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      Activated Regulatory T-Cells, Dysfunctional and Senescent T-Cells Hinder the Immunity in Pancreatic Cancer

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          Abstract

          Simple Summary

          Pancreatic cancer has the worst survival of any human cancer. Checkpoint blockade has not yielded much benefit in pancreatic cancer. We explored immune cell phenotypes with this disease to identify new targets for checkpoint blockade therapy. We created a checkpoint-focused panel to analyse immune cells from eight pancreatic cancer patients. This showed us the majority of T-cells are senescent. Further T-cell investigation demonstrated the majority of cytotoxic T-cells have intermediate to low PD1 expression suggesting why PD1 may not work as a pancreatic cancer therapy strategy. Our data has also highlighted a regulatory T-cell population which is highly activated and can mediate immunosuppression. The checkpoints that are highly expressed on these cells are TIGIT, ICOS and CD39, suggesting inhibition of these may be a viable therapeutic strategy. Furthermore, we showed that Tregs were retained amongst the fibroblast stroma of the tumour. Our work suggests there are key checkpoints on Tregs that may help guide therapeutic strategies in pancreatic cancer.

          Abstract

          Pancreatic cancer has one of the worst prognoses of any human malignancy and leukocyte infiltration is a major prognostic marker of the disease. As current immunotherapies confer negligible survival benefits, there is a need to better characterise leukocytes in pancreatic cancer to identify better therapeutic strategies. In this study, we analysed 32 human pancreatic cancer patients from two independent cohorts. A multi-parameter mass-cytometry analysis was performed on 32,000 T-cells from eight patients. Single-cell RNA sequencing dataset analysis was performed on a cohort of 24 patients. Multiplex immunohistochemistry imaging and spatial analysis were performed to map immune infiltration into the tumour microenvironment. Regulatory T-cell populations demonstrated highly immunosuppressive states with high TIGIT, ICOS and CD39 expression. CD8 + T-cells were found to be either in senescence or an exhausted state. The exhausted CD8 T-cells had low PD-1 expression but high TIGIT and CD39 expression. These findings were corroborated in an independent pancreatic cancer single-cell RNA dataset. These data suggest that T-cells are major players in the suppressive microenvironment of pancreatic cancer. Our work identifies multiple novel therapeutic targets that should form the basis for rational design of a new generation of clinical trials in pancreatic ductal adenocarcinoma.

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          Most cited references65

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          Cancer Statistics, 2021

          Each year, the American Cancer Society estimates the numbers of new cancer cases and deaths in the United States and compiles the most recent data on population-based cancer occurrence. Incidence data (through 2017) were collected by the Surveillance, Epidemiology, and End Results Program; the National Program of Cancer Registries; and the North American Association of Central Cancer Registries. Mortality data (through 2018) were collected by the National Center for Health Statistics. In 2021, 1,898,160 new cancer cases and 608,570 cancer deaths are projected to occur in the United States. After increasing for most of the 20th century, the cancer death rate has fallen continuously from its peak in 1991 through 2018, for a total decline of 31%, because of reductions in smoking and improvements in early detection and treatment. This translates to 3.2 million fewer cancer deaths than would have occurred if peak rates had persisted. Long-term declines in mortality for the 4 leading cancers have halted for prostate cancer and slowed for breast and colorectal cancers, but accelerated for lung cancer, which accounted for almost one-half of the total mortality decline from 2014 to 2018. The pace of the annual decline in lung cancer mortality doubled from 3.1% during 2009 through 2013 to 5.5% during 2014 through 2018 in men, from 1.8% to 4.4% in women, and from 2.4% to 5% overall. This trend coincides with steady declines in incidence (2.2%-2.3%) but rapid gains in survival specifically for nonsmall cell lung cancer (NSCLC). For example, NSCLC 2-year relative survival increased from 34% for persons diagnosed during 2009 through 2010 to 42% during 2015 through 2016, including absolute increases of 5% to 6% for every stage of diagnosis; survival for small cell lung cancer remained at 14% to 15%. Improved treatment accelerated progress against lung cancer and drove a record drop in overall cancer mortality, despite slowing momentum for other common cancers.
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            Integrating single-cell transcriptomic data across different conditions, technologies, and species

            Computational single-cell RNA-seq (scRNA-seq) methods have been successfully applied to experiments representing a single condition, technology, or species to discover and define cellular phenotypes. However, identifying subpopulations of cells that are present across multiple data sets remains challenging. Here, we introduce an analytical strategy for integrating scRNA-seq data sets based on common sources of variation, enabling the identification of shared populations across data sets and downstream comparative analysis. We apply this approach, implemented in our R toolkit Seurat (http://satijalab.org/seurat/), to align scRNA-seq data sets of peripheral blood mononuclear cells under resting and stimulated conditions, hematopoietic progenitors sequenced using two profiling technologies, and pancreatic cell 'atlases' generated from human and mouse islets. In each case, we learn distinct or transitional cell states jointly across data sets, while boosting statistical power through integrated analysis. Our approach facilitates general comparisons of scRNA-seq data sets, potentially deepening our understanding of how distinct cell states respond to perturbation, disease, and evolution.
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              Fast, sensitive, and accurate integration of single cell data with Harmony

              The emerging diversity of single cell RNAseq datasets allows for the full transcriptional characterization of cell types across a wide variety of biological and clinical conditions. However, it is challenging to analyze them together, particularly when datasets are assayed with different technologies. Here, real biological differences are interspersed with technical differences. We present Harmony, an algorithm that projects cells into a shared embedding in which cells group by cell type rather than dataset-specific conditions. Harmony simultaneously accounts for multiple experimental and biological factors. In six analyses, we demonstrate the superior performance of Harmony to previously published algorithms. We show that Harmony requires dramatically fewer computational resources. It is the only currently available algorithm that makes the integration of ~106 cells feasible on a personal computer. We apply Harmony to PBMCs from datasets with large experimental differences, 5 studies of pancreatic islet cells, mouse embryogenesis datasets, and cross-modality spatial integration.
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                Author and article information

                Contributors
                Role: Academic Editor
                Journal
                Cancers (Basel)
                Cancers (Basel)
                cancers
                Cancers
                MDPI
                2072-6694
                08 April 2021
                April 2021
                : 13
                : 8
                : 1776
                Affiliations
                [1 ]Department of Oncology, University of Oxford, Oxford OX3 7DQ, UK; shivan.sivakumar@ 123456oncology.ox.ac.uk (S.S.); alistair.easton@ 123456oncology.ox.ac.uk (A.E.); mark.middleton@ 123456oncology.ox.ac.uk (M.R.M.)
                [2 ]Kennedy Institute of Rheumatology, University of Oxford, Oxford OX3 7FY, UK; david.ahern@ 123456kennedy.ox.ac.uk (D.J.A.); ashwin.jainarayanan@ 123456exeter.ox.ac.uk (A.K.J.); Elke.kurz@ 123456kennedy.ox.ac.uk (E.K.)
                [3 ]Oncology, Oxford University Hospitals NHS Foundation Trust, Oxford OX3 9DU, UK
                [4 ]Sir William Dunn School of Pathology, University of Oxford, Oxford OX1 3RE, UK
                [5 ]University of Oxford Medical School, Oxford OX1 2JD, UK; edward.arbe-barnes@ 123456magd.ox.ac.uk
                [6 ]Interdisciplinary Bioscience Doctoral Training Program and Exeter College, University of Oxford, Oxford OX3 7DQ, UK
                [7 ]Nuffield Department of Surgical Sciences, University of Oxford, Oxford OX3 9DU, UK; n.mangal@ 123456imperial.ac.uk
                [8 ]Department of Surgery, Oxford University Hospitals NHS Foundation Trust, Oxford OX3 9DU, UK; srikanth.reddy@ 123456ouh.nhs.uk (S.R.); michael.silva@ 123456ouh.nhs.uk (M.S.); zahir.soonawalla@ 123456ouh.nhs.uk (Z.S.)
                [9 ]Department of Pathology, Oxford University Hospitals NHS Foundation Trust, Oxford OX3 9DU, UK; Aniko.Rendek@ 123456ouh.nhs.uk
                [10 ]Department of General, Gastrointestinal, Hepatobiliary and Transplant Surgery, RWTH Aachen University Hospital, 52074 Aachen, Germany; lheij@ 123456ukaachen.de
                [11 ]Institute of Pathology, University Hospital RWTH Aachen, 52074 Aachen, Germany
                [12 ]Wellcome Trust Centre for Human Genomics, University of Oxford, Oxford OX3 7BN, UK; rbr1@ 123456well.ox.ac.uk
                [13 ]Oxford NIHR Biomedical Research Centre, Oxford University Hospitals NHS Foundation Trust, Oxford OX3 9DU, UK
                Author notes
                [†]

                These authors contributed equally to the work.

                [‡]

                These authors jointly directed the work.

                Author information
                https://orcid.org/0000-0001-5033-8171
                https://orcid.org/0000-0002-7006-7396
                https://orcid.org/0000-0002-2687-6567
                https://orcid.org/0000-0001-8148-5406
                https://orcid.org/0000-0002-0602-3356
                https://orcid.org/0000-0002-6838-0711
                https://orcid.org/0000-0003-0167-1685
                https://orcid.org/0000-0003-4983-6389
                Article
                cancers-13-01776
                10.3390/cancers13081776
                8068251
                33917832
                4d3036da-b9e7-4968-b467-a53fdd743195
                © 2021 by the authors.

                Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ( https://creativecommons.org/licenses/by/4.0/).

                History
                : 27 February 2021
                : 29 March 2021
                Categories
                Article

                pancreatic cancer,immune checkpoints,tigit,cd39,icos,regulatory t-cells,senescent t-cells

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