Search

Immunotherapy shows great promise for treating advanced cancers, but its effectiveness varies widely among different patients and cancer types. Identifying biomarkers and developing robust predictive models to discern which patients are most likely to benefit from immunotherapy is of great importance. In this context, we have developed the tumor immunotherapy gene expression R package (tigeR 1.0) to address the increasing need for effective tools to explore biomarkers and construct predictive models. tigeR encompasses four distinct yet closely interconnected modules. The Biomarker Evaluation module enables researchers to evaluate whether the biomarkers of interest are associated with immunotherapy response via built‐in or custom immunotherapy gene expression data. The Tumor Microenvironment Deconvolution module integrates 10 open‐source algorithms to obtain the proportions of different cell types within the tumor microenvironment, facilitating the investigation of the association between immune cell populations and immunotherapy response. The Prediction Model Construction module equips users with the ability to construct sophisticated prediction models using a range of built‐in machine‐learning algorithms. The Response Prediction module predicts the immunotherapy response for the patients from gene expression data using our pretrained machine learning models or public gene expression signatures. By providing these diverse functionalities, tigeR aims to simplify the process of analyzing immunotherapy gene expression data, thus making it accessible to researchers without advanced programming skills. The source code and example for the tigeR project can be accessed at http://github.com/YuLab-SMU/tigeR. Highlights: Tumor Immunotherapy Gene Expression R package (tigeR) is an effective R package to explore biomarkers and construct predictive models to predict immunotherapeutic outcomes.tigeR enables the flexibility to load built‐in or custom gene expression data with immunotherapy outcome information.tigeR encompasses four distinct yet closely interconnected modules, including the Biomarker Evaluation module, Tumor Microenvironment Deconvolution module, Prediction Model Construction module, and Response Prediction module. [ABSTRACT FROM AUTHOR]

The article discusses the features and updates of the ggVennDiagram software, which is used for creating Venn diagrams to illustrate relationships among multiple datasets. The software, built upon existing tools like VennDiagram and RVenn, offers precise region/subset filling and easy-to-use features. The latest version, 1.5, includes improvements such as easier installation, native support for Upset plots, a full functional Venn calculator, and the availability of an official Shiny app and TBtools plugin for interactive use. The authors acknowledge the contributions of various individuals and institutions, and the work was supported by funding from the National Natural Science Foundation of China. The software and related materials are openly available for use. [Extracted from the article]

The increased use of low‐dose computed tomography screening has led to more frequent detection of early stage lung tumors, including minimally invasive adenocarcinoma (MIA). To unravel the intricacies of tumor cells and the immune microenvironment in MIA, this study performs a comprehensive single‐cell transcriptomic analysis and profiles the transcriptomes of 156,447 cells from fresh paired MIA and invasive adenocarcinoma (IA) tumor samples, peripheral blood mononuclear cells, and adjacent normal tissue samples from three patients with synchronous multiple primary lung adenocarcinoma. This study highlights a connection and heterogeneity between the tumor ecosystem of MIA and IA. MIA tumor cells exhibited high expression of aquaporin‐1 and angiotensin II receptor type 2 and a basal‐like molecular character. Furthermore, it identifies that cathepsin B+ tumor‐associated macrophages may over‐activate CD8+ T cells in MIA, leading to an enrichment of granzyme K+ senescent CD8+ T cells, indicating the possibility of malignant progression behind the indolent appearance of MIA. These findings are further validated in 34 MIA and 35 IA samples by multiplexed immunofluorescence. These findings provide valuable insights into the mechanisms that maintain the indolent nature and prompt tumor progression of MIA and can be used to develop more effective therapeutic targets and strategies for MIA patients. [ABSTRACT FROM AUTHOR]

Age‐associated immune changes and pre‐existing influenza immunity are hypothesized to reduce influenza vaccine effectiveness in older adults, although the contribution of each factor is unknown. Here, we constructed influenza‐specific IgG landscapes and determined baseline concentrations of cytokines typically associated with chronic inflammation in older adults (TNF‐α, IL‐10, IL‐6, and IFN‐γ) in 30 high and 29 low influenza vaccine responders (HR and LR, respectively). In a background of high H3 antibody titers, vaccine‐specific H3, but not H1, antibody titers were boosted in LRs to titers comparable to HRs. Pre‐vaccination concentrations of IL‐10 were higher in LRs compared with HRs and inversely correlated with titers of pre‐existing influenza antibodies. Baseline TNF‐α concentrations were positively correlated with fold‐increases in antibody titers in HRs. Our findings indicate that baseline inflammatory status is an important determinant for generating post‐vaccination hemagglutinin‐inhibition antibodies in older adults, and IgG responses can be boosted in the context of high pre‐existing immunity. [ABSTRACT FROM AUTHOR]

We present an r package, ggtree, which provides programmable visualization and annotation of phylogenetic trees., ggtree can read more tree file formats than other softwares, including newick, nexus, NHX, phylip and jplace formats, and support visualization of phylo, multiphylo, phylo4, phylo4d, obkdata and phyloseq tree objects defined in other r packages. It can also extract the tree/branch/node-specific and other data from the analysis outputs of beast, epa, hyphy, paml, phylodog, pplacer, r8s, raxml and revbayes software, and allows using these data to annotate the tree., The package allows colouring and annotation of a tree by numerical/categorical node attributes, manipulating a tree by rotating, collapsing and zooming out clades, highlighting user selected clades or operational taxonomic units and exploration of a large tree by zooming into a selected portion., A two-dimensional tree can be drawn by scaling the tree width based on an attribute of the nodes. A tree can be annotated with an associated numerical matrix (as a heat map), multiple sequence alignment, subplots or silhouette images., The package ggtree is released under the artistic-2.0 license. The source code and documents are freely available through bioconductor (). [ABSTRACT FROM AUTHOR]

Amplification of the activated Cdc42-associated kinase 1 (ACK1) gene is frequent in gastric cancer (GC). However, little is known about the clinical roles and molecular mechanisms of ACK1 abnormalities in GC. Here, we found that the ACK1 protein level and ACK1 phosphorylation at Tyr 284 were frequently elevated in GC and associated with poor patient survival. Ectopic ACK1 expression in GC cells induced epithelial-mesenchymal transition (EMT) and promoted migration and invasion in vitro, and metastasis in vivo; the depletion of ACK1 induced the opposite effects. We utilized SILAC quantitative proteomics to discover that the level of the cell cycle-related protein ecdysoneless homologue (ECD) was markedly altered by ACK1. Overexpression of ECD promoted EMT, migration, and invasion in GC, similar to the effects of ACK1 overexpression. Silencing of ECD completely blocked the augmentation of ACK1 overexpression-induced EMT, migration, and invasion. Mechanistically, ACK1 phosphorylated AKT at Thr 308 and Ser 473 and activated the AKT pathway to up-regulate the transcription factor POU2F1, which directly bound to the promoter region of its novel target gene ECD and thus regulated ECD expression in GC cells. Furthermore, the phosphorylation levels of AKT at Thr 308 and Ser 473 and POU2F1 and ECD levels were positively associated with ACK1 levels in clinical GC specimens. Collectively, we have demonstrated that ACK1 promotes EMT, migration, and invasion by activating AKT-POU2F1-ECD signalling in GC cells. ACK1 may be employed as a new prognostic factor and therapeutic target for GC. [ABSTRACT FROM AUTHOR]