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      Human Tumor-Associated Macrophage and Monocyte Transcriptional Landscapes Reveal Cancer-Specific Reprogramming, Biomarkers, and Therapeutic Targets

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          Summary

          The roles of tumor-associated macrophages (TAMs) and circulating monocytes in human cancer are poorly understood. Here, we show that monocyte subpopulation distribution and transcriptomes are significantly altered by the presence of endometrial and breast cancer. Furthermore, TAMs from endometrial and breast cancers are transcriptionally distinct from monocytes and their respective tissue-resident macrophages. We identified a breast TAM signature that is highly enriched in aggressive breast cancer subtypes and associated with shorter disease-specific survival. We also identified an auto-regulatory loop between TAMs and cancer cells driven by tumor necrosis factor alpha involving SIGLEC1 and CCL8, which is self-reinforcing through the production of CSF1. Together these data provide direct evidence that monocyte and macrophage transcriptional landscapes are perturbed by cancer, reflecting patient outcomes.

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          Highlights

          • Cancer alters circulating monocytic populations and their transcriptomes

          • Tumor-associated macrophages show tissue-specific programming

          • Tumor-associated macrophages’ gene signature correlates with poor clinical outcome

          • Tumor-associated macrophages enhance cancer cells malignancy through CCL8

          Abstract

          Cassetta et al. identify a breast cancer tumor-associated macrophage (TAM) transcriptome that is different from those of monocytes and tissue-resident macrophages, and which is associated with shorter disease-specific survival, and they demonstrate crosstalk between tumor cells and TAMs via SIGLEC1, CCL8, and CSF1.

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          Most cited references22

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          Pan-cancer Immunogenomic Analyses Reveal Genotype-Immunophenotype Relationships and Predictors of Response to Checkpoint Blockade.

          The Cancer Genome Atlas revealed the genomic landscapes of human cancers. In parallel, immunotherapy is transforming the treatment of advanced cancers. Unfortunately, the majority of patients do not respond to immunotherapy, making the identification of predictive markers and the mechanisms of resistance an area of intense research. To increase our understanding of tumor-immune cell interactions, we characterized the intratumoral immune landscapes and the cancer antigenomes from 20 solid cancers and created The Cancer Immunome Atlas (https://tcia.at/). Cellular characterization of the immune infiltrates showed that tumor genotypes determine immunophenotypes and tumor escape mechanisms. Using machine learning, we identified determinants of tumor immunogenicity and developed a scoring scheme for the quantification termed immunophenoscore. The immunophenoscore was a superior predictor of response to anti-cytotoxic T lymphocyte antigen-4 (CTLA-4) and anti-programmed cell death protein 1 (anti-PD-1) antibodies in two independent validation cohorts. Our findings and this resource may help inform cancer immunotherapy and facilitate the development of precision immuno-oncology.
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            Improving RNA-Seq expression estimates by correcting for fragment bias

            The biochemistry of RNA-Seq library preparation results in cDNA fragments that are not uniformly distributed within the transcripts they represent. This non-uniformity must be accounted for when estimating expression levels, and we show how to perform the needed corrections using a likelihood based approach. We find improvements in expression estimates as measured by correlation with independently performed qRT-PCR and show that correction of bias leads to improved replicability of results across libraries and sequencing technologies.
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              Stromal gene expression predicts clinical outcome in breast cancer.

              Although it is increasingly evident that cancer is influenced by signals emanating from tumor stroma, little is known regarding how changes in stromal gene expression affect epithelial tumor progression. We used laser capture microdissection to compare gene expression profiles of tumor stroma from 53 primary breast tumors and derived signatures strongly associated with clinical outcome. We present a new stroma-derived prognostic predictor (SDPP) that stratifies disease outcome independently of standard clinical prognostic factors and published expression-based predictors. The SDPP predicts outcome in several published whole tumor-derived expression data sets, identifies poor-outcome individuals from multiple clinical subtypes, including lymph node-negative tumors, and shows increased accuracy with respect to previously published predictors, especially for HER2-positive tumors. Prognostic power increases substantially when the predictor is combined with existing outcome predictors. Genes represented in the SDPP reveal the strong prognostic capacity of differential immune responses as well as angiogenic and hypoxic responses, highlighting the importance of stromal biology in tumor progression.
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                Author and article information

                Contributors
                Journal
                Cancer Cell
                Cancer Cell
                Cancer Cell
                Cell Press
                1535-6108
                1878-3686
                15 April 2019
                15 April 2019
                : 35
                : 4
                : 588-602.e10
                Affiliations
                [1 ]MRC Centre for Reproductive Health, Queen's Medical Research Institute, The University of Edinburgh, Edinburgh EH16 4TJ, UK
                [2 ]Applied Bioinformatics of Cancer, University of Edinburgh Cancer Research Centre, Institute of Genetics and Molecular Medicine, Edinburgh EH4 2XR, UK
                [3 ]Department of Cell, Developmental & Cancer Biology, and Knight Cancer Institute, Oregon Health & Science University, Portland 97239, USA
                [4 ]MRC Centre for Regenerative Medicine, University of Edinburgh, Edinburgh EH16 4UU, UK
                [5 ]Department of Developmental and Molecular Biology, Albert Einstein College of Medicine, New York 10461, USA
                [6 ]Edinburgh Breast Unit and Breast Cancer Now Research Unit, University of Edinburgh, Edinburgh EH4 2XU, UK
                [7 ]Department of Surgery, Montefiore Medical College, New York 10467, USA
                [8 ]Department of Obstetrics and Gynecology, Albert Einstein College of Medicine and Montefiore Medical Center, New York 10461, USA
                [9 ]Aquila Biomedical, Edinburgh Bioquarter, Little France Road, Edinburgh EH16 4TJ, UK
                [10 ]Department of Molecular and Medical Genetics and Knight Cancer Institute, Oregon Health & Science University, Portland 97239, USA
                [11 ]Department of Surgery, Duke University Medical Center, Durham, NC 27710, USA
                Author notes
                []Corresponding author jeff.pollard@ 123456ed.ac.uk
                [12]

                These authors contributed equally

                [13]

                Lead Contact

                Article
                S1535-6108(19)30104-7
                10.1016/j.ccell.2019.02.009
                6472943
                30930117
                36b53f14-129d-4371-a174-05819f610589
                © 2019 The Authors

                This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).

                History
                : 2 October 2017
                : 16 November 2018
                : 25 February 2019
                Categories
                Article

                Oncology & Radiotherapy
                breast cancer,endometrial cancer,tumor microenvironment,human macrophages,human circulating monocytes,ccl8,siglec1

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