The intrinsic immunogenic properties of cancer cell lines,immunogenic cell death,and how these influence host antitumor immune responses

Modern cancer therapies often involve the combination of tumor-directed cytotoxic strategies and generation of a host antitumor immune response. The latter is unleashed by immunotherapies that activate the immune system generating a more immunostimulatory tumor microenvironment and a stronger tumor antigen-specific immune response. Studying the interaction between antitumor cytotoxic therapies, dying cancer cells, and the innate and adaptive immune system requires appropriate experimental tumor models in mice. In this review, we discuss the immunostimulatory and immunosuppressive properties of cancer cell lines commonly used in immunogenic cell death (ICD) studies being apoptosis or necroptosis. We will especially focus on the antigenic component of immunogenicity. While in several cancer cell lines the epitopes of endogenously expressed tumor antigens are known, these intrinsic epitopes are rarely determined in experimental apoptotic or necroptotic ICD settings. Instead by far the most ICD research studies investigate the antigenic response against exogenously expressed model antigens such as ovalbumin or retroviral epitopes (e.g., AH1). In this review, we will argue that the immune response against endogenous tumor antigens and the immunopeptidome profile of cancer cell lines affect the eventual biological readouts in the typical prophylactic tumor vaccination type of experiments used in ICD research, and we will propose additional methods involving immunopeptidome profiling, major histocompatibility complex molecule expression, and identification of tumor-infiltrating immune cells to document intrinsic immunogenicity following different cell death modalities.Subject terms: Cancer models, Antigen-presenting cells, Immune cell death     

A primer on cancer immunology and immunotherapy

The role of immunity in cancer has been abundantly demonstrated in murine tumor models as well as in man. Induction of clinically effective antitumor immune responses, based on this information, in patients with cancer however, remains elusive. This is not because tumors lack recognizable antigens [in fact there is evidence that there are thousands of potential novel targets in each tumor cell] but rather due to the fact that the induction of responses is not adequate nor particularly well understood. Tumors seem to be rather effective at limiting immune responses. Many of the molecularly defined antigens that have been detected on tumor cells are derived from self-proteins and as such are subject to tolerizing mechanisms. Such tumors have also developed escape mechanisms capable of evading or suppressing immune responses. Understanding the role of dendritic cells during the effector phase of the immune response and the complex interactions of stromal, immune, and tumor cells in the tumor microenvironment represent the next challenges to be understood for tumor immunology.This is a summary of the work presented at the First Cancer Immunology and Immunotherapy Summer School, 8–13 September 2003, Ionian Village, Peloponnese, Greece     

Dissecting tumor responsiveness to immunotherapy: the experience of peptide-based melanoma vaccines

Recent years have witnessed important breakthroughs in our understanding of tumor immunology. A variety of immunotherapeutic strategies has shown that immune manipulation can induce the regression of established cancer in humans. The identification of the genes encoding tumor-associated antigens (TAA) and the development of means for immunizing against these antigens have opened new avenues for the development of an effective anticancer immunotherapy. However, an efficient immune response against tumor requires an intricate cross-talk between cancer and immune system cells, which is still poorly understood. Only when the molecular basis underlying tumor susceptibility to an immune response is deciphered could new therapeutic strategies be designed to fit biologically defined mechanisms of cancer immune rejection. In this article, we address some of the critical issues that have been identified in cancer immunotherapy, in part from our own studies on immune therapies in melanoma patients treated with peptide-based vaccination regimens. This is not meant to be a comprehensive overview of the immunological phenomena accompanying cancer patient vaccination but rather emphasizes some emergent findings, puzzling controversies and unanswered questions that characterize this complex field of oncology. In addition to reviewing the main immunological concepts underlying peptide-based vaccination, we also review the available data regarding naturally occurring and therapeutically induced anticancer immune response, both at the peripheral and intratumoral level. The hypothesized role of innate immunity in predetermining tumor responsiveness to immunotherapeutic manipulation is also discussed.     

microRNA在实体肿瘤免疫反应中的调节作用

免疫反应的作用逐渐成为调节各种复杂癌症的关键因素。免疫治疗也逐渐成为癌症肿瘤的有效干预方式。肿瘤微环境包含不同类型的免疫细胞,这有助于调节抗肿瘤信号中先天性和适应性免疫系统之间的细微平衡。在这种环境下,肿瘤细胞与免疫细胞之间相互关联的机制有待广泛阐明,但目前已被证明,多种microRNA在实体肿瘤相关免疫细胞的发育和功能中起调控作用,其通过肿瘤及免疫细胞介导免疫抑制或免疫刺激因子分泌增强或抑制免疫应答,靶向调控肿瘤发生的相关免疫途径,从而在癌症起始、转移进展的所有阶段中起关键作用,近而在肿瘤免疫治疗中寻找新的治疗靶点。本文针对microRNA在肿瘤免疫反应中的相关调节进行综述。     

Cancer: a Systems Biology disease

Cancer research has focused on the identification of molecular differences between cancerous and healthy cells. The emerging picture is overwhelmingly complex. Molecules out of many parallel signal transduction pathways are involved. Their activities appear to be controlled by multiple factors. The action of regulatory circuits, cross-talk between pathways and the non-linear reaction kinetics of biochemical processes complicate the understanding and prediction of the outcome of intracellular signaling. In addition, interactions between tumor and other cell types give rise to a complex supra-cellular communication network. If cancer is such a complex system, how can one ever predict the effect of a mutation in a particular gene on a functionality of the entire system? And, how should one go about identifying drug targets? Here, we argue that one aspect is to recognize, where the essence resides, i.e. recognize cancer as a Systems Biology disease. Then, more cancer biologists could become systems biologists aiming to provide answers to some of the above systemic questions. To this aim, they should integrate the available knowledge stemming from quantitative experimental results through mathematical models. Models that have contributed to the understanding of complex biological systems are discussed. We show that the architecture of a signaling network is important for determining the site at which an oncologist should intervene. Finally, we discuss the possibility of applying network-based drug design to cancer treatment and how rationalized therapies, such as the application of kinase inhibitors, may benefit from Systems Biology.     

Mutations and regulatory anomalies effecting tumor cell immune functions

The immune system is capable of interacting with tumor cells in such a way as to lead to tumor cell death, and this knowledge has inspired therapies to manipulate patient immune systems to eradicate cancer. However, tumor cells are able to mitigate the antitumor immune response, a fact that has rarely been addressed in the design of immunotherapies. There are many different tumor cell immune functions that play a role in mitigating the antitumor immune response. In some cases, these functions appear to be intimately associated with the tumor cell abnormalities that lead to loss of growth control, such as the cases where classical tumor suppressor proteins regulate tumor cell immune function genes. In other cases, tumor cell mutations appear to affect only the antitumor response, such as tumor cell mutations that eliminate MHC class I expression. Here I review the bases for tumor cell immune functions, noting in particular where tumor cell mutations, the gold standard for identifying a tumor-specific function, are known to be responsible for the tumor cell immune function. This review also discusses other known regulatory anomalies, in the absence of a known mutation, that are apparently important for tumor development and that regulate tumor cell immune functions. Surprisingly, in many cases where the tumor cell immune function is well understood in terms of its effect on the antitumor immune response, the tumor abnormality underlying the tumor cell immune function is completely uncharacterized.     

免疫反应动力学的几个数学模型

免疫系统对抗原刺激的应答过程非常复杂,由抗原刺激导致抗体产生的现象,可借助数学模型的研究获得有意义的结果。本文讨论有关抗体产生与免疫反应的动力学的问题,介绍有关的数学模型,并根据近斯免疫学研究的进展分析了若干模型。     

Multi-Stability and Multi-Instability Phenomena in a Mathematical Model of Tumor-Immune-Virus Interactions

Recent advances in virology, gene therapy, and molecular and cell biology have provided insight into the mechanisms through which viruses can boost the anti-tumor immune response, or can infect and directly kill tumor cells. A recent experimental report (Bridle et al. in Molec. Ther. 18(8):1430–1439, 2010) showed that a sequential treatment approach that involves two viruses that carry the same tumor antigen leads to an improved anti-tumor response compared to the effect of each virus alone. In this article, we derive a mathematical model to investigate the anti-tumor effect of two viruses, and their interactions with the immune cells. We discuss the conditions necessary for permanent tumor elimination and, in this context, we stress the importance of investigating the long-term effect of non-linear interactions. In particular, we discuss multi-stability and multi-instability, two complex phenomena that can cause abrupt transitions between different states in biological and physical systems. In the context of cancer immunotherapies, the transitions between a tumor-free and a tumor-present state have so far been associated with the multi-stability phenomenon. Here, we show that multi-instability can also cause the system to switch from one state to the other. In addition, we show that the multi-stability is driven by the immune response, while the multi-instability is driven by the presence of the virus.     

Pilot study of a novel combination of two therapeutic vaccines in advanced non-small-cell lung cancer patients

Cancer vaccines contain tumor antigens in a pro-inflammatory context with the purpose to generate potent antitumor immune responses. However, tumor cells develop different immunosuppressive mechanisms that limit the effectiveness of an anticancer immune response. Therefore, therapeutic vaccine treatment alone is usually not sufficient to generate tumor regression or survival improvement, especially in the advanced disease scenario in which most clinical studies have been conducted. Combining cancer vaccines with different anticancer therapies such as chemotherapy, radiotherapy and other immunotherapeutic agents has had different levels of success. However, the combination of cancer vaccines with different mechanisms of action has not been explored in clinical trials. To address this issue, the current review summarizes the main clinical and immunological results obtained with two different therapeutic vaccines used in advanced non-small-cell lung cancer patients, inducing an immune response against epidermal growth factor (CIMAvax-EGF) and NGcGM3 ganglioside (racotumomab). We also discuss preliminary findings obtained in a trial of combination of these two vaccines and future challenges with these therapies.     

CMTM家族成员在肿瘤细胞中的分子机制及潜在临床价值

恶性肿瘤已成为危害人类健康的重要杀手,针对肿瘤的研究也成为当今医学界的热点.含有MARVEL跨膜结构域的趋化素样因子基因家族(CKLF-like MARVEL transmembrane domain containing family of genes,CMTM family),原名人类趋化素样因子超家族(chemo...     

Antitumor immunity and cellular cancer therapies

The identification of tumor specific antigens has provided important advance in tumor immunology. It is now established that specific cytotoxic T lymphocytes (CTL) and natural killer cells infiltrate tumor tissues and are effector cells able to control tumor growth. However, such a natural antitumor immunity has limited effects in cancer patients. Failure of host defenses against tumor is consecutive to several mechanisms which are becoming targets to design new immunotherapeutic approaches. CTL are critical components of the immune response to human tumors and induction of strong CTL responses is the goal of most current vaccine strategies. Effectiveness of cytokine therapy, cancer vaccines and injection of cells improving cellular immunity have been established in tumor grafted murine models. Clinical trials are underway. To day, interest is particularly focused on cell therapy: injected cells are either "ready to use" effector cells (lymphocytes) or antigen presenting cells able to induce a protective immune reaction in vivo (dendritic cells). The challenge ahead lie in the careful optimization of the most promising strategies in clinical situation.     

Modelling the Immune Response to Cancer: An Individual-Based Approach Accounting for the Difference in Movement Between Inactive and Activated T Cells

A growing body of experimental evidence indicates that immune cells move in an unrestricted search pattern if they are in the pre-activated state, whilst they tend to stay within a more restricted area upon activation induced by the presence of tumour antigens. This change in movement is not often considered in the existing mathematical models of the interactions between immune cells and cancer cells. With the aim to fill such a gap in the existing literature, in this work we present a spatially structured individual-based model of tumour–immune competition that takes explicitly into account the difference in movement between inactive and activated immune cells. In our model, a Lévy walk is used to capture the movement of inactive immune cells, whereas Brownian motion is used to describe the movement of antigen-activated immune cells. The effects of activation of immune cells, the proliferation of cancer cells and the immune destruction of cancer cells are also modelled. We illustrate the ability of our model to reproduce qualitatively the spatial trajectories of immune cells observed in experimental data of single-cell tracking. Computational simulations of our model further clarify the conditions for the onset of a successful immune action against cancer cells and may suggest possible targets to improve the efficacy of cancer immunotherapy. Overall, our theoretical work highlights the importance of taking into account spatial interactions when modelling the immune response to cancer cells.     

Autologous melanoma-induced activation of regulatory T cells that suppress cytotoxic response

The host immune response toward autologous human cancer is subject to regulation by the immunoregulatory network. We show that certain CD4+ T cell clones, derived from melanoma involved lymph node lymphocytes and from PBL stimulated by autologous melanoma cells, selectively down-regulated the induction of cytotoxic immune response of PBL against the respective autologous melanoma cells in two autologous systems. In both systems, only the generation of cytotoxic response against the autologous melanoma cells were suppressed. Cytotoxic response against EBV-infected autologous lymphoblastoid cell line in one case and cytotoxic responses against allogeneic targets in the other were not affected. In addition to suppressor activity selectively expressed against the autologous melanoma cells, the T cell clones up-regulated their Tac receptors when cocultured with the autologous melanoma cells and APC. These results support the existence of a putative tumor Ag-driven activation of regulatory T cells that affect cytotoxic immune response, in vitro, against autologous human melanoma.     

Simple network motifs can capture key characteristics of the floral transition in Arabidopsis

The floral transition is a key decision during plant development. While different species have evolved diverse pathways to respond to different environmental cues to flower in the correct season, key properties such as irreversibility and robustness to fluctuating signals appear to be conserved. We have used mathematical modeling to demonstrate how minimal regulatory networks of core components are sufficient to capture these behaviors. Simplified models inevitably miss finer details of the biological system, yet they provide a tractable route to understanding the overall system behavior. We combined models with experimental data to qualitatively reproduce characteristics of the floral transition and to quantitatively scale the network to fit with available leaf numbers. Our study highlights the value of pursuing an iterative approach combining modeling with experimental work to capture key features of complex systems.     

Hybrid semi-parametric mathematical systems: bridging the gap between systems biology and process engineering

Systems biology is an integrative science that aims at the global characterization of biological systems. Huge amounts of data regarding gene expression, proteins activity and metabolite concentrations are collected by designing systematic genetic or environmental perturbations. Then the challenge is to integrate such data in a global model in order to provide a global picture of the cell. The analysis of these data is largely dominated by nonparametric modelling tools. In contrast, classical bioprocess engineering has been primarily founded on first principles models, but it has systematically overlooked the details of the embedded biological system. The full complexity of biological systems is currently assumed by systems biology and this knowledge can now be taken by engineers to decide how to optimally design and operate their processes. This paper discusses possible methodologies for the integration of systems biology and bioprocess engineering with emphasis on applications involving animal cell cultures. At the mathematical systems level, the discussion is focused on hybrid semi-parametric systems as a way to bridge systems biology and bioprocess engineering.     

Predictive Modeling of Neuroblastoma Growth Dynamics in Xenograft Model After Bevacizumab Anti-VEGF Therapy

Neuroblastoma is the leading cause of cancer death in young children. Although treatment for neuroblastoma has improved, the 5-year survival rate of patients still remains less than half. Recent studies have indicated that bevacizumab, an anti-VEGF drug used in treatment of several other cancer types, may be effective for treating neuroblastoma as well. However, its effect on neuroblastoma has not been well characterized. While traditional experiments are costly and time-consuming, mathematical models are capable of simulating complex systems quickly and inexpensively. In this study, we present a model of vascular tumor growth of neuroblastoma IMR-32 that is complex enough to replicate experimental data across a range of tumor cell properties measured in a suite of in vitro and in vivo experiments. The model provides quantitative insight into tumor vasculature, predicting a linear relationship between vasculature and tumor volume. The tumor growth model was coupled with known pharmacokinetics and pharmacodynamics of the VEGF blocker bevacizumab to study its effect on neuroblastoma growth dynamics. The results of our model suggest that total administered bevacizumab concentration per week, as opposed to dosage regimen, is the major determining factor in tumor suppression. Our model also establishes an exponentially decreasing relationship between administered bevacizumab concentration and tumor growth rate.