EZH2 deletion does not impact acinar regeneration but restricts progression to pancreatic cancer in mice.

Enhancer of Zeste Homologue 2 (EZH2) is part of the Polycomb Repressor Complex 2, which promotes trimethylation of lysine 27 on histone 3 (H3K27me3) and genes repression. EZH2 is overexpressed in many cancers and studies in mice attributed both pro-oncogenic and tumor suppressive functions to EZH2 in pancreatic ductal adenocarcinoma (PDAC). EZH2 deletion enhances de novo KRAS-driven neoplasia following pancreatic injury, while increased EZH2 expression in PDAC patients is correlated to poor prognosis, suggesting a context-dependant effect for EZH2 in PDAC progression.

Mouse Models for Pancreatic Ductal Adenocarcinoma are Affected by the cre-driver Used to Promote KRAS Activation.

Background & Aims: The fundamental biology of pancreatic ductal adenocarcinoma has been greatly impacted by the characterization of genetically engineered mouse models that allow temporal and spatial activation of oncogenic KRAS (KRAS). One of the most commonly used models involves targeted insertion of a cre-recombinase into the Ptf1a gene. However, this approach disrupts the Ptf1a gene, resulting in haploinsufficiency that likely affects sensitivity to oncogenic KRAS (KRAS).

Developing OCHROdb, a comprehensive quality checked database of open chromatin regions from sequencing data.

International consortia, including ENCODE, Roadmap Epigenomics, Genomics of Gene Regulation and Blueprint Epigenome have made large-scale datasets of open chromatin regions publicly available. While these datasets are extremely useful for studying mechanisms of gene regulation in disease and cell development, they only identify open chromatin regions in individual samples. A uniform comparison of accessibility of the same regulatory sites across multiple samples is necessary to correlate open chromatin accessibility and expression of target genes across matched cell types.

The EBV Gastric Cancer Resource (EBV-GCR): A Suite of Tools for Investigating EBV-Associated Human Gastric Carcinogenesis.

Epstein-Barr virus (EBV) causes lifelong infection in over 90% of the world's population. EBV infection leads to several types of B cell and epithelial cancers due to the viral reprogramming of host-cell growth and gene expression. EBV is associated with 10% of stomach/gastric adenocarcinomas (EBVaGCs), which have distinct molecular, pathological, and immunological characteristics compared to EBV-negative gastric adenocarcinomas (EBVnGCs).

Evaluation of single-cell RNAseq labelling algorithms using cancer datasets.

Single-cell RNA sequencing (scRNA-seq) clustering and labelling methods are used to determine precise cellular composition of tissue samples. Automated labelling methods rely on either unsupervised, cluster-based approaches or supervised, cell-based approaches to identify cell types. The high complexity of cancer poses a unique challenge, as tumor microenvironments are often composed of diverse cell subpopulations with unique functional effects that may lead to disease progression, metastasis and treatment resistance.

Evaluation of single-cell RNA-seq clustering algorithms on cancer tumor datasets.

Tumors are complex biological entities that comprise cell types of different origins, with different mutational profiles and different patterns of transcriptional dysregulation. The exploration of data related to cancer biology requires careful analytical methods to reflect the heterogeneity of cell populations in cancer samples. Single-cell techniques are now able to capture the transcriptional profiles of individual cells.

Single-Cell Chromatin Accessibility Data Combined with GWAS Improves Detection of Relevant Cell Types in 59 Complex Phenotypes.

Several disease risk variants reside on non-coding regions of DNA, particularly in open chromatin regions of specific cell types. Identifying the cell types relevant to complex traits through the integration of chromatin accessibility data and genome-wide association studies (GWAS) data can help to elucidate the mechanisms of these traits. In this study, we created a collection of associations between the combinations of chromatin accessibility data (bulk and single-cell) with an array of 201 complex phenotypes.

TMExplorer: A tumour microenvironment single-cell RNAseq database and search tool.

Motivation: The tumour microenvironment (TME) contains various cells including stromal fibroblasts, immune and malignant cells, and its composition can be elucidated using single-cell RNA sequencing (scRNA-seq). scRNA-seq datasets from several cancer types are available, yet we lack a comprehensive database to collect and present related TME data in an easily accessible format.

Results: We therefore built a TME scRNA-seq database, and created the R package TMExplorer to facilitate investigation of the TME.

Analysis of TERT Isoforms across TCGA, GTEx and CCLE Datasets.

Reactivation of the multi-subunit ribonucleoprotein telomerase is the primary telomere maintenance mechanism in cancer, but it is rate-limited by the enzymatic component, telomerase reverse transcriptase (TERT). While regulatory in nature, TERT alternative splice variant/isoform regulation and functions are not fully elucidated and are further complicated by their highly diverse expression and nature. Our primary objective was to characterize TERT isoform expression across 7887 neoplastic and 2099 normal tissue samples using The Cancer Genome Atlas (TCGA) and the Genotype-Tissue Expression Project (GTEx), respectively.

Leveraging Public Single-Cell and Bulk Transcriptomic Datasets to Delineate MAIT Cell Roles and Phenotypic Characteristics in Human Malignancies.

Mucosa-associated invariant T (MAIT) cells are unconventional, innate-like T lymphocytes that recognize vitamin B metabolites of microbial origin among other antigens displayed by the monomorphic molecule MHC class I-related protein 1 (MR1). Abundant in human tissues, reactive to local inflammatory cues, and endowed with immunomodulatory and cytolytic functions, MAIT cells are likely to play key roles in human malignancies. They accumulate in various tumor microenvironments (TMEs) where they often lose some of their functional capacities.

CReSCENT: CanceR Single Cell ExpressioN Toolkit.

CReSCENT: CanceR Single Cell ExpressioN Toolkit (https://crescent.cloud), is an intuitive and scalable web portal incorporating a containerized pipeline execution engine for standardized analysis of single-cell RNA sequencing (scRNA-seq) data. While scRNA-seq data for tumour specimens are readily generated, subsequent analysis requires high-performance computing infrastructure and user expertise to build analysis pipelines and tailor interpretation for cancer biology.

Integrative Genetic and Epigenetic Analysis Uncovers Regulatory Mechanisms of Autoimmune Disease.

Genome-wide association studies in autoimmune and inflammatory diseases (AID) have uncovered hundreds of loci mediating risk. These associations are preferentially located in non-coding DNA regions and in particular in tissue-specific DNase I hypersensitivity sites (DHSs). While these analyses clearly demonstrate the overall enrichment of disease risk alleles on gene regulatory regions, they are not designed to identify individual regulatory regions mediating risk or the genes under their control, and thus uncover the specific molecular events driving disease risk.

Network Analysis of Genome-Wide Selective Constraint Reveals a Gene Network Active in Early Fetal Brain Intolerant of Mutation.

Using robust, integrated analysis of multiple genomic datasets, we show that genes depleted for non-synonymous de novo mutations form a subnetwork of 72 members under strong selective constraint. We further show this subnetwork is preferentially expressed in the early development of the human hippocampus and is enriched for genes mutated in neurological Mendelian disorders. We thus conclude that carefully orchestrated developmental processes are under strong constraint in early brain development, and perturbations caused by mutation have adverse outcomes subject to strong purifying selection.

Flow cytometry data standards.

Background: Flow cytometry is a widely used analytical technique for examining microscopic particles, such as cells. The Flow Cytometry Standard (FCS) was developed in 1984 for storing flow data and it is supported by all instrument and third party software vendors. However, FCS does not capture the full scope of flow cytometry (FCM)-related data and metadata, and data standards have recently been developed to address this shortcoming.

Data reduction for spectral clustering to analyze high throughput flow cytometry data.

Background: Recent biological discoveries have shown that clustering large datasets is essential for better understanding biology in many areas. Spectral clustering in particular has proven to be a powerful tool amenable for many applications. However, it cannot be directly applied to large datasets due to time and memory limitations.

Correlation analysis of intracellular and secreted cytokines via the generalized integrated mean fluorescence intensity.

The immune response in humans is usually assessed using immunogenicity assays to provide biomarkers as correlates of protection (CoP). Flow cytometry is the assay of choice to measure intracellular cytokine staining (ICS) of cell-mediated immune (CMI) biomarkers. For CMI analysis, the integrated mean fluorescence intensity (iMFI) was introduced as a metric to represent the total functional CMI response as a CoP.