EXPRESSION AND IMMUNOLOGICAL CHARACTERIZATION OF HERV-K TRANSMEMBRANE ENVELOPE PROTEIN IN HUMAN BREAST CANCER

The human endogenous retrovirus K (HERV-K) env gene encodes envelope protein comprising surface (SU) and transmembrane (TM) domains. Having shown the exclusive expression of SU in human breast cancer and the stimulation of SU-specific immune responses in patients with breast cancer, our research here confirmed and extended the data by investigating the expression of HERV-K TM envelope domain and the induction of specific immune responses against TM in breast cancer patients. We found HERV-K TM mRNA and protein expression only in human breast cancer cells but not in normal controls. The specific immune responses against TM domain were induced in mice determined by enzyme-linked immunosorbent assay (ELISA) and IFN-γ enzyme-linked immunosorbent spot (ELISPOT) assay. Furthermore, ELISA detected higher titers of anti-HERV-K TM Env IgG antibodies in sera of breast cancer patients. In addition, the magnitude of the anti-HERV TM B cell response was correlated with the disease stage. Peripheral blood mononuclear cells (PBMCs) before and after in vitro stimulation (IVS) with HERV-K TM from patients with breast cancer as well as healthy controls were tested for T cell responses against HERV-K TM domain by ELISPOT assay. Breast cancer patients (n=21) had stronger HERV-K TM-specific cellular responses than healthy controls (n=12) (P \u3c 0.05). These findings suggest, for the first time, that HERV-K TM expression was enhanced in human breast cancer cells and was able to induce specific B cell and T cell immune responses in breast cancer patients. This study provides support for HERV-K TM as a promising source of antigen for anti-tumor immunotherapy, prevention, diagnosis, and prognosis

Deep-Learning-Enabled Fast Optical Identification and Characterization of Two-Dimensional Materials

Advanced microscopy and/or spectroscopy tools play indispensable role in nanoscience and nanotechnology research, as it provides rich information about the growth mechanism, chemical compositions, crystallography, and other important physical and chemical properties. However, the interpretation of imaging data heavily relies on the "intuition" of experienced researchers. As a result, many of the deep graphical features obtained through these tools are often unused because of difficulties in processing the data and finding the correlations. Such challenges can be well addressed by deep learning. In this work, we use the optical characterization of two-dimensional (2D) materials as a case study, and demonstrate a neural-network-based algorithm for the material and thickness identification of exfoliated 2D materials with high prediction accuracy and real-time processing capability. Further analysis shows that the trained network can extract deep graphical features such as contrast, color, edges, shapes, segment sizes and their distributions, based on which we develop an ensemble approach topredict the most relevant physical properties of 2D materials. Finally, a transfer learning technique is applied to adapt the pretrained network to other applications such as identifying layer numbers of a new 2D material, or materials produced by a different synthetic approach. Our artificial-intelligence-based material characterization approach is a powerful tool that would speed up the preparation, initial characterization of 2D materials and other nanomaterials and potentially accelerate new material discoveries

Good learning environment of medical schools is an independent predictor for medical students’ study engagement

BackgroundStudy engagement is regarded important to medical students’ physical and mental wellbeing. However, the relationship between learning environment of medical schools and the study engagement of medical students was still unclear. This study was aimed to ascertain the positive effect of learning environment in study engagement.MethodsWe collected 10,901 valid questionnaires from 12 medical universities in China, and UWES-S was utilized to assess the study engagement levels. Then Pearson Chi-Square test and Welch’s ANOVA test were conducted to find the relationship between study engagement and learning environment, and subgroup analysis was used to eradicate possible influence of confounding factors. After that, a multivariate analysis was performed to prove learning environment was an independent factor, and we constructed a nomogram as a predictive model.ResultsWith Pearson Chi-Square test (p < 0.001) and Welch’s ANOVA test (p < 0.001), it proved that a good learning environment contributed to a higher mean of UWES scores. Subgroup analysis also showed statistical significance (p < 0.001). In the multivariate analysis, we could find that, taking “Good” as reference, “Excellent” (OR = 0.329, 95%CI = 0.295–0.366, p < 0.001) learning environment was conducive to one’s study engagement, while “Common” (OR = 2.206, 95%CI = 1.989–2.446, p < 0.001), “Bad” (OR = 2.349, 95%CI = 1.597–3.454, p < 0.001), and “Terrible” (OR = 1.696, 95%CI = 1.015–2.834, p = 0.044) learning environment only resulted into relatively bad study engagement. Depending on the result, a nomogram was drawn, which had predictive discrimination and accuracy (AUC = 0.680).ConclusionWe concluded that learning environment of school was an independent factor of medical student’s study engagement. A higher level of learning environment of medical school came with a higher level of medical students’ study engagement. The nomogram could serve as a predictive reference for the educators and researchers

Identification of prostate-specific G-protein coupled receptor as a tumor antigen recognized by CD8(+) T cells for cancer immunotherapy.

Prostate cancer is the most common cancer among elderly men in the US, and immunotherapy has been shown to be a promising strategy to treat patients with metastatic castration-resistant prostate cancer. Efforts to identify novel prostate specific tumor antigens will facilitate the development of effective cancer vaccines against prostate cancer. Prostate-specific G-protein coupled receptor (PSGR) is a novel antigen that has been shown to be specifically over-expressed in human prostate cancer tissues. In this study, we describe the identification of PSGR-derived peptide epitopes recognized by CD8(+) T cells in an HLA-A2 dependent manner.Twenty-one PSGR-derived peptides were predicted by an immuno-informatics approach based on the HLA-A2 binding motif. These peptides were examined for their ability to induce peptide-specific T cell responses in peripheral blood mononuclear cells (PBMCs) obtained from either HLA-A2(+) healthy donors or HLA-A2(+) prostate cancer patients. The recognition of HLA-A2 positive and PSGR expressing LNCaP cells was also tested. Among the 21 PSGR-derived peptides, three peptides, PSGR3, PSGR4 and PSGR14 frequently induced peptide-specific T cell responses in PBMCs from both healthy donors and prostate cancer patients. Importantly, these peptide-specific T cells recognized and killed LNCaP prostate cancer cells in an HLA class I-restricted manner.We have identified three novel HLA-A2-restricted PSGR-derived peptides recognized by CD8(+) T cells, which, in turn, recognize HLA-A2(+) and PSGR(+) tumor cells. The PSGR-derived peptides identified may be used as diagnostic markers as well as immune targets for development of anticancer vaccines

Development of a TCR-like antibody and chimeric antigen receptor against NY-ESO-1/HLA-A2 for cancer immunotherapy

Background The current therapeutic antibodies and chimeric antigen receptor (CAR) T cells are capable of recognizing surface antigens, but not of intracellular proteins, thus limiting the target coverage for drug development. To mimic the feature of T-cell receptor (TCR) that recognizes the complex of major histocompatibility class I and peptide on the cell surface derived from the processed intracellular antigen, we used NY-ESO-1, a cancer-testis antigen, to develop a TCR-like fully human IgG1 antibody and its derivative, CAR-T cells, for cancer immunotherapy.Methods Human single-chain variable antibody fragment (scFv) phage library (~10∧11) was screened against HLA-A2/NY-ESO-1 (peptide 157–165) complex to obtain target-specific antibodies. The specificity and affinity of those antibodies were characterized by flow cytometry, ELISA, biolayer interferometry, and confocal imaging. The biological functions of CAR-T cells were evaluated against target tumor cells in vitro. In vivo antitumor activity was investigated in a triple-negative breast cancer (TNBC) model and primary melanoma tumor model in immunocompromised mice.Results Monoclonal antibody 2D2 identified from phage-displayed library specifically bound to NY-ESO-1157-165 in the context of human leukocyte antigen HLA-A*02:01 but not to non-A2 or NY-ESO-1 negative cells. The second-generation CAR-T cells engineered from 2D2 specifically recognized and eliminated A2+/NY-ESO-1+tumor cells in vitro, inhibited tumor growth, and prolonged the overall survival of mice in TNBC and primary melanoma tumor model in vivo.Conclusions This study showed the specificity of the antibody identified from human scFv phage library and demonstrated the potential antitumor activity by TCR-like CAR-T cells both in vitro and in vivo, warranting further preclinical and clinical evaluation of the TCR-like antibody in patients. The generation of TCR-like antibody and its CAR-T cells provides the state-of-the-art platform and proof-of-concept validation to broaden the scope of target antigen recognition and sheds light on the development of novel therapeutics for cancer immunotherapy