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Aksoy, B. A.

Publications and source records attributed to Aksoy, B. A..

15 recordsLinked to original sources

The degree of T cell stemness differentially impacts the potency of adoptive cancer immunotherapy in a Lef-1 and Tcf-1 dependent manner.

Generating stem memory T cells (TSCM) is a key goal for improving cancer immunotherapy. Yet, the optimal way to modulate signaling pathways that enrich TSCM properties remains elusive. Here, we discovered that the degree to which the PI3K{delta} pathway is blocked pharmaceutically can generate T cells with differential levels of stemness properties. This observation was based on the progressive enrichment of transcriptional factors of stemness (Tcf-1 and Lef-1). Additional investigation revealed that T cells with high stemness features had enhanced metabolic plasticity, marked by heightened mitochondrial function and glucose uptake. Conversely, T cells with low or medium features of stemness expressed more inhibitory checkpoint receptors (Tim-3, CD39) and were vulnerable to antigen-induced cell death. Only TCR-antigen specific T cells with high stemness persisted following adoptive transfer in vivo and mounted protective immunity to melanoma tumors. Likewise, the strongest level of PI3K{delta} blockade in vitro generated human tumor infiltrating lymphocytes (TILs) and CAR T cells with heightened stemness properties, in turn bolstering their capacity to regress human mesothelioma tumors. We find that the level of stemness T cells possess in vitro differentially impacts their potency upon transfer in three tumor models. Mechanistically, both Lef-1 and Tcf-1 sustain anti-tumor protection by high TSCM, as deletion of either one compromised cellular therapy. Collectively, these findings highlight the therapeutic potential of carefully modulating PI3K{delta} signaling in T cells to confer high stemness and mediate protective responses to solid tumors.

immunology↗

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↗

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↗

Pathway Commons: 2019 Update

Pathway Commons (https://www.pathwaycommons.org) is an integrated resource of publicly available information about biological pathways including biochemical reactions, assembly of biomolecular complexes, transport and catalysis events and physical interactions involving proteins, DNA, RNA, and small molecules (e.g., metabolites and drug compounds). Data is collected from multiple providers in standard formats, including the Biological Pathway Exchange (BioPAX) language and the Proteomics Standards Initiative Molecular Interactions format, and then integrated. Pathway Commons provides biologists with (1) tools to search this comprehensive resource, (2) a download site offering integrated bulk sets of pathway data (e.g., tables of interactions and gene sets), (3) reusable software libraries for working with pathway information in several programming languages (Java, R, Python, and Javascript), and (4) a web service for programmatically querying the entire dataset. Visualization of pathways is supported using the Systems Biological Graphical Notation (SBGN). Pathway Commons currently contains data from 22 databases with 4,794 detailed human biochemical processes (i.e., pathways) and [~]2.3 million interactions. To enhance the usability of this large resource for end-users, we develop and maintain interactive web applications and training materials that enable pathway exploration and advanced analysis.

bioinformatics↗

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↗

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↗

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↗

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↗

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↗

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↗

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↗

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↗

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↗

Somatic Mutations and Neoepitope Homology in Melanomas Treated with CTLA-4 Blockade

Immune checkpoint inhibitors are promising treatments for patients with a variety of malignancies. Toward understanding the determinants of response to immune checkpoint inhibitors, it was previously demonstrated that somatic mutation burden is associated with benefit and a hypothesis was posited that neoantigen homology to pathogens may in part explain the link between somatic mutations and response. To further examine this hypothesis, we reanalyzed cancer exome data obtained from a previously published study of 64 melanoma patients treated with CTLA-4 blockade and a new dataset of RNA-Seq data from 24 of those patients. We found that the predictive accuracy does not increase as analysis narrows from somatic mutation burden to predicted MHC Class I neoantigens, expressed neoantigens, or homology to pathogens. Further, the association between somatic mutation burden and response is only found when examining samples obtained prior to treatment. Neoantigen and expressed neoantigen burden are also associated with response, but neither is more predictive than somatic mutation burden. Neither the previously-described tetrapeptide signature nor an updated method to evaluate neoepitope homology to pathogens were more predictive than mutation burden.

cancer biology↗

Multi-omic analysis of urothelial cancer patients treated with PD-L1 blockade demonstrates the contribution of both systemic and somatic factors to the biology of response and resistance

BackgroundInhibition of programmed death-ligand one (PD-L1) with atezolizumab can induce durable clinical benefit (DCB) in patients with metastatic urothelial cancers, including complete remissions in patients with chemotherapy refractory disease. Although mutation load and PD-L1 immune cell (IC) staining have been associated with response, they lack sufficient sensitivity and specificity for clinical use. Thus, there is a need to evaluate the peripheral blood immune environment and to conduct detailed analyses of mutation load, predicted neoantigens and immune cellular infiltration in tumors to enhance our understanding of the biologic underpinnings of response and resistance.\n\nMethods and FindingsThe goals of this study were to (1) evaluate the association of mutation load and predicted neoantigen load with therapeutic benefit, and (2) determine whether intratumoral and peripheral blood T cell receptor (TCR) clonality inform clinical outcomes in urothelial carcinoma treated with atezolizumab. We hypothesized that an elevated mutation load in combination with T cell clonal dominance among intratumoral lymphocytes prior to treatment or among peripheral T cells after treatment would be associated with effective tumor control upon treatment with anti-PD-L1 therapy. We performed whole exome sequencing (WES), RNA sequencing (RNA-seq), and T cell receptor sequencing (TCR-seq) of pre-treatment tumor samples as well as TCR sequencing of matched, serially collected peripheral blood collected before and after treatment with atezolizumab. These parameters were assessed for correlation with DCB (defined as progression free survival (PFS) > 6 months), PFS, and overall survival (OS), both alone and in the context of clinical and intratumoral parameters known to be predictive of survival in this disease state.\n\nPatients with DCB displayed a higher proportion of tumor infiltrating T lymphocytes (TIL) (n=24, Mann-Whitney p=0.047). Pre-treatment peripheral blood TCR clonality below the median was associated with improved PFS (n=29, log-rank p=0.048) and OS (n=29, log-rank p=0.011). Patients with DCB also demonstrated more substantial expansion of tumor-associated TCR clones in the peripheral blood 3 weeks after starting treatment (n=22, Mann-Whitney p=0.022). The combination of high pre-treatment peripheral blood TCR clonality with elevated PD-L1 IC staining in tumor tissue was strongly associated with poor clinical outcomes (n=10, HR (mean)=89.88, HR (median)=23.41, 95% CI (2.43, 506.94), p(HR>1)=0.0014). Marked variations in mutation loads were seen with different somatic variant calling methodologies, which in turn impacted associations with clinical outcomes. Missense mutation load, predicted neoantigen load and expressed neoantigen load did not demonstrate significant association with DCB (n=25, Mann-Whitney p=0.22, n=25, Mann-Whitney p=0.55, and n=25, Mann-Whitney p=0.29 respectively). Instead, we found evidence of time-varying effects of somatic mutation load on progression-free survival in this cohort (n=25, p=0.044). A limitation of our study is its small sample size (n=29), a subset of the patients treated on IMvigor 210 (NCT02108652). Given the number of exploratory analyses performed, we intend for these results to be hypothesis-generating.\n\nConclusionsThese results demonstrate the complex nature of immune response to checkpoint blockade and the compelling need for greater interrogation and data integration of both host and tumor factors. Incorporating these variables in prospective studies will facilitate identification and treatment of resistant patients.

cancer biology↗