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  • 1
    Publication Date: 2015-04-30
    Description: Whereas cancers grow within host tissues and evade host immunity through immune-editing and immunosuppression, tumours are rarely transmissible between individuals. Much like transplanted allogeneic organs, allogeneic tumours are reliably rejected by host T cells, even when the tumour and host share the same major histocompatibility complex alleles, the most potent determinants of transplant rejection. How such tumour-eradicating immunity is initiated remains unknown, although elucidating this process could provide the basis for inducing similar responses against naturally arising tumours. Here we find that allogeneic tumour rejection is initiated in mice by naturally occurring tumour-binding IgG antibodies, which enable dendritic cells (DCs) to internalize tumour antigens and subsequently activate tumour-reactive T cells. We exploited this mechanism to treat autologous and autochthonous tumours successfully. Either systemic administration of DCs loaded with allogeneic-IgG-coated tumour cells or intratumoral injection of allogeneic IgG in combination with DC stimuli induced potent T-cell-mediated antitumour immune responses, resulting in tumour eradication in mouse models of melanoma, pancreas, lung and breast cancer. Moreover, this strategy led to eradication of distant tumours and metastases, as well as the injected primary tumours. To assess the clinical relevance of these findings, we studied antibodies and cells from patients with lung cancer. T cells from these patients responded vigorously to autologous tumour antigens after culture with allogeneic-IgG-loaded DCs, recapitulating our findings in mice. These results reveal that tumour-binding allogeneic IgG can induce powerful antitumour immunity that can be exploited for cancer immunotherapy.〈br /〉〈span class="detail_caption"〉Notes: 〈/span〉Carmi, Yaron -- Spitzer, Matthew H -- Linde, Ian L -- Burt, Bryan M -- Prestwood, Tyler R -- Perlman, Nicola -- Davidson, Matthew G -- Kenkel, Justin A -- Segal, Ehud -- Pusapati, Ganesh V -- Bhattacharya, Nupur -- Engleman, Edgar G -- 5T32AI007290-27/AI/NIAID NIH HHS/ -- F31 CA189331/CA/NCI NIH HHS/ -- F31CA189331/CA/NCI NIH HHS/ -- P30 CA124435/CA/NCI NIH HHS/ -- T32 AI007290/AI/NIAID NIH HHS/ -- U01 CA141468/CA/NCI NIH HHS/ -- England -- Nature. 2015 May 7;521(7550):99-104. doi: 10.1038/nature14424. Epub 2015 Apr 29.〈br /〉〈span class="detail_caption"〉Author address: 〈/span〉School of Medicine, Department of Pathology, Stanford University, Palo Alto, California 94305, USA. ; 1] School of Medicine, Department of Pathology, Stanford University, Palo Alto, California 94305, USA [2] School of Medicine, Baxter Laboratory in Stem Cell Biology, Department of Microbiology and Immunology, Stanford University, Palo Alto, California 94305, USA. ; School of Medicine, Department of Cardiothoracic Surgery, Stanford University, Palo Alto, California 94305, USA. ; School of Medicine, Department of Biochemistry, Stanford University, Palo Alto, California 94305, USA.〈br /〉〈span class="detail_caption"〉Record origin:〈/span〉 〈a href="http://www.ncbi.nlm.nih.gov/pubmed/25924063" target="_blank"〉PubMed〈/a〉
    Print ISSN: 0028-0836
    Electronic ISSN: 1476-4687
    Topics: Biology , Chemistry and Pharmacology , Medicine , Natural Sciences in General , Physics
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  • 2
    Publication Date: 2014-10-25
    Description: Cellular circuits sense the environment, process signals, and compute decisions using networks of interacting proteins. To model such a system, the abundance of each activated protein species can be described as a stochastic function of the abundance of other proteins. High-dimensional single-cell technologies, such as mass cytometry, offer an opportunity to characterize signaling circuit-wide. However, the challenge of developing and applying computational approaches to interpret such complex data remains. Here, we developed computational methods, based on established statistical concepts, to characterize signaling network relationships by quantifying the strengths of network edges and deriving signaling response functions. In comparing signaling between naive and antigen-exposed CD4(+) T lymphocytes, we find that although these two cell subtypes had similarly wired networks, naive cells transmitted more information along a key signaling cascade than did antigen-exposed cells. We validated our characterization on mice lacking the extracellular-regulated mitogen-activated protein kinase (MAPK) ERK2, which showed stronger influence of pERK on pS6 (phosphorylated-ribosomal protein S6), in naive cells as compared with antigen-exposed cells, as predicted. We demonstrate that by using cell-to-cell variation inherent in single-cell data, we can derive response functions underlying molecular circuits and drive the understanding of how cells process signals.〈br /〉〈br /〉〈a href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4334155/" target="_blank"〉〈img src="https://static.pubmed.gov/portal/portal3rc.fcgi/4089621/img/3977009" border="0"〉〈/a〉   〈a href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4334155/" target="_blank"〉This paper as free author manuscript - peer-reviewed and accepted for publication〈/a〉〈br /〉〈br /〉〈span class="detail_caption"〉Notes: 〈/span〉Krishnaswamy, Smita -- Spitzer, Matthew H -- Mingueneau, Michael -- Bendall, Sean C -- Litvin, Oren -- Stone, Erica -- Pe'er, Dana -- Nolan, Garry P -- 1K01DK095008/DK/NIDDK NIH HHS/ -- 1R01CA130826/CA/NCI NIH HHS/ -- 1U54CA121852-01A1/CA/NCI NIH HHS/ -- CA 09-011/CA/NCI NIH HHS/ -- HHSN268201000034C/HV/NHLBI NIH HHS/ -- HHSN272200700038C/PHS HHS/ -- HV-10-05/HV/NHLBI NIH HHS/ -- K01 DK095008/DK/NIDDK NIH HHS/ -- P01 CA034233/CA/NCI NIH HHS/ -- R00 GM104148/GM/NIGMS NIH HHS/ -- R01 CA130826/CA/NCI NIH HHS/ -- S10RR027582-01/RR/NCRR NIH HHS/ -- U19 AI057229/AI/NIAID NIH HHS/ -- U19 AI100627/AI/NIAID NIH HHS/ -- U54 CA149145/CA/NCI NIH HHS/ -- New York, N.Y. -- Science. 2014 Nov 28;346(6213):1250689. doi: 10.1126/science.1250689. Epub 2014 Oct 23.〈br /〉〈span class="detail_caption"〉Author address: 〈/span〉Department of Biological Sciences, Department of Systems Biology, Columbia University, New York, NY, USA. ; Baxter Laboratory in Stem Cell Biology, Department of Microbiology and Immunology, Stanford University, Stanford, CA, USA. ; Division of Immunology, Department of Microbiology and Immunobiology, Harvard Medical School, Boston, MA, USA. ; Molecular Biology Section, Division of Biological Sciences, Department of Cellular and Molecular Medicine, University of California San Diego, La Jolla, CA, USA. ; Department of Biological Sciences, Department of Systems Biology, Columbia University, New York, NY, USA. dpeer@biology.columbia.edu.〈br /〉〈span class="detail_caption"〉Record origin:〈/span〉 〈a href="http://www.ncbi.nlm.nih.gov/pubmed/25342659" target="_blank"〉PubMed〈/a〉
    Keywords: Animals ; CD4-Positive T-Lymphocytes/*immunology ; Computer Simulation ; Image Cytometry ; Male ; Mice ; Mice, Mutant Strains ; Mitogen-Activated Protein Kinase 1/genetics ; Receptors, Antigen, T-Cell/*metabolism ; Ribosomal Protein S6/metabolism ; Signal Transduction ; Single-Cell Analysis/*methods ; Systems Biology/*methods ; eIF-2 Kinase/metabolism
    Print ISSN: 0036-8075
    Electronic ISSN: 1095-9203
    Topics: Biology , Chemistry and Pharmacology , Computer Science , Medicine , Natural Sciences in General , Physics
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  • 3
    Publication Date: 2015-07-15
    Description: Immune cells function in an interacting hierarchy that coordinates the activities of various cell types according to genetic and environmental contexts. We developed graphical approaches to construct an extensible immune reference map from mass cytometry data of cells from different organs, incorporating landmark cell populations as flags on the map to compare cells from distinct samples. The maps recapitulated canonical cellular phenotypes and revealed reproducible, tissue-specific deviations. The approach revealed influences of genetic variation and circadian rhythms on immune system structure, enabled direct comparisons of murine and human blood cell phenotypes, and even enabled archival fluorescence-based flow cytometry data to be mapped onto the reference framework. This foundational reference map provides a working definition of systemic immune organization to which new data can be integrated to reveal deviations driven by genetics, environment, or pathology.〈br /〉〈br /〉〈a href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4537647/" target="_blank"〉〈img src="https://static.pubmed.gov/portal/portal3rc.fcgi/4089621/img/3977009" border="0"〉〈/a〉   〈a href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4537647/" target="_blank"〉This paper as free author manuscript - peer-reviewed and accepted for publication〈/a〉〈br /〉〈br /〉〈span class="detail_caption"〉Notes: 〈/span〉Spitzer, Matthew H -- Gherardini, Pier Federico -- Fragiadakis, Gabriela K -- Bhattacharya, Nupur -- Yuan, Robert T -- Hotson, Andrew N -- Finck, Rachel -- Carmi, Yaron -- Zunder, Eli R -- Fantl, Wendy J -- Bendall, Sean C -- Engleman, Edgar G -- Nolan, Garry P -- 1R01CA130826/CA/NCI NIH HHS/ -- 1R01GM109836/GM/NIGMS NIH HHS/ -- 1R01NS089533/NS/NINDS NIH HHS/ -- 1U19AI100627/AI/NIAID NIH HHS/ -- 201303028/PHS HHS/ -- 5-24927/PHS HHS/ -- 5R01AI073724/AI/NIAID NIH HHS/ -- 5U54CA143907/CA/NCI NIH HHS/ -- 7500108142/PHS HHS/ -- F31 CA189331/CA/NCI NIH HHS/ -- F31CA189331/CA/NCI NIH HHS/ -- F32 GM093508/GM/NIGMS NIH HHS/ -- F32 GM093508-01/GM/NIGMS NIH HHS/ -- HHSF223201210194C/PHS HHS/ -- HHSN268201000034C/HV/NHLBI NIH HHS/ -- HHSN272200700038C/AI/NIAID NIH HHS/ -- HHSN272200700038C/PHS HHS/ -- HHSN272201200028C/PHS HHS/ -- K99 GM104148/GM/NIGMS NIH HHS/ -- K99GM104148-01/GM/NIGMS NIH HHS/ -- N01-HV-00242/HV/NHLBI NIH HHS/ -- P01 CA034233/CA/NCI NIH HHS/ -- P01 CA034233-22A1/CA/NCI NIH HHS/ -- PN2 EY018228/EY/NEI NIH HHS/ -- PN2EY018228 0158 G KB065/EY/NEI NIH HHS/ -- R01 AI073724/AI/NIAID NIH HHS/ -- R01 CA130826/CA/NCI NIH HHS/ -- R01 CA184968/CA/NCI NIH HHS/ -- R01 GM109836/GM/NIGMS NIH HHS/ -- R01 NS089533/NS/NINDS NIH HHS/ -- R01CA184968/CA/NCI NIH HHS/ -- R33 CA183654/CA/NCI NIH HHS/ -- R33 CA183692/CA/NCI NIH HHS/ -- RFA CA 09-009/CA/NCI NIH HHS/ -- RFA CA 09-011/CA/NCI NIH HHS/ -- T32 GM007276/GM/NIGMS NIH HHS/ -- T32GM007276/GM/NIGMS NIH HHS/ -- U19 AI057229/AI/NIAID NIH HHS/ -- U19 AI100627/AI/NIAID NIH HHS/ -- U54 CA149145/CA/NCI NIH HHS/ -- U54CA149145/CA/NCI NIH HHS/ -- Howard Hughes Medical Institute/ -- New York, N.Y. -- Science. 2015 Jul 10;349(6244):1259425. doi: 10.1126/science.1259425.〈br /〉〈span class="detail_caption"〉Author address: 〈/span〉Baxter Laboratory in Stem Cell Biology, Department of Microbiology and Immunology, Stanford University, Stanford, CA 94305, USA. Department of Pathology, Stanford University, Stanford, CA 94305, USA. Program in Immunology, Stanford University, Stanford, CA 94305, USA. gnolan@stanford.edu matthew.spitzer@stanford.edu. ; Baxter Laboratory in Stem Cell Biology, Department of Microbiology and Immunology, Stanford University, Stanford, CA 94305, USA. ; Department of Pathology, Stanford University, Stanford, CA 94305, USA. ; Department of Pathology, Stanford University, Stanford, CA 94305, USA. Program in Immunology, Stanford University, Stanford, CA 94305, USA. ; Department of Obstetrics and Gynecology, Division of Gynecologic Oncology, Stanford University, Stanford, CA 94305, USA. ; Baxter Laboratory in Stem Cell Biology, Department of Microbiology and Immunology, Stanford University, Stanford, CA 94305, USA. Program in Immunology, Stanford University, Stanford, CA 94305, USA. gnolan@stanford.edu matthew.spitzer@stanford.edu.〈br /〉〈span class="detail_caption"〉Record origin:〈/span〉 〈a href="http://www.ncbi.nlm.nih.gov/pubmed/26160952" target="_blank"〉PubMed〈/a〉
    Keywords: Animals ; Bone Marrow/immunology ; Circadian Rhythm/immunology ; Flow Cytometry ; Genetic Variation ; Humans ; Immune System/*cytology/*immunology ; Mice ; Mice, Inbred C57BL ; Models, Biological ; Phenotype ; Reference Standards
    Print ISSN: 0036-8075
    Electronic ISSN: 1095-9203
    Topics: Biology , Chemistry and Pharmacology , Computer Science , Medicine , Natural Sciences in General , Physics
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