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      Immune checkpoint blockade reprograms systemic immune landscape and tumor microenvironment in obesity-associated breast cancer

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          SUMMARY

          Immune checkpoint blockade (ICB) has improved outcomes in some cancers. A major limitation of ICB is that most patients fail to respond, which is partly attributable to immunosuppression. Obesity appears to improve immune checkpoint therapies in some cancers, but impacts on breast cancer (BC) remain unknown. In lean and obese mice, tumor progression and immune reprogramming were quantified in BC tumors treated with anti-programmed death-1 (PD-1) or control. Obesity augments tumor incidence and progression. Anti-PD-1 induces regression in lean mice and potently abrogates progression in obese mice. BC primes systemic immunity to be highly responsive to obesity, leading to greater immunosuppression, which may explain greater anti-PD-1 efficacy. Anti-PD-1 significantly reinvigorates antitumor immunity despite persistent obesity. Laminin subunit beta-2 ( Lamb2), downregulated by anti-PD-1, significantly predicts patient survival. Lastly, a microbial signature associated with anti-PD-1 efficacy is identified. Thus, anti-PD-1 is highly efficacious in obese mice by reinvigorating durable antitumor immunity.

          In brief

          Pingili et al. show that breast cancer exacerbates obesity-driven immunosuppression. Anti-PD-1 reinvigorates antitumor immunity in the tumor microenvironment, mammary fat pad, and peripherally. Lamb2, downregulated by anti-PD-1 in tumors, associates with obesity and poor survival in patients. A microbial signature associated with immune checkpoint inhibitor efficacy is identified.

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          Most cited references108

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          Hallmarks of Cancer: The Next Generation

          The hallmarks of cancer comprise six biological capabilities acquired during the multistep development of human tumors. The hallmarks constitute an organizing principle for rationalizing the complexities of neoplastic disease. They include sustaining proliferative signaling, evading growth suppressors, resisting cell death, enabling replicative immortality, inducing angiogenesis, and activating invasion and metastasis. Underlying these hallmarks are genome instability, which generates the genetic diversity that expedites their acquisition, and inflammation, which fosters multiple hallmark functions. Conceptual progress in the last decade has added two emerging hallmarks of potential generality to this list-reprogramming of energy metabolism and evading immune destruction. In addition to cancer cells, tumors exhibit another dimension of complexity: they contain a repertoire of recruited, ostensibly normal cells that contribute to the acquisition of hallmark traits by creating the "tumor microenvironment." Recognition of the widespread applicability of these concepts will increasingly affect the development of new means to treat human cancer. Copyright © 2011 Elsevier Inc. All rights reserved.
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            limma powers differential expression analyses for RNA-sequencing and microarray studies

            limma is an R/Bioconductor software package that provides an integrated solution for analysing data from gene expression experiments. It contains rich features for handling complex experimental designs and for information borrowing to overcome the problem of small sample sizes. Over the past decade, limma has been a popular choice for gene discovery through differential expression analyses of microarray and high-throughput PCR data. The package contains particularly strong facilities for reading, normalizing and exploring such data. Recently, the capabilities of limma have been significantly expanded in two important directions. First, the package can now perform both differential expression and differential splicing analyses of RNA sequencing (RNA-seq) data. All the downstream analysis tools previously restricted to microarray data are now available for RNA-seq as well. These capabilities allow users to analyse both RNA-seq and microarray data with very similar pipelines. Second, the package is now able to go past the traditional gene-wise expression analyses in a variety of ways, analysing expression profiles in terms of co-regulated sets of genes or in terms of higher-order expression signatures. This provides enhanced possibilities for biological interpretation of gene expression differences. This article reviews the philosophy and design of the limma package, summarizing both new and historical features, with an emphasis on recent enhancements and features that have not been previously described.
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              edgeR: a Bioconductor package for differential expression analysis of digital gene expression data

              Summary: It is expected that emerging digital gene expression (DGE) technologies will overtake microarray technologies in the near future for many functional genomics applications. One of the fundamental data analysis tasks, especially for gene expression studies, involves determining whether there is evidence that counts for a transcript or exon are significantly different across experimental conditions. edgeR is a Bioconductor software package for examining differential expression of replicated count data. An overdispersed Poisson model is used to account for both biological and technical variability. Empirical Bayes methods are used to moderate the degree of overdispersion across transcripts, improving the reliability of inference. The methodology can be used even with the most minimal levels of replication, provided at least one phenotype or experimental condition is replicated. The software may have other applications beyond sequencing data, such as proteome peptide count data. Availability: The package is freely available under the LGPL licence from the Bioconductor web site (http://bioconductor.org). Contact: mrobinson@wehi.edu.au
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                Author and article information

                Journal
                101573691
                39703
                Cell Rep
                Cell Rep
                Cell reports
                2211-1247
                26 June 2021
                22 June 2021
                08 November 2021
                : 35
                : 12
                : 109285
                Affiliations
                [1 ]Department of Medicine, Division of Hematology and Oncology, Department of Medicine, College of Medicine, The University of Tennessee Health Science Center, Memphis, TN 38163, USA
                [2 ]Department of Pharmaceutical Sciences, College of Pharmacy, The University of Tennessee Health Science Center, Memphis, TN 38163, USA
                [3 ]Department of Pediatrics, Department of Medicine, College of Medicine, The University of Tennessee Health Science Center, Memphis, TN 38163, USA
                [4 ]Department of Microbiology, Immunology, and Biochemistry, College of Medicine, The University of Tennessee Health Science Center, Memphis, TN 38163, USA
                [5 ]Office of Vice Chancellor for Research, The University of Tennessee Health Science Center, Memphis, TN 38163, USA
                [6 ]Department of Pathology, The University of Tennessee Health Science Center, Memphis, TN 38163, USA
                [7 ]Department of Epidemiology, Gillings School of Global Public Health, University of North Carolina, Chapel Hill, NC 27599, USA
                [8 ]Department of Pathology and Laboratory Medicine, School of Medicine, University of North Carolina, Chapel Hill, NC 27599, USA
                [9 ]Department of Surgery, Comprehensive Cancer Center, Wake Forest University School of Medicine, Winston Salem, NC 27157, USA
                [10 ]UTHSC Center for Cancer Research, College of Medicine, The University of Tennessee Health Science Center, Memphis, TN 38163, USA
                [11 ]These authors contributed equally
                [12 ]Present address: Montefiore Medical Center. University Hospital for Albert Einstein College of Medicine, The Bronx, NY 10461, USA
                [13 ]Lead contact
                Author notes

                AUTHOR CONTRIBUTIONS

                Conceptualization, A.K.P., M.C., L.M.S., and L.M.; methodology, A.K.P., M.C., L.M.S., T.N.M., D.D., J.F.P., and L.M.; software, H.J., H.Y.C., D.N.H., and J.F.P.; validation, A.K.P., J.F.P., D.N.H., R.N., and L.M.; formal analysis, H.J., H.Y.C., A.M.H., and L.M.; investigation, A.K.P., M.C., J.R.Y., S.A., D.D., R.S., A.M.H., H.J., and H.Y.C.; resources, D.N.H., J.F.P., R.N., and L.M.; data curation, H.J., M.A.T., and L.M.; writing – original draft, A.K.P., J.F.P., and L.M.; writing – review & editing, M.C., L.M.S., J.F.P., K.L.C., and L.M.; visualization, A.K.P., J.F.P., and L.M.; supervision, J.F.P., D.N.H., and L.M.; project administration, L.M.; funding acquisition, J.F.P., D.N.H., and L.M.

                [* ]Correspondence: jpierre1@ 123456uthsc.edu (J.F.P.), liza.makowski@ 123456uthsc.edu (L.M.)
                Article
                NIHMS1717855
                10.1016/j.celrep.2021.109285
                8574993
                34161764
                9d3c056b-1957-4e89-937f-e7a524a55112

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

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                Cell biology
                Cell biology

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