Selective depletion of tumor-infiltrating regulatory T cells with BAY 3375968, a novel Fc-optimized anti-CCR8 antibody.

Regulatory T cells (Tregs) are known to facilitate tumor progression by suppressing CD8+ T cells within the tumor microenvironment (TME), thereby also hampering the effectiveness of immune checkpoint inhibitors (ICIs). While systemic depletion of Tregs can enhance antitumor immunity, it also triggers undesirable autoimmune responses. Therefore, there is a need for therapeutic agents that selectively target Tregs within the TME without affecting systemic Tregs.

Targeting the aryl hydrocarbon receptor (AhR) with BAY 2416964: a selective small molecule inhibitor for cancer immunotherapy.

Background: The metabolism of tryptophan to kynurenines (KYN) by indoleamine-2,3-dioxygenase or tryptophan-2,3-dioxygenase is a key pathway of constitutive and adaptive tumor immune resistance. The immunosuppressive effects of KYN in the tumor microenvironment are predominantly mediated by the aryl hydrocarbon receptor (AhR), a cytosolic transcription factor that broadly suppresses immune cell function. Inhibition of AhR thus offers an antitumor therapy opportunity via restoration of immune system functions.

Pan-PI3K inhibition with copanlisib overcomes Treg- and M2-TAM-mediated immune suppression and promotes anti-tumor immune responses.

The PI3K pathway is one of the most frequently altered signaling pathways in human cancer. In addition to its function in cancer cells, PI3K plays a complex role in modulating anti-tumor immune responses upon immune checkpoint inhibition (ICI). Here, we evaluated the effects of the pan-Class I PI3K inhibitor copanlisib on different immune cell types in vitro and on tumor growth and immune cell infiltration in syngeneic murine cancer models.

Cellenium-a scalable and interactive visual analytics app for exploring multimodal single-cell data.

Summary: Multimodal single-cell sequencing data provide detailed views into the molecular biology of cells. To allow for interactive analyses of such rich data and to readily derive insights from it, new analysis solutions are required. In this work, we present Cellenium, our new scalable visual analytics web application that enables users to semantically integrate and organize all their single-cell RNA-, ATAC-, and CITE-sequencing studies.

Parallel single-cell and bulk transcriptome analyses reveal key features of the gastric tumor microenvironment.

Background: The tumor microenvironment (TME) has been shown to strongly influence treatment outcome for cancer patients in various indications and to influence the overall survival. However, the cells forming the TME in gastric cancer have not been extensively characterized.

Results: We combine bulk and single-cell RNA sequencing from tumors and matched normal tissue of 24 treatment-naïve GC patients to better understand which cell types and transcriptional programs are associated with malignant transformation of the stomach.

Meta-Analysis of Human Cancer Single-Cell RNA-Seq Datasets Using the IMMUcan Database.

Unlabelled: The development of single-cell RNA sequencing (scRNA-seq) technologies has greatly contributed to deciphering the tumor microenvironment (TME). An enormous amount of independent scRNA-seq studies have been published representing a valuable resource that provides opportunities for meta-analysis studies. However, the massive amount of biological information, the marked heterogeneity and variability between studies, and the technical challenges in processing heterogeneous datasets create major bottlenecks for the full exploitation of scRNA-seq data.

TGF-β1 potentiates Vγ9Vδ2 T cell adoptive immunotherapy of cancer.

Despite its role in cancer surveillance, adoptive immunotherapy using γδ T cells has achieved limited efficacy. To enhance trafficking to bone marrow, circulating Vγ9Vδ2 T cells are expanded in serum-free medium containing TGF-β1 and IL-2 (γδ[T2] cells) or medium containing IL-2 alone (γδ[2] cells, as the control). Unexpectedly, the yield and viability of γδ[T2] cells are also increased by TGF-β1, when compared to γδ[2] controls.

Hepatocyte Growth Factor Receptor overexpression predicts reduced survival but its targeting is not effective in unselected HNSCC patients.

Background: MET has emerged as target in head and neck squamous cell carcinoma (HNSCC). However, clinical data on MET inhibition in HNSCC are limited.

Methods: HNSCC biopsies and cell lines were tested for MET activity.

Publisher Correction: Global characterization of T cells in non-small-cell lung cancer by single-cell sequencing.

In the version of this article originally published, the P statistic described in Fig. 3d was incorrect. It was described as "P < 22 × 10".

Global characterization of T cells in non-small-cell lung cancer by single-cell sequencing.

Cancer immunotherapies have shown sustained clinical responses in treating non-small-cell lung cancer, but efficacy varies and depends in part on the amount and properties of tumor infiltrating lymphocytes. To depict the baseline landscape of the composition, lineage and functional states of tumor infiltrating lymphocytes, here we performed deep single-cell RNA sequencing for 12,346 T cells from 14 treatment-naïve non-small-cell lung cancer patients. Combined expression and T cell antigen receptor based lineage tracking revealed a significant proportion of inter-tissue effector T cells with a highly migratory nature.

Drug2Gene: an exhaustive resource to explore effectively the drug-target relation network.

Background: Information about drug-target relations is at the heart of drug discovery. There are now dozens of databases providing drug-target interaction data with varying scope, and focus. Therefore, and due to the large chemical space, the overlap of the different data sets is surprisingly small.

Inferring epigenetic and transcriptional regulation during blood cell development with a mixture of sparse linear models.

Motivation: Blood cell development is thought to be controlled by a circuit of transcription factors (TFs) and chromatin modifications that determine the cell fate through activating cell type-specific expression programs. To shed light on the interplay between histone marks and TFs during blood cell development, we model gene expression from regulatory signals by means of combinations of sparse linear regression models.

Results: The mixture of sparse linear regression models was able to improve the gene expression prediction in relation to the use of a single linear model.

Transcription factor binding predictions using TRAP for the analysis of ChIP-seq data and regulatory SNPs.

The transcription factor affinity prediction (TRAP) method calculates the affinity of transcription factors for DNA sequences on the basis of a biophysical model. This method has proven to be useful for several applications, including for determining the putative target genes of a given factor. This protocol covers two other applications: (i) determining which transcription factors have the highest affinity in a set of sequences (illustrated with chromatin immunoprecipitation-sequencing (ChIP-seq) peaks), and (ii) finding which factor is the most affected by a regulatory single-nucleotide polymorphism.

Predicting gene expression in T cell differentiation from histone modifications and transcription factor binding affinities by linear mixture models.

Background: The differentiation process from stem cells to fully differentiated cell types is controlled by the interplay of chromatin modifications and transcription factor activity. Histone modifications or transcription factors frequently act in a multi-functional manner, with a given DNA motif or histone modification conveying both transcriptional repression and activation depending on its location in the promoter and other regulatory signals surrounding it.

Results: To account for the possible multi functionality of regulatory signals, we model the observed gene expression patterns by a mixture of linear regression models.

A trans-acting locus regulates an anti-viral expression network and type 1 diabetes risk.

Combined analyses of gene networks and DNA sequence variation can provide new insights into the aetiology of common diseases that may not be apparent from genome-wide association studies alone. Recent advances in rat genomics are facilitating systems-genetics approaches. Here we report the use of integrated genome-wide approaches across seven rat tissues to identify gene networks and the loci underlying their regulation.

TransFind--predicting transcriptional regulators for gene sets.

The analysis of putative transcription factor binding sites in promoter regions of coregulated genes allows to infer the transcription factors that underlie observed changes in gene expression. While such analyses constitute a central component of the in-silico characterization of transcriptional regulatory networks, there is still a lack of simple-to-use web servers able to combine state-of-the-art prediction methods with phylogenetic analysis and appropriate multiple testing corrected statistics, which returns the results within a short time. Having these aims in mind we developed TransFind, which is freely available at http://transfind.

CpG-depleted promoters harbor tissue-specific transcription factor binding signals--implications for motif overrepresentation analyses.

Motif overrepresentation analysis of proximal promoters is a common approach to characterize the regulatory properties of co-expressed sets of genes. Here we show that these approaches perform well on mammalian CpG-depleted promoter sets that regulate expression in terminally differentiated tissues such as liver and heart. In contrast, CpG-rich promoters show very little overrepresentation signal, even when associated with genes that display highly constrained spatiotemporal expression.

PASTAA: identifying transcription factors associated with sets of co-regulated genes.

Motivation: A major challenge in regulatory genomics is the identification of associations between functional categories of genes (e.g. tissues, metabolic pathways) and their regulating transcription factors (TFs).

Statistical modeling of transcription factor binding affinities predicts regulatory interactions.

Recent experimental and theoretical efforts have highlighted the fact that binding of transcription factors to DNA can be more accurately described by continuous measures of their binding affinities, rather than a discrete description in terms of binding sites. While the binding affinities can be predicted from a physical model, it is often desirable to know the distribution of binding affinities for specific sequence backgrounds. In this paper, we present a statistical approach to derive the exact distribution for sequence models with fixed GC content.

Predicting transcription factor affinities to DNA from a biophysical model.

Motivation: Theoretical efforts to understand the regulation of gene expression are traditionally centered around the identification of transcription factor binding sites at specific DNA positions. More recently these efforts have been supplemented by experimental data for relative binding affinities of proteins to longer intergenic sequences. The question arises to what extent these two approaches converge.