Tumor-immune metaphenotypes orchestrate an evolutionary bottleneck that promotes metabolic transformation.

Introduction: Metabolism plays a complex role in the evolution of cancerous tumors, including inducing a multifaceted effect on the immune system to aid immune escape. Immune escape is, by definition, a collective phenomenon by requiring the presence of two cell types interacting in close proximity: tumor and immune. The microenvironmental context of these interactions is influenced by the dynamic process of blood vessel growth and remodelling, creating heterogeneous patches of well-vascularized tumor or acidic niches. Methods: Here, we present a multiscale mathematical model that captures the phenotypic, vascular, microenvironmental, and spatial heterogeneity which shapes acid-mediated invasion and immune escape over a biologically-realistic time scale. The model explores several immune escape mechanisms such as i) acid inactivation of immune cells, ii) competition for glucose, and iii) inhibitory immune checkpoint receptor expression (PD-L1). We also explore the efficacy of anti-PD-L1 and sodium bicarbonate buffer agents for treatment. To aid in understanding immune escape as a collective cellular phenomenon, we define immune escape in the context of six collective phenotypes (termed "meta-phenotypes"): Self-Acidify, Mooch Acid, PD-L1 Attack, Mooch PD-L1, Proliferate Fast, and Starve Glucose. Results: Fomenting a stronger immune response leads to initial benefits (additional cytotoxicity), but this advantage is offset by increased cell turnover that leads to accelerated evolution and the emergence of aggressive phenotypes. This creates a bimodal therapy landscape: either the immune system should be maximized for complete cure, or kept in check to avoid rapid evolution of invasive cells. These constraints are dependent on heterogeneity in vascular context, microenvironmental acidification, and the strength of immune response. Discussion: This model helps to untangle the key constraints on evolutionary costs and benefits of three key phenotypic axes on tumor invasion and treatment: acid-resistance, glycolysis, and PD-L1 expression. The benefits of concomitant anti-PD-L1 and buffer treatments is a promising treatment strategy to limit the adverse effects of immune escape.

Duality of B Cell-CXCL13 Axis in Tumor Immunology.

Tumor immunity is a rapidly evolving area of research consisting of many possible permutations of immune cell tumor interactions that are dependent upon cell type, tumor type, and stage in tumor progression. At the same time, the majority of cancer immunotherapies have been focused on modulating the T cell-mediated antitumor immune response and have largely ignored the potential utility that B cells possess with respect to tumor immunity. Therefore, this motivated an exploration into the role that B cells and their accompanying chemokine, CXCL13, play in tumor immunity across multiple tumor types. Both B cells and CXCL13 possess dualistic impacts on tumor progression and tumor immunity which is furthered detail in this review. Specifically, various B cells subtypes are able to suppress or enhance several important immunological functions. Paradoxically, CXCL13 has been shown to drive several pro-growth and invasive signaling pathways across multiple tumor types, while also, correlating with improved survival and immune cell tumor localization in other tumor types. Potential tools for better elucidating the mechanisms by which B cells and CXCL13 impact the antitumor immune response are also discussed. In addition, multiples strategies are proposed for modulating the B cell-CXCL13 axis for cancer immunotherapies.

Joint reconstruction and classification of tumor cells and cell interactions in melanoma tissue sections with synthesized training data.

PURPOSE: Cancers are almost always diagnosed by morphologic features in tissue sections. In this context, machine learning tools provide new opportunities to describe tumor immune cell interactions within the tumor microenvironment and thus provide phenotypic information that might be predictive for the response to immunotherapy. METHODS: We develop a machine learning approach using variational networks for joint image denoising and classification of tissue sections for melanoma, which is an established model tumor for immuno-oncology research. The manual annotation of real training data would require substantial user interaction of experienced pathologists for each single training image, and the training of larger networks would rely on a very large number of such data sets with ground truth annotation. To overcome this bottleneck, we synthesize training data together with a proper tissue structure classification. To this end, a stochastic data generation process is used to mimic cell morphology, cell distribution and tissue architecture in the tumor microenvironment. Particular components of this tool are random placement and rotation of a large number of patches for presegmented cell nuclei, a stochastic fast marching approach to mimic the geometry of cells and texture generation based on a color covariance analysis of real data. Here, the generated training data reflect a large range of interaction patterns. RESULTS: In several applications to histological tissue sections, we analyze the efficiency and accuracy of the proposed approach. As a result, depending on the scenario considered, almost all cells and nuclei which ought to be detected are actually marked as classified and hardly any misclassifications occur. CONCLUSIONS: The proposed method allows for a computer-aided screening of histological tissue sections utilizing variational networks with a particular emphasis on tumor immune cell interactions and on the robust cell nuclei classification.

Perspective on nanochannels as cellular mediators in different disease conditions.

Tunnelling nanotubes (TNTs), also known as membrane nanochannels, are actin-based structures that facilitate cytoplasmic connections for rapid intercellular transfer of signals, organelles and membrane components. These dynamic TNTs can form de novo in animal cells and establish complex intercellular networks between distant cells up to 150 µm apart. Within the last decade, TNTs have been discovered in different cell types including tumor cells, macrophages, monocytes, endothelial cells and T cells. It has also been further elucidated that these nanotubes play a vital role in diseased conditions such as cancer, where TNT formation occurs at a higher pace and is used for rapid intercellular modulation of chemo-resistance. Viruses such as HIV, HSV and prions also hijack the existing TNT connections between host cells for rapid transmission and evasion of the host immune responses. The following review aims to describe the heterogeneity of TNTs, their role in different tissues and disease conditions in order to enhance our understanding on how these nanotubes can be used as a target for therapies.

In Vitro Modeling of Tumor-Immune System Interaction.

Immunotherapy has emerged during the past two decades as an innovative and successful form of cancer treatment. However, frequently, mechanisms of actions are still unclear, predictive markers are insufficiently characterized, and preclinical assays for innovative treatments are poorly reliable. In this context, the analysis of tumor/immune system interaction plays key roles, but may be unreliably mirrored by in vivo experimental models and standard bidimensional culture systems. Tridimensional cultures of tumor cells have been developed to bridge the gap between in vitro and in vivo systems. Interestingly, defined aspects of the interaction of cells from adaptive and innate immune systems and tumor cells may also be mirrored by 3D cultures. Here we review in vitro models of cancer/immune cell interaction and we propose that updated technologies might help develop innovative treatments, identify biologicals of potential clinical relevance, and select patients eligible for immunotherapy treatments.