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      The tumor immune microenvironment of primary and metastatic HER2− positive breast cancers utilizing gene expression and spatial proteomic profiling

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          Abstract

          Background

          The characterization of the immune component of the tumor microenvironment (TME) of human epidermal growth factor receptor 2 positive (HER2+) breast cancer has been limited. Molecular and spatial characterization of HER2+ TME of primary, recurrent, and metastatic breast tumors has the potential to identify immune mediated mechanisms and biomarker targets that could be used to guide selection of therapies.

          Methods

          We examined 15 specimens from eight patients with HER2+ breast cancer: 10 primary breast tumors (PBT), two soft tissue, one lung, and two brain metastases (BM). Using molecular profiling by bulk gene expression TME signatures, including the Tumor Inflammation Signature (TIS) and PAM50 subtyping, as well as spatial characterization of immune hot, warm, and cold regions in the stroma and tumor epithelium using 64 protein targets on the GeoMx Digital Spatial Profiler.

          Results

          PBT had higher infiltration of immune cells relative to metastatic sites and higher protein and gene expression of immune activation markers when compared to metastatic sites. TIS scores were lower in metastases, particularly in BM. BM also had less immune infiltration overall, but in the stromal compartment with the highest density of immune infiltration had similar levels of T cells that were less activated than PBT stromal regions suggesting immune exclusion in the tumor epithelium.

          Conclusions

          Our findings show stromal and tumor localized immune cells in the TME are more active in primary versus metastatic disease. This suggests patients with early HER2+ breast cancer could have more benefit from immune-targeting therapies than patients with advanced disease.

          Supplementary Information

          The online version contains supplementary material available at 10.1186/s12967-021-03113-9.

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          Most cited references31

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          Controlling the False Discovery Rate: A Practical and Powerful Approach to Multiple Testing

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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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              Atezolizumab and Nab-Paclitaxel in Advanced Triple-Negative Breast Cancer

              Unresectable locally advanced or metastatic triple-negative (hormone-receptor-negative and human epidermal growth factor receptor 2 [HER2]-negative) breast cancer is an aggressive disease with poor outcomes. Nanoparticle albumin-bound (nab)-paclitaxel may enhance the anticancer activity of atezolizumab.
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                Author and article information

                Contributors
                ischlam@tuftsmedicalcenter.org
                schurch@nanostring.com
                thether@nanostring.com
                Krysta.chaldekas@georgetown.edu
                bhudson@nanostring.com
                awhite@nanostring.com
                em1337@gerogetown.edu
                bth@georgetown.edu
                sms248@georgetown.edu
                Journal
                J Transl Med
                J Transl Med
                Journal of Translational Medicine
                BioMed Central (London )
                1479-5876
                27 November 2021
                27 November 2021
                2021
                : 19
                : 480
                Affiliations
                [1 ]GRID grid.415235.4, ISNI 0000 0000 8585 5745, Department of Hematology-Oncology, , MedStar Washington Hospital Center, ; Washington, DC USA
                [2 ]GRID grid.67033.31, ISNI 0000 0000 8934 4045, Present Address: Department of Hematology and Oncology, , Tufts Medical Center, ; 800 Washington St, 245, Boston, MA 02111 USA
                [3 ]GRID grid.510973.9, ISNI 0000 0004 5375 2863, NanoString Technologies Inc., ; Seattle, WA USA
                [4 ]GRID grid.411663.7, ISNI 0000 0000 8937 0972, MedStar Georgetown University Hospital, ; 4000 Reservoir road NW, 120 Building D, Washington, DC 20057 USA
                [5 ]GRID grid.213910.8, ISNI 0000 0001 1955 1644, Lombardi Comprehensive Cancer Center, ; Washington, DC USA
                [6 ]GRID grid.415232.3, ISNI 0000 0004 0391 7375, MedStar Health, ; Washington, DC USA
                Author information
                http://orcid.org/0000-0002-1320-3830
                Article
                3113
                10.1186/s12967-021-03113-9
                8626906
                34838031
                c7efe142-40c1-4bd2-80fc-edc0a69c509a
                © The Author(s) 2021

                Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

                History
                : 14 June 2021
                : 10 October 2021
                Funding
                Funded by: FundRef http://dx.doi.org/10.13039/100001006, breast cancer research foundation;
                Award ID: BCRF-20-156 2020-2021
                Award ID: BCRF-19-156 2019-2020
                Award Recipient :
                Funded by: FundRef http://dx.doi.org/10.13039/100000054, national cancer institute;
                Award ID: grant P30 CA051008
                Award Recipient :
                Categories
                Research
                Custom metadata
                © The Author(s) 2021

                Medicine
                breast cancer,her2 positive,tumor microenvironment,digital spatial profiling,gene expression profiling

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