Harnessing the Anti-Tumor Mediators in Mast Cells as a New Strategy for Adoptive Cell Transfer for Cancer

The emergence of cancer immunotherapies utilizing adoptive cell transfer (ACT) continues to be one of the most promising strategies for cancer treatment. Mast cells (MCs) which occur throughout vascularized tissues, are most commonly associated with Type I hypersensitivity, bind immunoglobin E (IgE) with high affinity, produce anti-cancer mediators such as tumor necrosis factor alpha (TNF-α) and granulocyte macrophage colony-stimulating factor (GM-CSF), and generally populate the tumor microenvironments. Yet, the role of MCs in cancer pathologies remains controversial with evidence for both anti-tumor and pro-tumor effects. Here, we review the studies examining the role of MCs in multiple forms of cancer, provide an alternative, MC-based hypothesis underlying the mechanism of therapeutic tumor IgE efficacy in clinical trials, and propose a novel strategy for using tumor-targeted, IgE-sensitized MCs as a platform for developing new cellular cancer immunotherapies. This autologous MC cancer immunotherapy could have several advantages over current cell-based cancer immunotherapies and provide new mechanistic strategies for cancer therapeutics alone or in combination with current approaches

Human Tumor Targeted Cytotoxic Mast Cells for Cancer Immunotherapy

The diversity of autologous cells being used and investigated for cancer therapy continues to increase. Mast cells (MCs) are tissue cells that contain a unique set of anti-cancer mediators and are found in and around tumors. We sought to exploit the anti-tumor mediators in MC granules to selectively target them to tumor cells using tumor specific immunoglobin E (IgE) and controllably trigger release of anti-tumor mediators upon tumor cell engagement. We used a human HER2/neu-specific IgE to arm human MCs through the high affinity IgE receptor (FcεRI). The ability of MCs to bind to and induce apoptosis of HER2/neu-positive cancer cells in vitro and in vivo was assessed. The interactions between MCs and cancer cells were investigated in real time using confocal microscopy. The mechanism of action using cytotoxic MCs was examined using gene array profiling. Genetically manipulating autologous MC to assess the effects of MC-specific mediators have on apoptosis of tumor cells was developed using siRNA. We found that HER2/neu tumor-specific IgE-sensitized MCs bound, penetrated, and killed HER2/neu-positive tumor masses in vitro. Tunneling nanotubes formed between MCs and tumor cells are described that parallel tumor cell apoptosis. In solid tumor, human breast cancer (BC) xenograft mouse models, infusion of HER2/neu IgE-sensitized human MCs co-localized to BC cells, decreased tumor burden, and prolonged overall survival without indications of toxicity. Gene microarray of tumor cells suggests a dependence on TNF and TGFβ signaling pathways leading to apoptosis. Knocking down MC-released tryptase did not affect apoptosis of cancer cells. These studies suggest MCs can be polarized from Type I hypersensitivity-mediating cells to cytotoxic cells that selectively target tumor cells and specifically triggered to release anti-tumor mediators. A strategy to investigate which MC mediators are responsible for the observed tumor killing is described so that rational decisions can be made in the future when selecting which mediators to target for deletion or those that could further polarize them to cytotoxic MC by adding other known anti-tumor agents. Using autologous human MC may provide further options for cancer therapeutics that offers a unique anti-cancer mechanism of action using tumor targeted IgE’s

Identification and Characterization of Tunneling Nanotubes Involved in Human Mast Cell FcεRI-Mediated Apoptosis of Cancer Cells

Mast cells (MCs) are found in practically all tissues where they participate in innate and adaptive immune responses. They are also found in and around tumors, yet their interactions with cancer cells and the resulting impact on cancer cell growth and metastasis are not well understood. In this study, we examined a novel mechanism of IgE-FcεRI-mediated, intercellular communication between human adipose-derived mast cells (ADMC) and cancer cells. The formation of heterotypic tunneling nanotubes (TnT) and membrane structures between MCs and tumor cells in vitro was examined using microscopy and a diverse array of molecule-specific indicator dyes. We show that several MC-specific structures are dependent on the specific interactions between human tumor IgE-sensitized MCs and antigens on the tumor cell surface. The formation of TnT, membrane blebs and other MC-specific structures paralleled FcεRI-degranulation occurring within 30 min and persisting for up to 24 h. The TnT-specific adhesion of FcεRI-activated MCs to tumor cells was characterized by the transport of the MC granule content into the tumor cells, including tryptase and TNF-α. This interaction led to apoptosis of the tumor cells, which differs from previous studies examining tissue cells within the cancer microenvironment. The formation of heterotypic TnT results in stimulation of an invasive tumor cell phenotype and increased tumor cell invasion and chemoresistance of the cancer cells. These studies describe a heretofore-unrecognized mechanism underlying IgE-mediated interactions and FcεRI-activated MC-mediated killing of tumor cells through the formation of TnT

Preference learning for eco-friendly hotels recommendation: A multi-criteria collaborative filtering approach

The crucial role of customers’ positive experience and their subsequent word-of-mouth have been highlighted by both scholars and practitioners for all industry sectors. In response to an increasing concern of environmental sustainability and sensitivity of consumers for deteriorating environment, eco-friendly (green) products and services gained tremendous attention. TripAdvisor is increasingly known as one of the most popular e-tourism platforms. Understanding and predicting the traveler’ preferences by advanced big data analytics technology is an important task that the recommendation engine of this platform does. In this paper, we aim to develop a new soft computing method with the aid of machine learning techniques in order to find the best matching eco-friendly hotels based on the several quality factors in TripAdvisor. We develop the method using dimensionality reduction and prediction machine learning techniques to improve the scalability of prediction from the large number of users’ ratings. The proposed soft computing method is evaluated on a large dataset discovered from the TripAdvisor platform. The results show that the combination of dimensionality reduction and prediction machine learning techniques is robust in processing the large number of the ratings provided by users on the features of eco-friendly hotels and predicting travelers’ choice preferences of eco-friendly hotels in TripAdvisor. © 2019 Elsevier Lt