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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]

In recent years, due to advancements in medical conditions and the development of scientific research, the fundamental research of TCM antitumor treatments has progressed from the cellular level to the molecular and genetic levels. Previous studies have demonstrated the significant role of traditional Chinese medicine (TCM) in antitumor therapy through various mechanisms and pathways. Its mechanism of action is closely associated with cancer biology across different stages. This includes inhibiting tumor cell proliferation, blocking invasion and metastasis to surrounding tissues, inducing tumor cell apoptosis, inhibiting tumor angiogenesis, regulating immune function, maintaining genome stability, preventing mutation, and regulating cell energy metabolism. The use of TCM for eliciting antitumor effects not only has a good therapeutic effect and low side effects, it also provides a solid theoretical basis for clinical treatment and medication. This paper reviews the mechanism of the antitumor effects of TCM based on tumor characteristics. Through our review, we found that TCM not only directly inhibits tumors, but also enhances the body's immunity, thereby indirectly inducing an antitumor effect. This function aligns with the TCM theory of "strengthening the body's resistance to eliminate pathogenic factors". Furthermore, TCM will play a significant role in tumor treatment in clinical settings. [ABSTRACT FROM AUTHOR]

This paper presents a mathematical framework for modeling the dynamics of heterogeneous populations. Models describing local and non-local growth and transport processes appear in a variety of applications, such as crowd dynamics, tissue regeneration, cancer development and coagulation-fragmentation processes. The diverse applications pose a common challenge to mathematicians due to the multiscale nature of the structures that underlie the system's self-organization and control. Similar abstract mathematical problems arise when formulating problems in the language of measure evolution on a multi-faceted state space. Motivated by these observations, we propose a general mathematical framework for nonlinear structured population models on abstract metric spaces, which are only assumed to be separable and complete. We exploit the structure of the space of non-negative Radon measures with the dual bounded Lipschitz distance (flat metric), which is a generalization of the Wasserstein distance, capable of addressing non-conservative problems. The formulation of models on general metric spaces allows considering infinite-dimensional state spaces or graphs and coupling discrete and continuous state transitions. This opens up exciting possibilities for modeling single-cell data, crowd dynamics or coagulation-fragmentation processes. [ABSTRACT FROM AUTHOR]

In this paper we discuss some natural limitations in quantitative inference about the frequency, correlation and ordering of genetic events occurring in the course of tumor development. We consider a simple, yet frequently used experimental design, under which independent tumors are examined once for the presence/absence of specific mutations of interest. The most typical factors that affect the inference on the chronological order of genetic events are: a possible dependence of mutation rates, the sampling bias that arises from the observation process and small sample sizes. Our results clearly indicate that just these three factors alone may dramatically distort the outcome of data analysis, thereby leading to estimates of limited utility as an underpinning for mechanistic models of carcinogenesis. [ABSTRACT FROM AUTHOR]

The generally accepted Moolgavkar's theory of carcinogenesis assumes that all cancers are clonal, i.e. that they arise from progressive genetic deregulation in a cell pedigree originating from a single ancestral cell.18 However, recently the clonal theory has been challenged by the field theory of carcinogenesis, which admits the possibility of simultaneous changes in tissue subject to carcinogenic agents, such as tobacco smoke in lung cancer. Axelrod et al.1 formulated a more detailed framework, in which partially transformed cells depend in a mutualistic way on growth factors they produce, in this way enabling these cells to proliferate and undergo further transformations. On the other hand, the field theory assumes spatial distribution of precancerous cells and indeed there exists evidence that early-stage precancerous lesions in lung cancer progress along linear, tubular, or irregular surface structures. This seems to be the case for the atypical adenomatous hyperplasia (AAH),10 a likely precursor of adenocarcinoma of the lung. In this paper we explore the consequences of linking the model of spatial growth of precancerous cells,12 with the mutualistic hypothesis. We investigate the solutions of the model using analytical and computational techniques. The picture emerging from our modelling indicates that production of growth factors by cells considered may lead to diffusion-driven instability, which in turn may lead either to decay of both population, or to emergence of local growth foci, represented by spike-like solutions. Mutualism may, in some situations, increase the stability of solutions. One important conclusion is that models of field carcinogenesis, which include spatial effects, generally have very different behaviour compared to ODE models. [ABSTRACT FROM AUTHOR]

This paper presents result of QSTR (quantitative structure-toxicity relationship) study obtained using the PRECLAV software. The dependent property is toxicity against rat (Rattus norvegicus), measured by TD50 values. The calibration/training/learning set includes 49 molecules having a very high chemical diversity. There are five outliers in calibration set. In the presence of outliers the predictive power of QSTR is very low (r2 = 0.5425, F = 10.4, $r^{2}_{CV} = 0.4387$). After elimination of outliers the predictive power of QSTR is much higher (r2 = 0.9078, F = 44.3, $r^{2}_{CV}=0.8776$). All eight predictors are nonlinear functions (parabolic and products) of descriptors. The LogP is not predictor. Presence of C = CH2 and N-NO molecular fragments increases toxicity. Presence of C6H4 molecular fragment decreases toxicity. [ABSTRACT FROM AUTHOR]

Induction of antitumor immunity by vaccination is one of the major current immunotherapy strategies. We present a mathematical model of the competition between immune cells and mammary carcinogenesis under the effect of Triplex vaccine. The model describes both humoral and cell-mediated immune responses against cancer cells. The control of the cancer cells growth occurs through the application of the pulse vaccination. Here we determine the relationship between the strength of the vaccine and the time required to eradicate cancer cells, and we present some simulations to illustrate our theoretical results, namely, the total cancer cells depletion, which is influenced by competition occurs among the immune and cancer cells. [ABSTRACT FROM AUTHOR]

Colorectal cancers are the third most common type of cancer. They originate from intestinal crypts, glands that descend from the intestinal lumen into the underlying connective tissue. Normal crypts are thought to exist in a dynamic equilibrium where the rate of cell production at the base of a crypt is matched by that of loss at the top. Understanding how genetic alterations accumulate and proceed to disrupt this dynamic equilibrium is fundamental to understanding the origins of colorectal cancer. Colorectal cancer emerges from the interaction of biological processes that span several spatial scales, from mutations that cause inappropriate intracellular responses to changes at the cell/tissue level, such as uncontrolled proliferation and altered motility and adhesion. Multiscale mathematical modelling can provide insight into the spatiotemporal organisation of such a complex, highly regulated and dynamic system. Moreover, the aforementioned challenges are inherent to the multiscale modelling of biological tissue more generally. In this review we describe the mathematical approaches that have been applied to investigate multiscale aspects of crypt behavior, highlighting a number of model predictions that have since been validated experimentally. We also discuss some of the key mathematical and computational challenges associated with the multiscale modelling approach. We conclude by discussing recent efforts to derive coarse-grained descriptions of such models, which may offer one way of reducing the computational cost of simulation by leveraging well-established tools of mathematical analysis to address key problems in multiscale modelling. [ABSTRACT FROM AUTHOR]

Human carcinogenesis may be thought of as Darwinian evolution of cells within the body (somatic evolution). As such, it may be modeled using evolutionary game theory. Winners in this game proliferate while losers become extinct. Here evolutionary dynamics are proposed that model the rate of somatic evolution. The speed at which somatic evolution proceeds determines whether a clinically significant cancer will emerge in the lifetime of the individual. The underlying model has an associated adaptive landscape that illustrates the evolutionary potential of cells due to the selection forces and microenvironment. Normal cells have a novel adaptive landscape that permits both coexistence and invasion of mutant phenotypes. A mutant cellular population does not initially form a malignancy because of constraints resulting in limited growth and nonlethal coexistence. However this coexistence deforms the local adaptive landscape resulting in the potential for evolution to drive the mutant cells to an unoccupied fitness peak resulting in an evasive cancer. There are two factors that determine this evolutionary potential. The first factor involves a relaxation of proliferation constraints and the second involves an increase in nutrients thereby removing a barrier allowing the mutant cells to climb to an adaptive peak. One unfortunate consequence of the cancer reaching this peak is that it starts producing acid that further contributes to the demise of the normal cells. [ABSTRACT FROM AUTHOR]