Targeted Stat2 deletion in conventional dendritic cells impairs CTL responses but does not affect antibody production

STATs signaling pathways in dendritic cells: As potential therapeutic targets?

Dendritic cells (DCs) are professional antigen-presenting cells (APCs), including heterogenous populations with phenotypic and functional diversity that coordinate bridging innate and adaptive immunity. Signal transducer and activator of transcriptions (STAT) factors as key proteins in cytokine signaling were shown to play distinct roles in the maturation and antigen presentation of DCs and play a pivotal role in modulating immune responses mediated by DCs such as differentiation of T cells to T helper (Th) 1, Th2 or regulatory T (Treg) cells. This review sheds light on the importance of STAT transcription factors' signaling pathways in different subtypes of DCs and highlights their targeting potential usages for improving DC-based immunotherapies for patients who suffer from cancer or diverse autoimmune conditions according to the type of the STAT transcription factor and its specific activating or inhibitory agent.

The duality of STAT2 mediated type I interferon signaling in the tumor microenvironment and chemoresistance

The tumor microenvironment consists of tumor cells, extracellular matrix, blood vessels, and non-tumor cells such as fibroblasts and immune cells. Crosstalk among components of this cellular ecosystem can transform non-malignant cells and promote tumor invasion and metastasis. Evidence is accumulating that the transcription factor STAT2, a downstream effector of type I interferon (IFN-I) signaling, can either inhibit or promote tumorigenesis depending on the unique environment presented by each type of cancer. STAT2 has long been associated with the canonical JAK/STAT pathway involved in various biological processes including reshaping of the tumor microenvironment and in antitumor immunity. This dichotomous tendency of STAT2 to both inhibit and worsen tumor formation makes the protein a curious, and yet relatively ill-defined player in many cancer pathways involving IFN-I. In this review, we discuss the role of STAT2 in contributing to either a tumorigenic or anti-tumorigenic microenvironment as well as chemoresistance.

scATACpipe: A nextflow pipeline for comprehensive and reproducible analyses of single cell ATAC-seq data

Single cell ATAC-seq (scATAC-seq) has become the most widely used method for profiling open chromatin landscape of heterogeneous cell populations at a single-cell resolution. Although numerous software tools and pipelines have been developed, an easy-to-use, scalable, reproducible, and comprehensive pipeline for scATAC-seq data analyses is still lacking. To fill this gap, we developed scATACpipe, a Nextflow pipeline, for performing comprehensive analyses of scATAC-seq data including extensive quality assessment, preprocessing, dimension reduction, clustering, peak calling, differential accessibility inference, integration with scRNA-seq data, transcription factor activity and footprinting analysis, co-accessibility inference, and cell trajectory prediction. scATACpipe enables users to perform the end-to-end analysis of scATAC-seq data with three sub-workflow options for preprocessing that leverage 10x Genomics Cell Ranger ATAC software, the ultra-fast Chromap procedures, and a set of custom scripts implementing current best practices for scATAC-seq data preprocessing. The pipeline extends the R package ArchR for downstream analysis with added support to any eukaryotic species with an annotated reference genome. Importantly, scATACpipe generates an all-in-one HTML report for the entire analysis and outputs cluster-specific BAM, BED, and BigWig files for visualization in a genome browser. scATACpipe eliminates the need for users to chain different tools together and facilitates reproducible and comprehensive analyses of scATAC-seq data from raw reads to various biological insights with minimal changes of configuration settings for different computing environments or species. By applying it to public datasets, we illustrated the utility, flexibility, versatility, and reliability of our pipeline, and demonstrated that our scATACpipe outperforms other workflows.

High Expression of Interferon Pathway Genes CXCL10 and STAT2 Is Associated with Activated T-Cell Signature and Better Outcome of Oral Cancer Patients

To improve the survival rate of cancer patients, biomarkers for both early diagnosis and patient stratification for appropriate therapeutics play crucial roles in precision oncology. Investigation of altered gene expression and the relevant molecular pathways in cancer cells are helpful for discovering such biomarkers. In this study, we explore the potential prognostic biomarkers for oral cancer patients through systematically analyzing five oral cancer transcriptomic data sets (TCGA, GSE23558, GSE30784, GSE37991, and GSE138206). Gene Set Enrichment Analysis (GSEA) was individually applied to each data set and the upregulated Hallmark molecular pathways of each data set were intersected to generate 13 common pathways including interferon-α/γ pathways. Among the 5 oral cancer data sets, 43 interferon pathway genes were commonly upregulated and 17 genes exhibited prognostic values in TCGA cohort. After validating in another oral cancer cohort (GSE65858), high expressions of C-X-C motif chemokine ligand 10 (CXCL10) and Signal transducer and activator of transcription 2 (STAT2) were confirmed to be good prognostic biomarkers. GSEA of oral cancers stratified by CXCL10/STAT2 expression showed that activation of T-cell pathways and increased tumor infiltration scores of Type 1 T helper (Th1) and CD8+ T cells were associated with high CXCL10/STAT2 expression. These results suggest that high CXCL10/STAT2 expression can predict a favorable outcome in oral cancer patients.