Modeling putative therapeutic implications of exosome exchange between tumor and immune cells

Development of effective strategies to mobilize the immune system as a therapeutic modality in cancer necessitates a better understanding of the contribution of the tumor microenvironment to the complex interplay between cancer cells and the immune response. Recently, effort has been directed at unraveling the functional role of exosomes and their cargo of messengers in this interplay. Exosomes are small vesicles (30–200 nm) that mediate local and long-range communication through the horizontal transfer of information, such as combinations of proteins, mRNAs and microRNAs. Here, we develop a tractable theoretical framework to study the putative role of exosome-mediated cell–cell communication in the cancer–immunity interplay. We reduce the complex interplay into a generic model whose three components are cancer cells, dendritic cells (consisting of precursor, immature, and mature types), and killer cells (consisting of cytotoxic T cells, helper T cells, effector B cells, and natural killer cells). The framework also incorporates the effects of exosome exchange on enhancement/reduction of cell maturation, proliferation, apoptosis, immune recognition, and activation/inhibition. We reveal tristability—possible existence of three cancer states: a low cancer load with intermediate immune level state, an intermediate cancer load with high immune level state, and a high cancer load with low immune-level state, and establish the corresponding effective landscape for the cancer–immunity network. We illustrate how the framework can contribute to the design and assessments of combination therapies.Immunotherapeutic approaches have recently emerged as effective therapeutic modalities (1) exemplified by immune checkpoint blockade with anti–CTLA-4 to activate T-cells and induce tumor cell killing, which has been shown to be effective for some cancers but not others (2). A better understanding of the intricate interplay between cancer and the immune system, and of mechanisms of immune evasion and of hijacking of the host response by cancer cells, is relevant to the development of effective immunotherapeutic approaches (3–6).The immune-based suppression of tumor development and progression is mediated through nonspecific innate immunity and antigen-specific adaptive immunity (7). However, cancer cells can inhibit the immune response, thus evading suppression in multiple ways (8) (see below for details), and additionally hijack the immune system to their advantage (3, 4). The challenge to understand the tumor–immune interplay stems from the dynamic nature, and complexity and heterogeneity of both the cancer cells and the immune system and their interactions through the tumor microenvironment (9).Here we consider immune cells as consisting of macrophages (10), natural killer cells (11), cytotoxic T cells (12), helper T cells (13), and regulatory T cells (3). These various immune cells are produced, activated, and perform their functions separated by space and time, which contributes to additional complexity (14). Among the immune cells, dendritic cells (DCs) are the most efficient antigen-presenting cells to bridge innate immunity with adaptive immunity (15). DCs also secrete cytokines that promote the antitumor functions of both natural killer cells and macrophages (16, 17). We consider the tumor microenvironment as comprised of a heterogeneous population of cancer cells (18), stromal cells (19), and tumor-infiltrating immune cells (20). The interactions among these cell types contribute to tumor development and progression. Tumor-associated macrophages and cancer-associated fibroblasts regulate tumor metabolism and engender an immune-suppressive environment by secreting TGF-β and other cytokines (21). Fluctuations in energy sources and oxygen within a tumor contribute to malignant progression and cell phenotypic diversity (22, 23).Though secreted factors play critical roles in cell–cell communications, here we focus on the additional role of cell–cell communication mediated by the exchange of special extracellular lipid vesicles called exosomes (24). These nanovesicles of ∼30–200 nm are formed in the multivesicular bodies and then released from the cell into the extracellular space (25). The exosomes carry a broad range of cargo, including proteins, microRNAs, mRNAs, and DNA fragments, to specific target cells at a remote location (26). Membrane markers assign the exosomes to specific targeted cells. Notably, upon entering the target cell, the exosomes induce modulation of cell function and even identity switch (phenotypic, epigenetic, and even genetic) (27). Exosomes have recently emerged as playing an important role in the immune system interaction with tumors (28, 29). Tumor-derived exosomes can promote metastatic niche formation by influencing bone marrow-derived cells toward a prometastatic phenotype through upregulation of c-Met (29). DCs have been shown to induce tumor cell killing through release of exosomes that contain potent tumor-suppressive factors such as TNF and through activation of natural killer cells, cytotoxic T cells, and helper T cells (24, 30–32). However, tumor-derived exosomes (Tex) can directly inhibit the differentiation of DCs in bone marrow (33), which strongly inhibits the dendritic cell-mediated immune response to the tumor. In addition, Tex can also directly inhibit natural killer cells (34).Mathematical models have been devised to study the complex interactions of cancer and immune system, including those that consider spatial heterogeneity (as reviewed in ref. 35) and those that consider spatially homogeneous populations (as reviewed in ref. 36). Cancer–immunity models have been constructed to investigate the effects of therapy (37–39), cancer dormancy (40), and interactions with time delay (41). Other types of modeling methods have also been applied. For example, tumor growth has also been fitted to experimental data by artificial neural networks (42); a detailed network of cancer immune system has been modeled by multiple subset models (43).In this study, we have developed an exosome exchange-based cancer–immunity interplay (ECI) model, to incorporate the special role of DCs and exosome-mediated communications. Distinct from the previous approaches, our modeling strategy is adapted from methodology used in studies of gene regulatory circuits, allowing us to check the multistability features of the system (44). We find that, by including exosome exchange, the cancer–immunity interplay can give rise to three quasi-stable cancer states, which may be associated with the elimination/equilibrium/escape phases proposed in the immunoediting theory (45). The ECI model is also capable of explaining tumorigenesis by considering the time evolution of immune responses. Guided by the treatment simulations, we assess the effectiveness of various therapeutic protocols with and without time delay and noise.     

Angiographic lesion severity and subsequent myocardial infarction

We sought to determine the angiographic severity of coronary lesions leading to ST-segment elevation myocardial infarction (STEMI) and non-STEMI (NSTEMI) with a focus on determining the impact of interval from initial angiogram to subsequent clinical event. In the late 1980s angiographic data on lesion characteristics that culminated in STEMI and NSTEMI were obtained from angiograms obtained several months before MI. It is not clear whether the conclusions on lesion severity would be different if elapsed interval from baseline angiogram to clinical event was factored in the analysis. From 2003 through 2010, we identified 84 patients with NSTEMI and 41 patients with STEMI in vessels without previous intervention. These patients had ≥1 previous angiographic study at our center. Angiograms were reanalyzed with quantitative coronary angiography, and relevant clinical data were obtained from medical records. Similar to previous studies, 71% of patients with STEMI and 63% of patients with NSTEMI had <50% baseline stenosis at the culprit site when the interval from initial angiogram to MI was >3 months. Interestingly, lesions that led to STEMI ≤3 months after evaluation were more severe than those leading to STEMI in >3 months (59 ± 31% vs 36 ± 21%, p = 0.02) with 57% of lesions having >50% stenosis. Although most MIs occurred at sites that did not have significant obstruction when examined >3 months before MI, most baseline lesions showed significant luminal narrowing when examined ≤3 months before STEMI. In conclusion, high-grade coronary stenosis may be an important predictor of STEMI in subsequent months.