models, quantitative systems pharmacology (QSP) models, and agent-based models (ABMs). ML models include linear regression models, logistic regression models, support vector machine (SVM)/random forest/extra trees/k-nearest neighbors (KNN) models, artificial neural network (ANN) and deep learning models. Additionally, there are hybrid models of systems biology and ML. We summarized the details of these models, outlining the datasets they utilize, their evaluation methods/metrics, and their respective strengths and limitations. By summarizing the major advances in the research on predictive biomarkers and computer models for the therapeutic effect and clinical utility of tumor ICI, we aim to assist researchers in choosing appropriate biomarkers or computer models for research exploration and help clinicians conduct precision medicine by selecting the best biomarkers.

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.

Treatment of leishmaniasis with chemotherapy and vaccine: a mathematical model

Leishmaniasis, an infectious disease, manifests itself mostly in two forms, cutaneous leishmaniasis (CL) and, a more severe and potentially deadly form, visceral leishmaniasis (VL). The current control strategy for leishmaniasis relies on chemotherapy drugs such as sodium antimony gluconate (SAG) and meglumine antimoniate (MA). However, all these chemotherapy compounds have poor efficacy, and they are associated with toxicity and other adverse effects, as well as drug resistance. While research in vaccine development for leishmaniasis is continuously progressing, no vaccine is currently available. However, some experimental vaccines such as LEISH-F1+MPL-SE (V) have demonstrated some efficacy when used as drugs for CL patients. In this paper we use a mathematical model to address the following question: To what extent vaccine shots can enhance the efficacy of standard chemotherapy treatment of leishmaniasis? Starting with standard MA treatment of leishmaniasis and combining it with three injections of V , we find, by Day 84, that efficacy increased from 29% to 65-91% depending on the amount of the vaccine. With two or just one injection of V , efficacy is still very high, but there is a definite resurgence of the disease by end-time.

PD-1 and PD-L1: architects of immune symphony and immunotherapy breakthroughs in cancer treatment

PD-1 (Programmed Cell Death Protein-1) and PD-L1 (Programmed Cell Death Ligand-1) play a crucial role in regulating the immune system and preventing autoimmunity. Cancer cells can manipulate this system, allowing them to escape immune detection and promote tumor growth. Therapies targeting the PD-1/PD-L1 pathway have transformed cancer treatment and have demonstrated significant effectiveness against various cancer types. This study delves into the structure and signaling dynamics of PD-1 and its ligands PD-L1/PD-L2, the diverse PD-1/PD-L1 inhibitors and their efficacy, and the resistance observed in some patients. Furthermore, this study explored the challenges associated with the PD-1/PD-L1 inhibitor treatment approach. Recent advancements in the combination of immunotherapy with chemotherapy, radiation, and surgical procedures to enhance patient outcomes have also been highlighted. Overall, this study offers an in-depth overview of the significance of PD-1/PD-L1 in cancer immunotherapy and its future implications in oncology.

Combination of CAR‑T cell therapy and radiotherapy: Opportunities and challenges in solid tumors (Review)

Chimeric antigen receptor (CAR) T cell therapy has emerged as a new and breakthrough cancer immunotherapy. Although CAR-T cell therapy has made significant progress clinically in patients with refractory or drug-resistant hematological malignancies, there are numerous challenges in its application to solid tumor therapy, including antigen escape, severe toxic reactions, abnormal vascularization, tumor hypoxia, insufficient infiltration of CAR-T cells and immunosuppression. As a conventional mode of anti-tumor therapy, radiotherapy has shown promising effects in combination with CAR-T cell therapy by enhancing the specific immunity of endogenous target antigens, which promoted the infiltration and expansion of CAR-T cells and improved the hypoxic tumor microenvironment. This review focuses on the obstacles to the application of CAR-T technology in solid tumor therapy, the potential opportunities and challenges of combined radiotherapy and CAR-T cell therapy, and the review of recent literature to evaluate the best combination for the treatment of solid tumors.

Efficacy of therapies targeting TGF-β in solid tumors: a systematic review and meta-analysis of clinical trials

Aims: A comprehensive meta-analysis was conducted to explore the efficacy of TGF-β blockade therapies in solid tumors. Patients & methods: Results of overall survival (OS), progression-free survival (PFS), time to progression (TTP) and overall response rate (ORR) with their 95% CI were calculated. Also, subgroup analyses were conducted according to the categories of TGF-β blocker alone or combined with chemotherapy or radiotherapy. Results: Overall OS, PFS, TTP and ORR were 10.5 months (95% CI: 7.76-13.25), 2.54 months (95% CI: 1.66-3.43), 4.69 months (95% CI: 3.18-6.21) and 0.83% (95% CI: 0.82-0.85), respectively. Conclusion: Collectively, TGF-β blockade combined with chemotherapy or radiotherapy showed more favorable clinical outcomes than monotherapy using TGF-β blockade.

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.

Breast Cancer Exosomal microRNAs Facilitate Pre-Metastatic Niche Formation in the Bone: A Mathematical Model

Pre-metastatic niche is a location where cancer cells, separating from a primary tumor, find "fertile soil" for growth and proliferation, ensuring successful metastasis. Exosomal miRNAs of breast cancer are known to enter the bone and degrade it, which facilitates cancer cells invasion into the bone interior and ensures its successful colonization. In this paper, we use a mathematical model to first describe, in health, the continuous remodeling of the bone by bone-forming osteoblasts, bone-resorbing osteoclasts and the RANKL-OPG-RANK signaling system, which keeps the balance between bone formation and bone resorption. We next demonstrate how breast cancer exosomal miRNAs disrupt this balance, either by increasing or by decreasing the ratio of osteoclasts/osteoblasts, which results in abnormal high bone resorption or abnormal high bone forming, respectively, and in bone weakening in both cases. Finally we consider the case of abnormally high resorption and evaluate the effect of drugs, which may increase bone density to normal level, thus protecting the bone from invasion by cancer cells.

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.

Hyperprogression, a challenge of PD-1/PD-L1 inhibitors treatments: potential mechanisms and coping strategies

Immunotherapy is now a mainstay in cancer treatments. Programmed cell death 1 (PD-1)/programmed cell death ligand 1 (PD-L1) immune checkpoint inhibitor (ICI) therapies have opened up a new venue of advanced cancer immunotherapy. However, hyperprogressive disease (HPD) induced by PD-1/PD-L1 inhibitors caused a significant decrease in the overall survival (OS) of the patients, which compromise the efficacy of PD-1/PD-L1 inhibitors. Therefore, HPD has become an urgent issue to be addressed in the clinical uses of PD-1/PD-L1 inhibitors. The mechanisms of HPD remain unclear, and possible predictive factors of HPD are not well understood. In this review, we summarized the potential mechanisms of HPD and coping strategies that can effectively reduce the occurrence and development of HPD.

Natural Products in Preventing Tumor Drug Resistance and Related Signaling Pathways

Drug resistance is still an obstacle in cancer therapy, leading to the failure of tumor treatment. The emergence of tumor drug resistance has always been a main concern of oncologists. Therefore, overcoming tumor drug resistance and looking for new strategies for tumor treatment is a major focus in the field of tumor research. Natural products serve as effective substances against drug resistance because of their diverse chemical structures and pharmacological effects. We reviewed the signaling pathways involved in the development of tumor drug resistance, including Epidermal growth factor receptor (EGFR), Renin-angiotensin system (Ras), Phosphatidylinositol-3-kinase/protein kinase B (PI3K/Akt), Wnt, Notch, Transforming growth factor-beta (TGF-β), and their specific signaling pathway inhibitors derived from natural products. This can provide new ideas for the prevention of drug resistance in cancer therapy.

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.

TGF-β Signaling and Resistance to Cancer Therapy

The transforming growth factor β (TGF-β) pathway, which is well studied for its ability to inhibit cell proliferation in early stages of tumorigenesis while promoting epithelial-mesenchymal transition and invasion in advanced cancer, is considered to act as a double-edged sword in cancer. Multiple inhibitors have been developed to target TGF-β signaling, but results from clinical trials were inconsistent, suggesting that the functions of TGF-β in human cancers are not yet fully explored. Multiple drug resistance is a major challenge in cancer therapy; emerging evidence indicates that TGF-β signaling may be a key factor in cancer resistance to chemotherapy, targeted therapy and immunotherapy. Finally, combining anti-TGF-β therapy with other cancer therapy is an attractive venue to be explored for the treatment of therapy-resistant cancer.

Transforming Growth Factor-β: An Agent of Change in the Tumor Microenvironment

Transforming Growth Factor-β (TGF-β) is a key regulator of embryonic development, adult tissue homeostasis, and lesion repair. In tumors, TGF-β is a potent inhibitor of early stage tumorigenesis and promotes late stage tumor progression and metastasis. Here, we review the roles of TGF-β as well as components of its signaling pathways in tumorigenesis. We will discuss how a core property of TGF-β, namely its ability to change cell differentiation, leads to the transition of epithelial cells, endothelial cells and fibroblasts to a myofibroblastoid phenotype, changes differentiation and polarization of immune cells, and induces metabolic reprogramming of cells, all of which contribute to the progression of epithelial tumors.