The Wdr5-H3K4me3 Epigenetic Axis Regulates Pancreatic Tumor Immunogenicity and Immune Suppression.

The WDR5/MLL1-H3K4me3 epigenetic axis is often activated in both tumor cells and tumor-infiltrating immune cells to drive various cellular responses in the tumor microenvironment and has been extensively studied in hematopoietic cancer, but its respective functions in tumor cells and immune cells in the context of tumor growth regulation of solid tumor is still incompletely understood. We report here that WDR5 exhibits a higher expression level in human pancreatic tumor tissues compared with adjacent normal pancreas. Moreover, WDR5 expression is negatively correlated with patients' response to chemotherapy or immunotherapy in human colon cancer and melanoma. However, WDR5 expression is positively correlated with the HLA level in human cancer cells, and H3K4me3 enrichment is observed at the promoter region of the HLA-A, HLA-B, and HLA-C genes in pancreatic cancer cells. Using mouse tumor cell lines and in vivo tumor models, we determined that WDR5 deficiency or inhibition significantly represses MHC I expression in vitro and in vivo in pancreatic tumor cells. Mechanistically, we determine that WDR5 deficiency inhibits H3K4me3 deposition at the MHC I (H2K) promoter region to repress MHC I (H2K) transcription. On the other hand, WDR5 depletion leads to the effective downregulation of immune checkpoints and immunosuppressive cytokines, including TGFß and IL6, in the pancreatic tumor microenvironments. Our data determine that WDR5 not only regulates tumor cell immunogenicity to suppress tumor growth but also activates immune suppressive pathways to promote tumor immune evasion. Selective activation of the WDR5-MHC I pathway and/or selective inhibition of the WDR5-immune checkpoint and WDR5-cytokine pathways should be considered in WDR5-based epigenetic cancer immunotherapy.

Type I Interferon Activates PD-1 Expression through Activation of the STAT1-IRF2 Pathway in Myeloid Cells.

PD-1 (Programmed cell death protein 1) regulates the metabolic reprogramming of myeloid-derived suppressor cells and myeloid cell differentiation, as well as the type I interferon (IFN-I) signaling pathway in myeloid cells in the tumor microenvironment. PD-1, therefore, is a key inhibitory receptor in myeloid cells. However, the regulation of PD-1 expression in myeloid cells is unknown. We report that the expression level of PDCD1, the gene that encodes the PD-1 protein, is positively correlated with the levels of IFNB1 and IFNAR1 in myeloid cells in human colorectal cancer. Treatment of mouse myeloid cell lines with recombinant IFNß protein elevated PD-1 expression in myeloid cells in vitro. Knocking out IFNAR1, the gene that encodes the IFN-I-specific receptor, diminished the inductive effect of IFNß on PD-1 expression in myeloid cells in vitro. Treatment of tumor-bearing mice with a lipid nanoparticle-encapsulated IFNß-encoding plasmid (IFNBCOL01) increased IFNß expression, resulting in elevated PD-1 expression in tumor-infiltrating myeloid cells. At the molecular level, we determined that IFNß activates STAT1 (signal transducer and activator of transcription 1) and IRFs (interferon regulatory factors) in myeloid cells. Analysis of the cd279 promoter identified IRF2-binding consensus sequence elements. ChIP (chromatin immunoprecipitation) analysis determined that the pSTAT1 directly binds to the irf2 promoter and that IRF2 directly binds to the cd279 promoter in myeloid cells in vitro and in vivo. In colon cancer patients, the expression levels of STAT1, IRF2 and PDCD1 are positively correlated in tumor-infiltrating myeloid cells. Our findings determine that IFNß activates PD-1 expression at least in part by an autocrine mechanism via the stimulation of the pSTAT1-IRF2 axis in myeloid cells.

Delivery of Interferon ß-Encoding Plasmid via Lipid Nanoparticle Restores Interferon ß Expression to Enhance Antitumor Immunity in Colon Cancer.

Type I interferon (IFN-I) plays a critical role in host cancer immunosurveillance, but its expression is often impaired in the tumor microenvironment. We aimed at testing the hypothesis that cationic lipid nanoparticle delivery of interferon ß (IFNß)-encoding plasmid to tumors is effective in restoring IFNß expression to suppress tumor immune evasion. We determined that IFN-I function in tumor suppression depends on the host immune cells. IFN-I activates the expression of Cxcl9 and Cxcl10 to enhance T cell tumor infiltration. RNA-Seq detected a low level of IFNα13 and IFNß in colon tumor tissue. scRNA-Seq revealed that IFNß is expressed in immune cell subsets in non-neoplastic human tissues and to a lesser degree in human colon tumor tissues. Forced expression of IFNα13 and IFNß in colon tumor cells up-regulates major histocompatibility complex I (MHC I) expression and suppresses colon tumor growth in vivo. In human cancer patients, IFNß expression is positively correlated with human leukocyte antigen (HLA) expression, and IFN-I signaling activation correlates with the patient response to PD-1 blockade immunotherapy. To translate this finding to colon cancer immunotherapy, we formulated a 1,2-dioleoyl-3-trimethylammonium propane (DOTAP)-cholesterol-encapsulated IFNß-encoding plasmid (IFNBCOL01). IFNBCOL01 transfects colon tumor cells to express IFNß to increase the level of MHC I expression. IFNBCOL01 therapy transfects tumor cells and tumor-infiltrating immune cells to produce IFNß to activate MHC I and granzyme B expression and inhibits colon tumor growth in mice. Our data determine that lipid nanoparticle delivery of IFNß-encoding plasmid DNA enhances tumor immunogenicity and T cell effector function to suppress colon tumor growth in vivo.

Improving Image Super-Resolution Based on Multiscale Generative Adversarial Networks.

Convolutional neural networks have greatly improved the performance of image super-resolution. However, perceptual networks have problems such as blurred line structures and a lack of high-frequency information when reconstructing image textures. To mitigate these issues, a generative adversarial network based on multiscale asynchronous learning is proposed in this paper, whereby a pyramid structure is employed in the network model to integrate high-frequency information at different scales. Our scheme employs a U-net as a discriminator to focus on the consistency of adjacent pixels in the input image and uses the LPIPS loss for perceptual extreme super-resolution with stronger supervision. Experiments on benchmark datasets and independent datasets Set5, Set14, BSD100, and SunHays80 show that our approach is effective in restoring detailed texture information from low-resolution images.