Biology subjects

Hammerbacher, J.

Publications and source records attributed to Hammerbacher, J..

At least 19 recordsLinked to original sources

bioRxiv2024

Analysis-ready VCF at Biobank scale using Zarr

BackgroundVariant Call Format (VCF) is the standard file format for interchanging genetic variation data and associated quality control metrics. The usual row-wise encoding of the VCF data model (either as text or packed binary) emphasises efficient retrieval of all data for a given variant, but accessing data on a field or sample basis is inefficient. Biobank scale datasets currently available consist of hundreds of thousands of whole genomes and hundreds of terabytes of compressed VCF. Row-wise data storage is fundamentally unsuitable and a more scalable approach is needed. ResultsZarr is a format for storing multi-dimensional data that is widely used across the sciences, and is ideally suited to massively parallel processing. We present the VCF Zarr specification, an encoding of the VCF data model using Zarr, along with fundamental software infrastructure for efficient and reliable conversion at scale. We show how this format is far more efficient than standard VCF based approaches, and competitive with specialised methods for storing genotype data in terms of compression ratios and single-threaded calculation performance. We present case studies on subsets of three large human datasets (Genomics England: n=78,195; Our Future Health: n=651,050; All of Us: n=245,394) along with whole genome datasets for Norway Spruce (n=1,063) and SARS-CoV-2 (n=4,484,157). We demonstrate the potential for VCF Zarr to enable a new generation of high-performance and cost-effective applications via illustrative examples using cloud computing and GPUs. ConclusionsLarge row-encoded VCF files are a major bottleneck for current research, and storing and processing these files incurs a substantial cost. The VCF Zarr specification, building on widely-used, open-source technologies has the potential to greatly reduce these costs, and may enable a diverse ecosystem of next-generation tools for analysing genetic variation data directly from cloud-based object stores, while maintaining compatibility with existing file-oriented workflows. Key PointsO_LIVCF is widely supported, and the underlying data model entrenched in bioinformatics pipelines. C_LIO_LIThe standard row-wise encoding as text (or binary) is inherently inefficient for large-scale data processing. C_LIO_LIThe Zarr format provides an efficient solution, by encoding fields in the VCF separately in chunk-compressed binary format. C_LI

bioinformatics↗

bioRxiv2021

Discovering tumor-reactive T-cell receptors through single-cell sequencing of tumor-infiltrating lymphocytes

We evaluated the utility of single-cell sequencing of tumor-infiltrating lymphocytes (TIL) for tumor-reactive T-cell receptor (TCR) discovery. Using the MC38 cell line as our tumor model in mice, we show that expression of exogenous TCRs via mRNA electroporation in human T cells provides an easy and quick path to validating tumor-specific candidate TCRs. We detail the identification and validation of four novel MC38-reactive mouse TCRs with varying levels of reactivity to the target cells. Validating our process, one of the MC38 TCRs is specific against a previously reported neoantigen (ASMTNMELM in the Adpgk gene). Consideration of these methodologies may aid in the development of rapid TCR-based therapies for the treatment of cancer and human disease. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=197 SRC="FIGDIR/small/470597v1_ufig1.gif" ALT="Figure 1"> View larger version (31K): org.highwire.dtl.DTLVardef@792d44org.highwire.dtl.DTLVardef@18af6aforg.highwire.dtl.DTLVardef@498c2aorg.highwire.dtl.DTLVardef@9127e8_HPS_FORMAT_FIGEXP M_FIG C_FIG

immunology↗

bioRxiv2020

Massively parallel quantification of phenotypic heterogeneity in single cell drug responses

Single cell analysis tools have made significant advances in characterizing genomic heterogeneity, however tools for measuring phenotypic heterogeneity have lagged due to the increased difficulty of handling live biology. Here, we report a single cell phenotyping tool capable of measuring image-based clonal properties at scales approaching 100,000 clones per experiment. These advances are achieved by exploiting a novel flow regime in ladder microfluidic networks that, under appropriate conditions, yield a mathematically perfect cell trap. Machine learning and computer vision tools are used to control the imaging hardware and analyze the cellular phenotypic parameters within these images. Using this platform, we quantified the responses of tens of thousands of single cell-derived acute myeloid leukemia (AML) clones to targeted therapy, identifying rare resistance and morphological phenotypes at frequencies down to 0.05%. This approach can be extended to higher-level cellular architectures such as cell pairs and organoids and on-chip live-cell fluorescence assays.

bioengineering↗

bioRxiv2019

Towards a scaled-up T cell-mediated cytotoxicity assay in 3D cell culture using microscopy

Three-dimensional (3D) cell culture systems with tumor spheroids are being adopted for research on the antitumor activity of drug treatments and cytotoxic T cells. Analysis of the cytotoxic effect on 3D tumor cultures within a 3D scaffold, such as collagen, is challenging. Image-based approaches often use confocal microscopy, which greatly limits the sample size of tumor spheroids that can be assayed. We explored a system where tumor spheroids growing in a collagen gel within a microfluidics chip can be treated with drugs or co-cultured with T cells. We attempted to adapt the system to measure the death of cells in the tumor spheroids directly in the microfluidics chip via automated widefield fluorescence microscopy. We were able to successfully measure drug-induced cytotoxicity in tumor spheroids, but had difficulties extending the system to measure T cell-mediated tumor killing. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=193 SRC="FIGDIR/small/842039v1_ufig1.gif" ALT="Figure 1"> View larger version (44K): org.highwire.dtl.DTLVardef@c95725org.highwire.dtl.DTLVardef@785728org.highwire.dtl.DTLVardef@a23dceorg.highwire.dtl.DTLVardef@187bd74_HPS_FORMAT_FIGEXP M_FIG C_FIG

immunology↗

bioRxiv2019

Extracting T Cell Function and Differentiation Characteristics from the Biomedical Literature

The role of many cytokines and transcription factors in the function and development of human T cells has been the subject of extensive research, however much of this work only demonstrates experimental findings for a relatively small portion of the molecular signaling network that enables the plasticity inherent to these cells. We apply recent advancements in methods for weak supervision and transfer learning for natural language models to aid in extracting these individual findings as 283k cell type, cytokine, and transcription factor relations from 64k relevant documents (53k full-text PMC articles and 11k PubMed abstracts). All data, results and source code available at https://github.com/hammerlab/t-cell-relation-extraction.

immunology↗

bioRxiv2019

Computational and experimental optimization of T cell activation

Bead-based activation is widely-used for ex vivo expansion of T cells for either research or clinical purposes. Despite its wide use, culture conditions that can potentially affect the efficiency of bead-based T cell activation has not been extensively documented. With the help of computationally-driven experimental investigations of basic culturing factors, we found that culture density, bead-to-cell ratio, and debeading time can have a major impact on the efficiency of bead-based T cell activation for short-term cultures. Furthermore, discrepancies across expected and observed activation efficiencies helped discover interesting artifacts of bead-based T cell activation.\n\n\n\nO_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=152 SRC=\"FIGDIR/small/629857v1_ufig1.gif\" ALT=\"Figure 1\">\nView larger version (104K):\norg.highwire.dtl.DTLVardef@107140corg.highwire.dtl.DTLVardef@1b52475org.highwire.dtl.DTLVardef@16c95cborg.highwire.dtl.DTLVardef@1de5184_HPS_FORMAT_FIGEXP M_FIG Human primary T cells were imaged together with activation beads at 20X magnification after three hours of culturing at varying confluencies and bead-to-cell ratios.\n\nC_FIG

immunology↗

bioRxiv2018

Electroporation characteristics of human primary T cells

Electroporation is the most feasible non-viral material delivery system for manipulating human T cells given its time- and cost-effectiveness. However, efficient delivery requires electroporation settings to be optimized for different devices, cellular states, and materials to be delivered. Here, we used electroporation to either induce exogenous gene expression in human primary T cells by plasmids or in vitro transcribed (IVT) mRNA and also target endogenous genes by Cas9 ribonucleoproteins (RNPs). We characterized the electroporation conditions both for activated and unstimulated human T cells. Although naive cells are non-dividing and therefore their genetic manipulation is harder compared to activated T cells, we developed the technical ability to manipulate both naive and memory cells within the unstimulated T cell population by IVT mRNA and Cas9 RNP electroporation. Here, we outline the best practices for achieving highly-efficient genetic manipulation in primary T cells without causing significant cytotoxicity to the cells. Because there is increasing evidence for \"less-differentiated\" T cells to have better anti-tumor activity for immunotherapy, manipulating naive T cells with high efficiency is also of high importance to clinical applications and to study the biology of these cells.

immunology↗

bioRxiv2018

Cytokit: A single-cell analysis toolkit for high dimensional fluorescent microscopy imaging

BackgroundMultiplexed in-situ fluorescent imaging offers several advantages over single-cell assays that do not preserve the spatial characteristics of biological samples. This spatial information, in addition to morphological properties and extensive intracellular or surface marker profiling, comprise promising avenues for rapid advancements in the understanding of disease progression and diagnosis. As protocols for conducting such imaging experiments continue to improve, it is the intent of this study to provide and validate software for processing the large quantity of associated data in kind.\n\nResultsCytokit offers (i) an end-to-end, GPU-accelerated image processing pipeline; (ii) efficient input/output (I/O) strategies for operations specific to high dimensional microscopy; and (iii) an interactive user interface for cross filtering of spatial, graphical, expression, and morphological cell properties within the 100+ GB image datasets common to multiplexed immunofluorescence. Image processing operations supported in Cytokit are generally sourced from existing deep learning models or are at least in part adapted from open source packages to run in a single or multi-GPU environment. The efficacy of these operations is demonstrated through several imaging experiments that pair Cytokit results with those from an independent but comparable assay. A further validation also demonstrates that previously published results can be reproduced from a publicly available multiplexed image dataset.\n\nConclusionCytokit is a collection of open source tools for quantifying and analyzing properties of individual cells in large fluorescent microscopy datasets that are often, but not necessarily, generated from multiplexed antibody labeling protocols over many fields of view or time periods. This project is best suited to bioinformaticians or other technical users that wish to analyze such data in a batch-oriented, high-throughput setting. All source code, documentation, and data generated for this article are available under the Apache License 2.0 at https://github.com/hammerlab/cytokit.

bioinformatics↗

bioRxiv2018

Coral: Clear and customizable visualization of human kinome data

Protein kinases represent one ofthe largest gene families in eukaryotes and play roles in a wide range of cell signaling processes and human diseases, making them attractive targets for drug development. The human kinome is extensively featured inhigh-throughput studiesgenerating genomic, proteomic, and kinase profiling data. Current tools for visualizing kinase data in the context of the human kinome superfamily are limited to encoding data through the addition of nodes to a low-resolution image of the kinome tree. We present Coral, a user-friendly interactive webapplication for visualizing both quantitative and qualitative data. Unlike previous tools, Coral can encode data in three features (node color, node size, and branchcolor), allowsthreemodesofkinomevisualization (the traditional kinome tree as well as radial and dynamic-force networks) and generates high-resolution scalable vector graphic files suitable for publication without the need for refinement in graphic editing software. Due to its user-friendly, interactive, and highly customizable design, Coral is broadly applicable to high-throughput studies of the human kinome. The source code and web application are available at github.com/dphansti/Coral. html and phanstiel-lab.med.unc.edu/Coral respectively.

bioinformatics↗

bioRxiv2018

Prohlatype: A Probabilistic Framework for HLA Typing.

HLA typing from sequencing data is considered as a classical probabilistic inference problem and Profile Hidden Markov Models (PHMM) are motivated for the likelihood calculation. Their generative property makes them a natural and highly discernible method; at the cost of considerable computation. We discuss ways to ameliorate this burden, and present an implementation https://github.com/hammerlab/prohlatype.

genomics↗

bioRxiv2017

Bioinformatics Workflow Management With The Wobidisco Ecosystem

ReferencesTo conduct our computational experiments, our team developed a set of workflow-management-related projects: Ketrew, Biokepi, and Coclobas. The family of tools and libraries are designed with reliability and flexibility as main guiding principles. We describe the components of the software stack and explain the choices we made. Every piece of software is free and open-source; the umbrella documentation project is available at https://github.com/hammerlab/wobidisco.

bioinformatics↗

bioRxiv2017

Computational pipeline for the PGV-001 neoantigen vaccine trial

This paper describes the sequencing protocol and computational pipeline for the PGV-001 personalized vaccine trial. PGV-001 is a therapeutic peptide vaccine targeting neoantigens identified from patient tumor samples. Peptides are selected by a computational pipeline which identifies mutations from tumor/normal exome sequencing and ranks mutant sequences by a combination of predicted Class I MHC affinity and abundance estimated from tumor RNA. The PGV pipeline is modular and consists of many independently usable tools and software libraries. We draw attention to three particular tools which may be useful to other groups working on neoantigen vaccination. (1) Epidisco is a workflow which orchestrates the parallel execution of the PGV pipeline, including both common steps such as alignment as well as tools which have been developed specifically for the PGV-001 trial. (2) Vaxrank uses somatic variants and tumor RNA reads to select peptides for inclusion in a patients vaccine. (3) Isovar is a library used by Vaxrank to determine the mutated protein sequence associated with a genomic variant from supporting tumor RNA reads. We hope that the functionality of these tools may extend beyond the specifics of the PGV-001 trial and enable other research groups in their own neoantigen investigations.

bioinformatics↗

bioRxiv2017

MHCflurry: open-source class I MHC binding affinity prediction

Machine learning prediction of the interaction between major histocompatibility complex I (MHC I) proteins and their small peptide ligands is important for vaccine design and other applications in adaptive immunity. We describe and benchmark a new open-source MHC I binding prediction package, MHCflurry. The software is a collection of allele-specific binding predictors incorporating a novel neural network architecture and adhering to software development best practices. MHCflurry outperformed the standard predictors NetMHC 4.0 and NetMHCpan 3.0 on a benchmark of mass spec-identified MHC ligands and showed competitive accuracy on a benchmark of affinity measurements. The accuracy improvement was due to substantially better prediction of non-9-mer peptide ligands, which offset a narrowly lower accuracy on 9-mers. MHCflurry was on average 8.6X faster than NetMHC and 44X faster than NetMHCpan; performance is further increased when a graphics processing unit (GPU) is available. MHCflurry is freely available to use, retrain, or extend, includes Python library and command line interfaces, and may be installed using standard package managers.

immunology↗

bioRxiv2017

PI3Kδ inhibition supports memory T cells with enhanced antitumor fitness

Phosphatidylinositol-3-kinase p110{delta} (PI3K{delta}) inhibition by Idelalisib (CAL-101) in hematological malignancies directly induces apoptosis in cancer cells and disrupts immunological tolerance by depleting regulatory T cells (Tregs). Yet, little is known about the direct impact of PI3K{delta} blockade on effector T cells from CAL-101 therapy. Herein, we demonstrate a direct effect of p110{delta} inactivation via CAL-101 on murine and human CD8+ T cells that promotes a strong undifferentiated memory phenotype (elevated CD62L/CCR7, CD127 and Tcf7). These CAL-101 T cells also persisted longer after transfer and exerted stronger antitumor immunity compared to traditionally expanded CD8+ T cells in two solid tumor models. Thus, this report describes a novel direct enhancement of CD8+ T cell memory by a p110{delta} inhibitor that leads to markedly improved tumor regression. This finding has significant implications to improve outcomes from next generation cancer immunotherapies.\n\nHighlightsO_LIIn vitro blockade of PI3K p110{delta} with CAL-101 endows antitumor T cells with a stronger memory phenotype than those treated with AKTi\nC_LIO_LIThe strong memory phenotype of CAL-101 treated cells translates into improved survival of mice bearing aggressive tumors after adoptive transfer of these T cells\nC_LIO_LIHuman CAR engineered T cells treated with CAL-101 possess an enhanced memory phenotype and robust antitumor efficacy\nC_LIO_LIThe antitumor efficacy of CAL-101 primed T cells is not mediated by high CD62L or CD127 expression, but is likely driven by their stem memory phenotype\nC_LI\n\neTOC BlurbBowers et al report a novel function of PI3K blockade using the p110{delta} subunit inhibitor CAL-101 to induce memory and antitumor potency in CD8+ T cells. Ex vivo treatment of T cells with CAL-101 leads to improved antitumor control and subject survival in both murine transgenic T cell and human CAR T cell models.

cancer biology↗

bioRxiv2017

Informatics for Cancer Immunotherapy

The rapid development of immunomodulatory cancer therapies has led to a concurrent increase in the application of informatics techniques to the analysis of tumors, the tumor microenvironment, and measures of systemic immunity. In this review, the use of tumors to gather genetic and expression data will first be explored. Next, techniques to assess tumor immunity are reviewed, including HLA status, predicted neoantigens, immune microenvironment deconvolution and T-cell receptor (TCR) sequencing. Attempts to integrate these data are in early stages of development and are discussed next. Finally, we review the application of these informatics strategies to therapy development, with a focus on vaccines, adoptive cell transfer, and checkpoint blockade therapies.

bioinformatics↗

bioRxiv2017

Vaxrank: A Computational Tool For Designing Personalized Cancer Vaccines

Therapeutic vaccines targeting mutant tumor antigens (\"neoantigens\") are an increasingly popular form of personalized cancer immunotherapy. Vaxrank is a computational tool for selecting neoantigen vaccine peptides from tumor mutations, tumor RNA data, and patient HLA type. Vaxrank is freely available at www.github.com/openvax/vaxrank under the Apache 2.0 open source license and can also be installed from the Python Package Index.

bioinformatics↗

bioRxiv2017

Cycledash: a web application for the interactive analysis and exploration of variants

As genomics begins to influence clinical care, validating the result of a somatic variant calling pipeline has become increasingly important. Cycledash is a web application which facilitates the validation and analysis of somatic variants, bringing together and streamlining existing tools and workflows for examining and verifying the existence of variants in patient samples.\n\nAvailabilityThe source code is freely available at https://github.com/hammerlab/cycledash under the Apache 2 License. A public demo (username cycledash password cycledash123) is available at http://cycledash.hammerlab.org. The web application is tested to work in recent version of Firefox, Safari, and Chrome, and the server applications will run on Linux and OS X systems.\n\nContactcorrespondence@hammerlab.org

bioinformatics↗

bioRxiv2016

Chemotherapy weakly contributes to predicted neoantigen expression in ovarian cancer

BackgroundPatients with highly mutated tumors, such as melanoma or smoking-related lung cancer, have higher rates of response to immune checkpoint blockade therapy, perhaps due to increased neoantigen expression. Many chemotherapies including platinum compounds are known to be mutagenic, but the impact of standard treatment protocols on mutational burden and resulting neoantigen expression in most human cancers is unknown.\n\nMethodsWe sought to quantify the effect of chemotherapy treatment on computationally predicted neoantigen expression for 12 high grade serous ovarian carcinoma (HGSC) patients with pre- and post-chemotherapy samples collected in the Australian Ovarian Cancer Study. We additionally analyzed 16 patients from the cohort with post-treatment samples only, including five primary surgical samples exposed to neoadjuvant chemotherapy. Our approach integrates tumor whole genome and RNA sequencing with class I MHC binding prediction and mutational signatures of chemotherapy exposure extracted from two preclinical studies.\n\nResultsThe mutational signatures for cisplatin and cyclophosphamide identified in a preclinical model had significant but inexact associations with the relevant exposure in the clinical samples. In an analysis stratified by tissue type (solid tumor or ascites), relapse samples collected after chemotherapy harbored a median of 90% more expressed neoantigens than untreated primary samples, a figure that combines the effects of chemotherapy and other mutagenic processes operative during relapse. Neoadjuvant-treated primary samples showed no detectable increase over untreated samples. The contribution from chemotherapy-associated signatures was small, accounting for a mean of 5% (range 0-16) of the expressed neoantigen burden in relapse samples. In both treated and untreated samples, most neoantigens were attributed to COSMIC Signature (3), associated with BRCA disruption, Signature (1), associated with a slow mutagenic process active in healthy tissue, and Signature (8), of unknown etiology.\n\nConclusionRelapsed HGSC tumors harbor nearly double the predicted expressed neoantigen burden of primary samples, but mutations associated with chemotherapy signatures account for only a small part of this increase. The mutagenic processes responsible for most neoantigens are similar between primary and relapse samples. Our analyses are based on mutations detectable from whole genome sequencing of bulk samples and do not account for neoantigens present in small populations of cells.

cancer biology↗