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      TGF-β attenuates tumour response to PD-L1 blockade by contributing to exclusion of T cells

      1 , 1 , 1 , 1 , 1 , 1 , 1 , 1 , 1 , 1 , 1 , 1 , 1 , 1 , 1 , 1 , 1 , 1 , 1 , 1 , 1 , 1 , 1 , 1 , 1 , 1 , 2 , 2 , 3 , 3 , 4 , 5 , 6 , 7 , 1 , 1 , 1 , 1 , 1 , 1 , 1 , 8

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          Therapeutic antibodies that block the programmed death-ligand 1 (PD-L1)/programmed death-1 (PD-1) pathway can induce robust and durable responses in patients with various cancers, including metastatic urothelial cancer (mUC) 15 . However, these responses only occur in a subset of patients. Elucidating the determinants of response and resistance is key to improving outcomes and developing new treatment strategies. Here, we examined tumours from a large cohort of mUC patients treated with an anti–PD-L1 agent (atezolizumab) and identified major determinants of clinical outcome. Response was associated with CD8+ T-effector cell phenotype and, to an even greater extent, high neoantigen or tumour mutation burden (TMB). Lack of response was associated with a signature of transforming growth factor β (TGF-β) signalling in fibroblasts, particularly in patients with CD8+ T cells that were excluded from the tumour parenchyma and instead found in the fibroblast- and collagen-rich peritumoural stroma—a common phenotype among patients with mUC. Using a mouse model that recapitulates this immune excluded phenotype, we found that therapeutic administration of a TGF-β blocking antibody together with anti–PD-L1 reduced TGF-β signalling in stromal cells, facilitated T cell penetration into the centre of the tumour, and provoked vigorous anti-tumour immunity and tumour regression. Integration of these three independent biological features provides the best basis for understanding outcome in this setting and suggests that TGF-β shapes the tumour microenvironment to restrain anti-tumour immunity by restricting T cell infiltration.

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          The Where, the When, and the How of Immune Monitoring for Cancer Immunotherapies in the Era of Checkpoint Inhibition.

          Clinical trials with immune checkpoint inhibitors have provided important insights into the mode of action of anticancer immune therapies and potential mechanisms of immune escape. Development of the next wave of rational clinical combination strategies will require a deep understanding of the mechanisms by which combination partners influence the battle between the immune system's capabilities to fight cancer and the immune-suppressive processes that promote tumor growth. This review focuses on our current understanding of tumor and circulating pharmacodynamic correlates of immune modulation and elaborates on lessons learned from human translational research with checkpoint inhibitors. Actionable tumor markers of immune activation including CD8(+)T cells, PD-L1 IHC as a pharmacodynamic marker of T-cell function, T-cell clonality, and challenges with conduct of trials that ask scientific questions from serial biopsies are addressed. Proposals for clinical trial design, as well as future applications of peripheral pharmacodynamic endpoints as potential surrogates of early clinical activity, are discussed. On the basis of emerging mechanisms of response and immune escape, we propose the concept of the tumor immunity continuum as a framework for developing rational combination strategies.
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            Is Open Access

            INTERFEROME v2.0: an updated database of annotated interferon-regulated genes

            Interferome v2.0 (http://interferome.its.monash.edu.au/interferome/) is an update of an earlier version of the Interferome DB published in the 2009 NAR database edition. Vastly improved computational infrastructure now enables more complex and faster queries, and supports more data sets from types I, II and III interferon (IFN)-treated cells, mice or humans. Quantitative, MIAME compliant data are collected, subjected to thorough, standardized, quantitative and statistical analyses and then significant changes in gene expression are uploaded. Comprehensive manual collection of metadata in v2.0 allows flexible, detailed search capacity including the parameters: range of -fold change, IFN type, concentration and time, and cell/tissue type. There is no limit to the number of genes that can be used to search the database in a single query. Secondary analysis such as gene ontology, regulatory factors, chromosomal location or tissue expression plots of IFN-regulated genes (IRGs) can be performed in Interferome v2.0, or data can be downloaded in convenient text formats compatible with common secondary analysis programs. Given the importance of IFN to innate immune responses in infectious, inflammatory diseases and cancer, this upgrade of the Interferome to version 2.0 will facilitate the identification of gene signatures of importance in the pathogenesis of these diseases.
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              Intrinsic subtypes of high-grade bladder cancer reflect the hallmarks of breast cancer biology.

              We sought to define whether there are intrinsic molecular subtypes of high-grade bladder cancer. Consensus clustering performed on gene expression data from a meta-dataset of high-grade, muscle-invasive bladder tumors identified two intrinsic, molecular subsets of high-grade bladder cancer, termed "luminal" and "basal-like," which have characteristics of different stages of urothelial differentiation, reflect the luminal and basal-like molecular subtypes of breast cancer, and have clinically meaningful differences in outcome. A gene set predictor, bladder cancer analysis of subtypes by gene expression (BASE47) was defined by prediction analysis of microarrays (PAM) and accurately classifies the subtypes. Our data demonstrate that there are at least two molecularly and clinically distinct subtypes of high-grade bladder cancer and validate the BASE47 as a subtype predictor. Future studies exploring the predictive value of the BASE47 subtypes for standard of care bladder cancer therapies, as well as novel subtype-specific therapies, will be of interest.

                Author and article information

                19 January 2018
                14 February 2018
                22 February 2018
                14 August 2018
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                [1 ]Genentech, South San Francisco, California 94080, USA
                [2 ]Division of Oncology and Pathology, Department of Clinical Sciences Lund, Lund University, Lund, Skåne, SE-223 81, Sweden
                [3 ]Fios Genomics, Edinburgh, Scotland EH16 4UX, UK
                [4 ]Netherlands Cancer Institute, 1066 CX Amsterdam, The Netherlands
                [5 ]Department of Cancer Medicine, Institut Gustave Roussy, University of Paris Sud, 94800 Villejuif, France
                [6 ]Genitourinary Oncology Service, Department of Medicine, Memorial Sloan Kettering Cancer Center, New York, New York 10065, USA
                [7 ]University of California San Francisco, Helen Diller Family Comprehensive Cancer Center, San Francisco, California 94158, USA
                [8 ]Barts Experimental Cancer Medicine Centre, Barts Cancer Institute, Queen Mary University of London, London EC1M 6BQ, UK
                Author notes
                [^ ]Co-corresponding authors: Sanjeev Mariathasan, mariathasan.sanjeev@ 123456gene.com ; Shannon Turley, turley.shannon@ 123456gene.com ; Richard Bourgon, bourgon.richard@ 123456gene.com

                These authors contributed equally to this work.


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