Search

The tumor microenvironment (TME) plays an important role in the development of tumors. Immunoregulatory cells and cytokines facilitate cancer cells to avoid immune surveillance. Overexpression of immune checkpoint molecules such as CTLA-4 and PD-1/PD-L1 inhibits immune function and enables cancer cells to avoid clearance by the immune system. Thus, minimizing tumor immunosuppression could be an important strategy for cancer therapy. Currently, many immune checkpoint-targeted drugs, such as PD-1/PD-L1 inhibitors, have been approved for marketing and have shown unique advantages in the clinical treatment of cancers. The concept of "strengthening resistance to eliminate pathogenic factors" in traditional Chinese medicine (TCM) is consistent with the immunotherapy of cancer. According to previous studies, the role of TCM in tumor immunotherapy is mainly associated with the positive regulation of natural killer cells, CD8/CD4 T cells, dendritic cells, M2 macrophages, interleukin-2, tumor necrosis factor- α , and IFN- γ , as well as with the negative regulation of Tregs, myeloid-derived suppressor cells, cancer-associated fibroblasts, PD-1/PD-L1, transforming growth factor- β , and tumor necrosis factor- β. This paper summarizes the current research on the effect of TCM targeting the TME, and further introduces the research progress on studying the effects of TCM on immune checkpoints. Modern pharmacological studies have demonstrated that TCM can directly or indirectly affect the TME by inhibiting the overexpression of immune checkpoint molecules and enhancing the efficacy of tumor immunotherapy. TCM with immunomodulatory stimulation could be the key factor to achieve benefits from immunotherapy for patients with non-inflammatory, or "cold", tumors. [ABSTRACT FROM AUTHOR]

Abdominal aortic aneurysm (AAA) is a localized enlargement of the abdominal aorta, such that the diameter exceeds 3 cm. The natural history of AAA is progressive growth leading to rupture, an event that carries up to 90% risk of mortality. Hence there is a need to predict the growth of the diameter of the aorta based on the diameter of a patient’s aneurysm at initial screening and aided by non-invasive biomarkers. IL-6 is overexpressed in AAA and was suggested as a prognostic marker for the risk in AAA. The present paper develops a mathematical model which relates the growth of the abdominal aorta to the serum concentration of IL-6. Given the initial diameter of the aorta and the serum concentration of IL-6, the model predicts the growth of the diameter at subsequent times. Such a prediction can provide guidance to how closely the patient’s abdominal aorta should be monitored. The mathematical model is represented by a system of partial differential equations taking place in the aortic wall, where the media is assumed to have the constituency of an hyperelastic material. [ABSTRACT FROM AUTHOR]

Crohn’s Disease (CD) results from inappropriate response toward commensal flora. Earlier studies described CD as a Th1 mediated disease. Current models view both phenotypes as a continuum of various permutations between Th1, Th2 and Th17 pathways compounded by a range of Treg disfunctions. In the present paper, we develop a mathematical model, by a system of differential equations, which describe the dynamic relations among these T cells and their cytokines. The model identities four groups of CD patients according to up/down regulation of Th1 and Th2. The model simulations show that immunosuppression by TNF-α blockage benefits the group with Th1High/Th2Low while, by contrast, the group with Th1Low/Th2High will benefit from immune activation. [ABSTRACT FROM AUTHOR]

Psoriasis is a recurrent, chronic, immune-mediated, systemic inflammatory disease of the skin, joints, and other organic systems. After atopic dermatitis, chronic stationary psoriasis is the most common inflammatory skin disease, affecting an average of 2–4% of the world's population. The disease carries a significant burden due to its numerous comorbidities and the major impact on patients' social and emotional aspects of life. According to current knowledge, psoriasis is a multifactorial disease that occurs in genetically predisposed individuals under various environmental factors, which trigger an immune response disorder with a series of complex inflammatory cascades. The disease is initiated and maintained by mutual interaction of the innate and adaptive immune cells, primarily dendritic cells, T lymphocytes, and keratinocytes, whose leading role alternates at different stages of the disease, consisting mainly in the IL-23/Th17 pathway. Inflammatory events result in consequent epidermal and dermal changes and evolution of the characteristic psoriatic phenotype, respectively. This paper aims to present a comprehensive overview of current knowledge on psoriasis genetic and environmental etiological factors, immunopathogenesis, and the leading cellular and cytokine participants in the inflammatory pathways of this disease. [ABSTRACT FROM AUTHOR]

Simple Summary: Osteosarcoma is the most common primary bone tumor and has a poor prognosis. Therefore, it is important to understand the mechanism of the development of osteosarcoma to overcome therapy resistance. Several mathematical models have been developed to study the initiation and progression of many cancer types. However, there are currently no mathematical models for the progression of osteosarcoma, to the best of our knowledge. In this work, we develop a data-driven mathematical model to analyze the impact of the immune cell interactions on the growth of osteosarcoma tumors that have distinct immune patterns. Our model provides a foundation for investigating the effect of various treatments on the dynamics of key players in the primary tumor, including immune cells and cytokines, and ultimately the whole tumor. As the immune system has a significant role in tumor progression, in this paper, we develop a data-driven mathematical model to study the interactions between immune cells and the osteosarcoma microenvironment. Osteosarcoma tumors are divided into three clusters based on their relative abundance of immune cells as estimated from their gene expression profiles. We then analyze the tumor progression and effects of the immune system on cancer growth in each cluster. Cluster 3, which had approximately the same number of naive and M2 macrophages, had the slowest tumor growth, and cluster 2, with the highest population of naive macrophages, had the highest cancer population at the steady states. We also found that the fastest growth of cancer occurred when the anti-tumor immune cells and cytokines, including dendritic cells, helper T cells, cytotoxic cells, and IFN- γ , switched from increasing to decreasing, while the dynamics of regulatory T cells switched from decreasing to increasing. Importantly, the most impactful immune parameters on the number of cancer and total cells were the activation and decay rates of the macrophages and regulatory T cells for all clusters. This work presents the first osteosarcoma progression model, which can be later extended to investigate the effectiveness of various osteosarcoma treatments. [ABSTRACT FROM AUTHOR]