Transcriptomic signatures designed to predict melanoma patient responses to PD-1 blockade have been reported but rarely validated. We now show that intra-patient heterogeneity of tumor responses to PD-1 inhibition limit the predictive performance of these signatures. We reasoned that resistance mechanisms will reflect the tumor microenvironment, and thus we examined PD-1 inhibitor resistance relative to T-cell activity in 94 melanoma tumors collected at baseline and at time of PD-1 inhibitor progression. Tumors were analyzed using RNA sequencing and flow cytometry, and validated functionally. These analyses confirm that major histocompatibility complex (MHC) class I downregulation is a hallmark of resistance to PD-1 inhibitors and is associated with the MITF low /AXL high de-differentiated phenotype and cancer-associated fibroblast signatures. We demonstrate that TGFß drives the treatment resistant phenotype (MITF low /AXL high) and contributes to MHC class I downregulation in melanoma. Combinations of anti-PD-1 with drugs that target the TGFß signaling pathway and/or which reverse melanoma de-differentiation may be effective future therapeutic strategies.

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There is growing evidence of a malignancy-protective role for vitamin D in breast cancer. The effects of vitamin D are mediated via the vitamin D receptor (VDR) which is encoded by VDR gene. Several SNPs on VDR gene has attracted research interest, although the magnitude of the impact of VDR allelic variations on breast cancer has been controversial. In the present study, we focused on the distribution of VDR FokI and BsmI polymorphisms in Iranian breast cancer patients. A case-control study was conducted on 296 samples including 140 breast cancer patients and 156 age matched control women. Restriction fragment length polymorphism (RFLP) analysis was performed for BsmI and FokI genotyping. Randomly selected PCR products were subjected to sequencing to verify the RFLP results. A significantly increased risk of breast cancer was observed with BsmI bb or even Bb genotype (OR 2.39, CI 1.17-4.85 and OR 2.28, CI 1.16-4.47, respectively). Nevertheless, statistically significant association between FokI genotypes and breast cancer risk was not observed. This study lends support for an increased risk of breast cancer associated with the VDR BsmI polymorphism.

The introduction of checkpoint inhibitors revolutionized immuno-oncology. The efficacy of traditional immunotherapeutics, like vaccines and immunostimulants was very limited due to persistent immune-escape strategies of cancer cells. Checkpoint inhibitors target these escape mechanisms and re-direct the immune system to anti-tumor toxicity. Phenomenal results have been reported in entities like melanoma, where no other therapy was able to demonstrate survival benefit, before the introduction of immunotherapeutics. The first experience in ovarian cancer (OC) was reported for nivolumab, a fully human anti-programmed cell death protein 1 (PD1) antibody, in 2015. While the data are extraordinary for a mono-immunotherapeutic agent and very promising, they do not match up to the revolutionary results in entities like melanoma. The key to exceptional treatment response in OC, could be the identification of the most immunogenic patients. We hypothyse that BRCA mutation could be a predictor of improved response in OC. The underlying DNA-repair-deficiancy should result in increased immunogenicity because of higher mutational load and more neoantigen presentation. This hypothesis was not tested to date and should be subject to future trials. The present article gives an overview of the immunologic background of checkpoint inhibition (CI). It presents current data on nivolumab and other checkpoint-inhibitors in solid tumors and OC specifically and depicts important topics in the management of this novel substance group, such as side effect control, diagnostic PD-1/programmed cell death-ligand 1 (PD-L1) expression assessment and management of pseudoprogression.

This study underlines the high need of patients with ovarian cancer for all details concerning treatment options irrespective of their cultural background, the stage of disease and the patient's age. Increased information requirements regarding potential side effects and treatment alternatives were recorded. Besides the need for more effective therapy, alopecia and fatigue are the most important side effects of concern to patients.

Immune checkpoint inhibitors that block the programmed cell death protein 1/PD-L1 pathway have significantly improved the survival of patients with advanced melanoma. Immunotherapies are only effective in 15–40% of melanoma patients and resistance is associated with defects in antigen presentation and interferon signaling pathways. In this study, we examined interferon-γ (IFNγ) responses in a large panel of immune checkpoint inhibitor-naïve melanoma cells with defined genetic drivers; BRAF-mutant (n = 11), NRAS-mutant (n = 10), BRAF/NRAS wild type (n = 10), and GNAQ/GNA11-mutant uveal melanomas (UVMs) (n = 8). Cell surface expression of established IFNγ downstream targets PD-L1, PD-L2, HLA-A, -B, and -C, HLA-DR, and nerve growth factor receptor (NGFR) were analyzed by flow cytometry. Basal cellular expression levels of HLA-A, -B, -C, HLA-DR, NGFR, and PD-L2 predicted the levels of IFNγ-stimulation, whereas PD-L1 induction was independent of basal expression levels. Only 13/39 (33%) of the melanoma cell lines tested responded to IFNγ with potent induction of all targets, indicating that downregulation of IFNγ signaling is common in melanoma. In addition, we identified two well-recognized mechanisms of immunotherapy resistance, the loss of β-2-microglobulin and interferon gamma receptor 1 expression. We also examined the influence of melanoma driver oncogenes on IFNγ signaling and our data suggest that UVM have diminished capacity to respond to IFNγ, with lower induced expression of several targets, consistent with the disappointing response of UVM to immunotherapies. Our results demonstrate that melanoma responses to IFNγ are heterogeneous, frequently downregulated in immune checkpoint inhibitor-naïve melanoma and potentially predictive of response to immunotherapy.

Background and Objective. The new coronavirus disease (known as COVID-19) was first identified in Wuhan and quickly spread worldwide, wreaking havoc on the economy and people’s everyday lives. As the number of COVID-19 cases is rapidly increasing, a reliable detection technique is needed to identify affected individuals and care for them in the early stages of COVID-19 and reduce the virus’s transmission. The most accessible method for COVID-19 identification is Reverse Transcriptase-Polymerase Chain Reaction (RT-PCR); however, it is time-consuming and has false-negative results. These limitations encouraged us to propose a novel framework based on deep learning that can aid radiologists in diagnosing COVID-19 cases from chest X-ray images. Methods. In this paper, a pretrained network, DenseNet169, was employed to extract features from X-ray images. Features were chosen by a feature selection method, i.e., analysis of variance (ANOVA), to reduce computations and time complexity while overcoming the curse of dimensionality to improve accuracy. Finally, selected features were classified by the eXtreme Gradient Boosting (XGBoost). The ChestX-ray8 dataset was employed to train and evaluate the proposed method. Results and Conclusion. The proposed method reached 98.72% accuracy for two-class classification (COVID-19, No-findings) and 92% accuracy for multiclass classification (COVID-19, No-findings, and Pneumonia). The proposed method’s precision, recall, and specificity rates on two-class classification were 99.21%, 93.33%, and 100%, respectively. Also, the proposed method achieved 94.07% precision, 88.46% recall, and 100% specificity for multiclass classification. The experimental results show that the proposed framework outperforms other methods and can be helpful for radiologists in the diagnosis of COVID-19 cases.

Background: Due to burgeoning population, solid waste site selection is one of the most crucial issues in waste management system. Regarding the complexity of waste management systems, the convenient solid waste site selection involves considering multiple alternative solutions and assessing different criteria. Objectives: The current study, aimed to select the fuzzy logic method as one of the most popular approaches in multi criteria decision analysis (MCDA) to find a new sanitary landfill site through considering various criteria in Bardaskan city, Iran. Materials and Methods: Nine types of criteria divided into two main groups of ecological and economical criteria were involved to select the most convenient landfill site. A model based on the multi criteria evaluation techniques and combination of geographical information systems (GIS) with fuzzy logic was employed. The ESRI Arc GIS 10 term system was utilized to manipulate and present spatial data. The fuzzy AND operator was used to overlay all standardized maps. Results:The results of the current research proposed five locations with the fuzzy membership value of more than 0.9 as the best landfill sites in the study area. The sites were located in the North and North East of Bardaskan city. The study demonstrated that 51.97 % of the study area was not suitable for landfill location whilst only 7.8 % was highly suitable for landfill siting. About 1956.61 hectares of the studied area was suitable for territorial landfill siting. Conclusions:The study results guided the municipality authorities to select the best landfill site among the candidate ones, and due to the broad spectrum of classifications, the output results can enable decision makers to make appropriate decisions to reduce the costs both in economical and bioenvironmental criteria.