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The family of human NKG2D ligands (NKG2DL) consists of eight stress-induced molecules. Over 80% of human cancers express these ligands on the surface of tumour cells and/or associated stromal elements. In mice, NKG2D deficiency increases susceptibility to some types of cancer, implicating this system in immune surveillance for malignancy. However, NKG2DL can also be shed, released via exosomes and trapped intracellularly, leading to immunosuppressive effects. Moreover, NKG2D can enhance chronic inflammatory processes which themselves can increase cancer risk and progression. Indeed, tumours commonly deploy a range of countermeasures that can neutralise or even corrupt this surveillance system, tipping the balance away from immune control towards tumour progression. Consequently, the prognostic impact of NKG2DL expression in human cancer is variable. In this review, we consider the underlying biology and regulation of the NKG2D/NKG2DL system and its expression and role in a range of cancer types. We also consider the opportunities for pharmacological modulation of NKG2DL expression while cautioning that such interventions need to be carefully calibrated according to the biology of the specific cancer type.

Ligands of the natural killer group 2D (NKG2DL) family are expressed on malignant cells and are usually absent from healthy tissues. Recognition of NKG2DLs such as MICA/B and ULBP1-3 by the activating immunoreceptor NKG2D, expressed by NK and cytotoxic T cells, stimulates anti-tumor immunity in breast cancer. Upregulation of membrane-bound NKG2DLs in breast cancer has been demonstrated by immunohistochemistry. Tumor cells release NKG2DLs via proteolytic cleavage as soluble (s)NKG2DLs, which allows for effective immune escape and is associated with poor prognosis. In this study, we collected serum from 140 breast cancer (BC) and 20 ductal carcinoma in situ (DCIS) patients at the time of initial diagnosis and 20 healthy volunteers (HVs). Serum levels of sNKG2DLs were quantified through the use of ELISA and correlated with clinical data. The analyzed sNKG2DLs were low to absent in HVs and significantly higher in BC patients. For some of the ligands analyzed, higher sNKG2DLs serum levels were associated with the classification of malignant tumor (TNM) stage and grading. Low sMICA serum levels were associated with significantly longer progression-free (PFS) and overall survival (OS). In conclusion, we provide the first insights into sNKG2DLs in BC patients and suggest their potential role in tumor immune escape in breast cancer. Furthermore, our observations suggest that serum sMICA levels may serve as a prognostic parameter in the patients analyzed in this study.

NKG2D is an activating receptor expressed by NK and T cells primarily involved in the elimination of transformed and infected cells. NKG2D ligands are self-proteins restrictedly expressed in healthy tissues, but induced in response to signaling pathways commonly associated with transformation. Proliferative, tumor suppressor, and stress signaling pathways linked to the tumorigenic process induce the expression of NKG2D ligands, initiating an immune response against the incipient tumor. Nevertheless, the activity of NKG2D ligands is counter-regulated in vivo by the immunoediting of cancer cells, resulting in the expression of multiple mechanisms of immune evasion in advanced tumors. The redundancy of NKG2D ligands, besides increasing the complexity of their regulation, may impair the generation of these immune evasion mechanisms. In this review, we attempt to integrate the mechanisms and pathways involved in the regulation of NKG2D ligand expression in cancer.

Immune evasion from NK surveillance related to inadequate NK-cell function has been suggested as an explanation of the high incidence of relapse and fatal outcome of many blood malignancies. In this report we have used Jurkat and Raji cell lines as a model for studies of the NKG2D receptor-ligand system in T-and B cell leukemia/lymphoma. Using real-time quantitative RT-PCR and immunoflow cytometry we show that Jurkat and Raji cells constitutively express mRNA and protein for the stress-inducible NKG2D ligands MICA/B and ULBP1 and 2, and up-regulate the expression in a cell-line specific and stress-specific manner. Furthermore, we revealed by electron microscopy, immunoflow cytometry and western blot that these ligands were expressed and secreted on exosomes, nanometer-sized microvesicles of endosomal origin. Acting as a decoy, the NKG2D ligand-bearing exosomes downregulate the in vitro NKG2D receptor-mediated cytotoxicity and thus impair NK-cell function. Interestingly, thermal and oxidative stress enhanced the exosome secretion generating more soluble NKG2D ligands that aggravated the impairment of the cytotoxic response. Taken together, our results might partly explain the clinically observed NK-cell dysfunction in patients suffering from leukemia/lymphoma. The adverse effect of thermal and oxidative stress, enhancing the release of immunosuppressive exosomes, should be considered when cytostatic and hyperthermal anti-cancer therapies are designed.

Epstein-Barr virus (EBV) is a ubiquitous human pathogen, infecting up to 95% of the world’s adult population. Initial infection with EBV can cause infectious mononucleosis. The innate immune system serves as frontline defense against pathogens, such as bacteria and viruses. Natural killer (NK) cells are a part of innate immunity and can both secrete cytokines and directly target cells for lysis. NK cells express several cell surface receptors, including NKG2D, which bind multiple ligands. People with deficiencies in NK cells are often susceptible to uncontrolled infection by herpesviruses, such as Epstein-Barr virus (EBV). Infection with EBV stimulates both innate and adaptive immunity, yet the virus establishes lifelong latent infection in memory B cells. We show that the EBV oncogene EBNA1, previously known to be necessary for maintaining EBV genomes in latently infected cells, also plays an important role in suppressing NK cell responses and cell death in newly infected cells. EBNA1 does so by downregulating the NKG2D ligands ULBP1 and ULBP5 and modulating expression of c-Myc. B cells infected with a derivative of EBV that lacks EBNA1 are more susceptible to NK cell-mediated killing and show increased levels of apoptosis. Thus, EBNA1 performs a previously unappreciated role in reducing immune response and programmed cell death after EBV infection, helping infected cells avoid immune surveillance and apoptosis and thus persist for the lifetime of the host. IMPORTANCE Epstein-Barr virus (EBV) is a ubiquitous human pathogen, infecting up to 95% of the world’s adult population. Initial infection with EBV can cause infectious mononucleosis. EBV is also linked to several human malignancies, including lymphomas and carcinomas. Although infection by EBV alerts the immune system and causes an immune response, the virus persists for life in memory B cells. We show that the EBV protein EBNA1 can downregulate several components of the innate immune system linked to natural killer (NK) cells. This downregulation of NK cell activity translates to lower killing of EBV-infected cells and is likely one way that EBV escapes immune surveillance after infection. Additionally, we show that EBNA1 reduces apoptosis in newly infected B cells, allowing more of these cells to survive. Taken together, our findings uncover new functions of EBNA1 and provide insights into viral strategies to survive the initial immune response postinfection.

The activating receptor NKG2D (natural-killer group 2, member D) and its ligands play an important role in the NK, γδ + and CD8 + T-cell-mediated immune response to tumors. Ligands for NKG2D are rarely detectable on the surface of healthy cells and tissues, but are frequently expressed by tumor cell lines and in tumor tissues. It is evident that the expression levels of these ligands on target cells have to be tightly regulated to allow immune cell activation against tumors, but at the same time avoid destruction of healthy tissues. Importantly, it was recently discovered that another safeguard mechanism controlling activation via the receptor NKG2D exists. It was shown that NKG2D signaling is coupled to the IL-15 receptor pathway in a cell-specific manner suggesting that priming of NKG2D-mediated activation depends on the cellular microenvironment and the distinct cellular context. This review will provide a broad overview of our up-to-date knowledge of the NKG2D receptor and its ligands in the context of tumor immunology. Strategies to amplify NKG2D-mediated antitumor responses and counteract tumor immune escape mechanisms will be discussed.

Seam carving is widely used excellent content-aware image scaling method. When an image is processed by seam carving, its local texture changes. Local binary patterns is an excellent local descriptor for describing the local texture of an image. In this paper, a blind detection based uniform local binary patterns(ULBP) is proposed to detect seam-carved image. Firstly, the image is transformed into gray-scale image. Then the ULBP histogram features and seam features are extracted from the gray-scale image. Finally support vector machine (SVM) is adopted as classifier to train and test those features to identify whether an image is subjected to seam carving or not. Wei et al. (Pattern Recogn Lett 36:100–106 2014 ) method and Ryu et al. (IEICE Trans Inf Syst 97(5):1304–1311 2014 ) method are selected as the benchmark. Extensive compared experiments are conducted by the three methods, respectively. Experimental results show that the proposed method has the best performance among the three methods under a variety of setting.

Natural killer cells possess key regulatory function in various malignant diseases, including acute myeloid leukemia. NK cell activity is driven by signals received through ligands binding activating or inhibitory receptors. Their activity towards elimination of transformed or virally infected cells can be mediated through MICA, MICB and ULBP ligands binding the activating receptor NKG2D. Given the efficiency of NK cells, potential target cells developed multiple protecting mechanisms to overcome NK cells killing on various levels of biogenesis of NKG2D ligands. Targeted cells can degrade ligand transcripts via microRNAs or modify them at protein level to prevent their presence at cell surface via shedding, with added benefit of shed ligands to desensitize NKG2D receptor and avert the threat of destruction via NK cells. NK cells and their activity are also indispensable during hematopoietic stem cell transplantation, crucial treatment option for patients with malignant disease, including acute myeloid leukemia. Function of both NKG2D and its ligands is strongly affected by polymorphisms and particular allelic variants, as different alleles can play variable roles in ligand-receptor interaction, influencing NK cell function and HSCT outcome differently. For example, role of amino acid exchange at position 129 in MICA or at position 98 in MICB , as well as the role of other polymorphisms leading to different shedding of ligands, was described. Finally, match or mismatch between patient and donor in NKG2D ligands affect HSCT outcome. Having the information beyond standard HLA typing prior HSCT could be instrumental to find the best donor for the patient and to optimize effects of treatment by more precise patient-donor match. Here, we review recent research on the NKG2D/NKG2D ligand biology, their regulation, description of their polymorphisms across the populations of patients with AML and the influence of particular polymorphisms on HSCT outcome.

For a pattern classification problem, dataset size plays a vital role in the training and testing of the used classifier. With a smaller dataset, the model suffers from a common problem of data underfitting in which the model remains unable to cover the underlying trend of scenarios, as well as its learning rules remain simple and flexible. As a result, the classification model performs wrong predictions and its performance remains low. In this direction, the proposed work augments a mammographic image analysis society (MIAS) dataset of mammogram images that are used for the classification of breast cancer. In this process, the suspected cancerous region of interest (ROI) has been extracted from all 323 (64 benign, 52 malignant, and 207 normal) mammogram images to form a non-augmented dataset. The images of this non-augmented dataset are augmented using simple image transformation (rotations and flip) techniques to form its augmented dataset. It consists of 23,256 (4608 benign, 3744 malignant, and 14,904 normal) ROI images. The images of both datasets are normalized to 128 X 128 pixels. A total of five feature vectors from these images are generated using individual features such as histogram, uniform local binary pattern (ULBP) and histogram-oriented gradient (HOG) as well as some of their combinations like (ULBP + HOG) and (histogram + ULBP + HOG). Using these feature vectors, the performance of 24 popular classification models have been analyzed in terms of accuracy, classification time per image, sensitivity, specificity, precision and f1-score for both datasets using k-fold (k = 5) cross-validation. Along with a significant improvement in all parameters, an improvement of average accuracy of 8.26% has been obtained for the augmented dataset over the non-augmented one. Specifically, this average accuracy improvement for the above five feature vectors is 22.16%, 2.87%, 5.25%, 2.8%, and 8.22% in that order of their listing which pleads for the finding that histogram features are more suitable for mammogram image classification. It shows that the augmented dataset not only improves the performance of various classifiers but it can be useful for further research based on mammogram images.

Coronavirus disease 2019 (COVID19), caused by SARS-CoV-2, is a complex disease, with a variety of clinical manifestations ranging from asymptomatic infection or mild cold-like symptoms to more severe cases requiring hospitalization and critical care. The most severe presentations seem to be related with a delayed, deregulated immune response leading to exacerbated inflammation and organ damage with close similarities to sepsis. In order to improve the understanding on the relation between host immune response and disease course, we have studied the differences in the cellular (monocytes, CD8+ T and NK cells) and soluble (cytokines, chemokines and immunoregulatory ligands) immune response in blood between Healthy Donors (HD), COVID19 and a group of patients with non-COVID19 respiratory tract infections (NON-COV-RTI). In addition, the immune response profile has been analyzed in COVID19 patients according to disease severity. In comparison to HDs and patients with NON-COV-RTI, COVID19 patients show a heterogeneous immune response with the presence of both activated and exhausted CD8+ T and NK cells characterised by the expression of the immune checkpoint LAG3 and the presence of the adaptive NK cell subset. An increased frequency of adaptive NK cells and a reduction of NK cells expressing the activating receptors NKp30 and NKp46 correlated with disease severity. Although both activated and exhausted NK cells expressing LAG3 were increased in moderate/severe cases, unsupervised cell clustering analyses revealed a more complex scenario with single NK cells expressing more than one immune checkpoint (PD1, TIM3 and/or LAG3). A general increased level of inflammatory cytokines and chemokines was found in COVID19 patients, some of which like IL18, IL1RA, IL36B and IL31, IL2, IFNα and TNFα, CXCL10, CCL2 and CCL8 were able to differentiate between COVID19 and NON-COV-RTI and correlated with bad prognosis (IL2, TNFα, IL1RA, CCL2, CXCL10 and CXCL9). Notably, we found that soluble NKG2D ligands from the MIC and ULBPs families were increased in COVID19 compared to NON-COV-RTI and correlated with disease severity. Our results provide a detailed comprehensive analysis of the presence of activated and exhausted CD8+T, NK and monocyte cell subsets as well as extracellular inflammatory factors beyond cytokines/chemokines, specifically associated to COVID19. Importantly, multivariate analysis including clinical, demographical and immunological experimental variables have allowed us to reveal specific immune signatures to i) differentiate COVID19 from other infections and ii) predict disease severity and the risk of death.