Hypoimmune anti-CD19 chimeric antigen receptor T cells provide lasting tumor control in fully immunocompetent allogeneic humanized mice.

Manufacturing autologous chimeric antigen receptor (CAR) T cell therapeutics is complex, and many patients experience treatment delays or cannot be treated at all. Although current allogeneic CAR products have the potential to overcome manufacturing bottlenecks, they are subject to immune rejection and failure to persist in the host, and thus do not provide the same level of efficacy as their autologous counterparts. Here, we aimed to develop universal allogeneic CAR T cells that evade the immune system and produce a durable response. We generated human hypoimmune (HIP) T cells with disrupted B2M, CIITA, and TRAC genes using CRISPR-Cas9 editing. In addition, CD47 and anti-CD19 CAR were expressed using lentiviral transduction. These allogeneic HIP CD19 CAR T cells were compared to allogeneic CD19 CAR T cells that only expressed the anti-CD19 CAR (allo CAR T). In vitro assays for cancer killing and exhaustion revealed no differences between allo CAR T and HIP CAR T cells, confirming that the HIP edits did not negatively affect T cell performance. Clearance of CD19+ tumors by HIP CAR T cells in immunodeficient NSG mice was comparable to that of allo CAR T cells. In fully immunocompetent humanized mice, HIP CAR T cells significantly outperformed allo CAR T cells, showed improved persistence and expansion, and provided lasting cancer clearance. Furthermore, CD47-targeting safety strategies reliably and specifically eliminated HIP CAR T cells. These findings suggest that universal allogeneic HIP CAR T cell-based therapeutics might overcome the limitations associated with poor persistence of allogeneic CAR T cells and exert durable anti-tumor responses.

A (fire)cloud-based DNA methylation data preprocessing and quality control platform.

BACKGROUND: Bisulfite sequencing allows base-pair resolution profiling of DNA methylation and has recently been adapted for use in single-cells. Analyzing these data, including making comparisons with existing data, remains challenging due to the scale of the data and differences in preprocessing methods between published datasets. RESULTS: We present a set of preprocessing pipelines for bisulfite sequencing DNA methylation data that include a new R/Bioconductor package, scmeth, for a series of efficient QC analyses of large datasets. The pipelines go from raw data to CpG-level methylation estimates and can be run, with identical results, either on a single computer, in an HPC cluster or on Google Cloud Compute resources. These pipelines are designed to allow users to 1) ensure reproducibility of analyses, 2) achieve scalability to large whole genome datasets with 100 GB+ of raw data per sample and to single-cell datasets with thousands of cells, 3) enable integration and comparison between user-provided data and publicly available data, as all samples can be processed through the same pipeline, and 4) access to best-practice analysis pipelines. Pipelines are provided for whole genome bisulfite sequencing (WGBS), reduced representation bisulfite sequencing (RRBS) and hybrid selection (capture) bisulfite sequencing (HSBS). CONCLUSIONS: The workflows produce data quality metrics, visualization tracks, and aggregated output for further downstream analysis. Optional use of cloud computing resources facilitates analysis of large datasets, and integration with existing methylome profiles. The workflow design principles are applicable to other genomic data types.

Preprocessing and Computational Analysis of Single-Cell Epigenomic Datasets.

Recent technological developments have enabled the characterization of the epigenetic landscape of single cells across a range of tissues in normal and diseased states and under various biological and chemical perturbations. While analysis of these profiles resembles methods from single-cell transcriptomic studies, unique challenges are associated with bioinformatics processing of single-cell epigenetic data, including a much larger (10-1,000×) feature set and significantly greater sparsity, requiring customized solutions. Here, we discuss the essentials of the computational methodology required for analyzing common single-cell epigenomic measurements for DNA methylation using bisulfite sequencing and open chromatin using ATAC-Seq.