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Konecny AJ, Mage P, Tyznik AJ, Prlic M, Mair F. 50-color phenotyping of the human immune system with in-depth assessment of T cells and dendritic cells. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.12.14.571745. [PMID: 38168221 PMCID: PMC10760076 DOI: 10.1101/2023.12.14.571745] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/05/2024]
Abstract
We report the development of an optimized 50-color spectral flow cytometry panel designed for the in-depth analysis of the immune system in human blood and tissues, with the goal of maximizing the amount of information that can be collected using currently available flow cytometry platforms. We established and tested this panel using peripheral blood mononuclear cells (PBMCs), but included CD45 to enable its use for the analysis of human tissue samples. The panel contains lineage markers for all major immune cell subsets, and an extensive set of phenotyping markers focused on the activation and differentiation status of the T cell and dendritic cell (DC) compartment. We outline the biological insight that can be gained from the simultaneous measurement of such a large number of proteins and propose that this approach provides a unique opportunity for the comprehensive exploration of the immune status in tissue biopsies and other human samples with a limited number of cells. Of note, we tested the panel to be compatible with cell sorting for further downstream applications. Furthermore, to facilitate the wide-spread implementation of such a panel across different cohorts and samples, we established a trimmed-down 45-color version which can be used with different spectral cytometry platforms. Finally, to generate this panel, we utilized not only existing panel design guidelines, but also developed new metrics to systematically identify the optimal combination of 50 fluorochromes and evaluate fluorochrome-specific resolution in the context of a 50-color unmixing matrix.
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Affiliation(s)
- Andrew J. Konecny
- Vaccine and Infectious Disease Division, Fred Hutchinson Cancer Center, Seattle WA, 98107, USA
- Department of Immunology, University of Washington, Seattle, WA 98195, USA
| | - Peter Mage
- Advanced Technology Group, BD Biosciences, San Jose, CA 95131, USA
| | - Aaron J. Tyznik
- Applied Research & Technology, Medical and Scientific Affairs, BD Biosciences, San Diego, CA 92037, USA
| | - Martin Prlic
- Vaccine and Infectious Disease Division, Fred Hutchinson Cancer Center, Seattle WA, 98107, USA
- Department of Immunology, University of Washington, Seattle, WA 98195, USA
| | - Florian Mair
- Vaccine and Infectious Disease Division, Fred Hutchinson Cancer Center, Seattle WA, 98107, USA
- Flow Cytometry Core Facility, Institute of Molecular Health Sciences, ETH Zurich, 8093 Zurich, Switzerland
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Spurgeon BEJ, Frelinger AL. OMIP-097: High-parameter phenotyping of human platelets by spectral flow cytometry. Cytometry A 2023; 103:935-940. [PMID: 37786346 DOI: 10.1002/cyto.a.24797] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/18/2023] [Revised: 08/24/2023] [Accepted: 09/12/2023] [Indexed: 10/04/2023]
Abstract
Using spectral flow cytometry, we developed a 16-color panel for analysis of platelet phenotype and function in human whole blood. The panel contains markers of clinical relevance and follows an optimized protocol for the high-parameter phenotyping of (phosphatidylserine positive) procoagulant platelets. Inclusion of established markers, such as CD62P and PAC-1, allows the subsetting of classic (proinflammatory and proaggregatory) phenotypes, while addition of novel markers, such as TLR9, allows the resolution of platelets with nonclassic functions. Multiple inducible (C3b, CD63, CD107a, CD154, and TLT-1) and constitutive (CD29, CD31, CD32, CD36, CD42a, CD61, and GPVI) markers are also measurable, and we demonstrate the use of automatic gating for platelet analysis. The panel is widely applicable to research and clinical settings and can be readily modified, should users wish to tailor the panel to more specific needs.
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Affiliation(s)
- Benjamin E J Spurgeon
- Center for Platelet Research Studies, Dana-Farber/Boston Children's Cancer and Blood Disorders Center, Harvard Medical School, Boston, Massachusetts, USA
| | - Andrew L Frelinger
- Center for Platelet Research Studies, Dana-Farber/Boston Children's Cancer and Blood Disorders Center, Harvard Medical School, Boston, Massachusetts, USA
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3
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Robles EE, Jin Y, Smyth P, Scheuermann RH, Bui JD, Wang HY, Oak J, Qian Y. A cell-level discriminative neural network model for diagnosis of blood cancers. Bioinformatics 2023; 39:btad585. [PMID: 37756695 PMCID: PMC10563151 DOI: 10.1093/bioinformatics/btad585] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/06/2023] [Revised: 09/12/2023] [Accepted: 09/22/2023] [Indexed: 09/29/2023] Open
Abstract
MOTIVATION Precise identification of cancer cells in patient samples is essential for accurate diagnosis and clinical monitoring but has been a significant challenge in machine learning approaches for cancer precision medicine. In most scenarios, training data are only available with disease annotation at the subject or sample level. Traditional approaches separate the classification process into multiple steps that are optimized independently. Recent methods either focus on predicting sample-level diagnosis without identifying individual pathologic cells or are less effective for identifying heterogeneous cancer cell phenotypes. RESULTS We developed a generalized end-to-end differentiable model, the Cell Scoring Neural Network (CSNN), which takes sample-level training data and predicts the diagnosis of the testing samples and the identity of the diagnostic cells in the sample, simultaneously. The cell-level density differences between samples are linked to the sample diagnosis, which allows the probabilities of individual cells being diagnostic to be calculated using backpropagation. We applied CSNN to two independent clinical flow cytometry datasets for leukemia diagnosis. In both qualitative and quantitative assessments, CSNN outperformed preexisting neural network modeling approaches for both cancer diagnosis and cell-level classification. Post hoc decision trees and 2D dot plots were generated for interpretation of the identified cancer cells, showing that the identified cell phenotypes match the cancer endotypes observed clinically in patient cohorts. Independent data clustering analysis confirmed the identified cancer cell populations. AVAILABILITY AND IMPLEMENTATION The source code of CSNN and datasets used in the experiments are publicly available on GitHub (http://github.com/erobl/csnn). Raw FCS files can be downloaded from FlowRepository (ID: FR-FCM-Z6YK).
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Affiliation(s)
- Edgar E Robles
- Department of Computer Science, University of California, Irvine, CA 92697, United States
| | - Ye Jin
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA 02115, United States
| | - Padhraic Smyth
- Department of Computer Science, University of California, Irvine, CA 92697, United States
| | - Richard H Scheuermann
- Department of Informatics, J. Craig Venter Institute, La Jolla, CA 92037, United States
- Department of Pathology, University of California, San Diego, CA 92093, United States
- Center for Infectious Disease and Vaccine Research, La Jolla Institute for Immunology, La Jolla, CA 92037, United States
| | - Jack D Bui
- Department of Pathology, University of California, San Diego, CA 92093, United States
| | - Huan-You Wang
- Department of Pathology, University of California, San Diego, CA 92093, United States
| | - Jean Oak
- Department of Pathology, Stanford University, Stanford, CA 94305, United States
| | - Yu Qian
- Department of Informatics, J. Craig Venter Institute, La Jolla, CA 92037, United States
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4
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Robinson JP, Ostafe R, Iyengar SN, Rajwa B, Fischer R. Flow Cytometry: The Next Revolution. Cells 2023; 12:1875. [PMID: 37508539 PMCID: PMC10378642 DOI: 10.3390/cells12141875] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/21/2023] [Revised: 07/06/2023] [Accepted: 07/13/2023] [Indexed: 07/30/2023] Open
Abstract
Unmasking the subtleties of the immune system requires both a comprehensive knowledge base and the ability to interrogate that system with intimate sensitivity. That task, to a considerable extent, has been handled by an iterative expansion in flow cytometry methods, both in technological capability and also in accompanying advances in informatics. As the field of fluorescence-based cytomics matured, it reached a technological barrier at around 30 parameter analyses, which stalled the field until spectral flow cytometry created a fundamental transformation that will likely lead to the potential of 100 simultaneous parameter analyses within a few years. The simultaneous advance in informatics has now become a watershed moment for the field as it competes with mature systematic approaches such as genomics and proteomics, allowing cytomics to take a seat at the multi-omics table. In addition, recent technological advances try to combine the speed of flow systems with other detection methods, in addition to fluorescence alone, which will make flow-based instruments even more indispensable in any biological laboratory. This paper outlines current approaches in cell analysis and detection methods, discusses traditional and microfluidic sorting approaches as well as next-generation instruments, and provides an early look at future opportunities that are likely to arise.
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Affiliation(s)
- J Paul Robinson
- Department of Basic Medical Sciences, Purdue University, West Lafayette, IN 47907, USA
- Weldon School of Biomedical Engineering, Purdue University, West Lafayette, IN 47907, USA
| | - Raluca Ostafe
- Molecular Evolution, Protein Engineering and Production Facility (PI4D), Purdue University, West Lafayette, IN 47907, USA
| | | | - Bartek Rajwa
- Bindley Bioscience Center, Purdue University, West Lafayette, IN 47907, USA
| | - Rainer Fischer
- Department of Comparative Pathobiology, College of Veterinary Medicine, Purdue University, West Lafayette, IN 47907, USA
- Purdue Institute of Inflammation, Immunology and Infectious Diseases, Purdue University, West Lafayette, IN 47907, USA
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5
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Robles EE, Jin Y, Smyth P, Scheuermann RH, Bui JD, Wang HY, Oak J, Qian Y. A cell-level discriminative neural network model for diagnosis of blood cancers. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2023:2023.02.07.23285606. [PMID: 36798344 PMCID: PMC9934808 DOI: 10.1101/2023.02.07.23285606] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/12/2023]
Abstract
Motivation Precise identification of cancer cells in patient samples is essential for accurate diagnosis and clinical monitoring but has been a significant challenge in machine learning approaches for cancer precision medicine. In most scenarios, training data are only available with disease annotation at the subject or sample level. Traditional approaches separate the classification process into multiple steps that are optimized independently. Recent methods either focus on predicting sample-level diagnosis without identifying individual pathologic cells or are less effective for identifying heterogeneous cancer cell phenotypes. Results We developed a generalized end-to-end differentiable model, the Cell Scoring Neural Network (CSNN), which takes the available sample-level training data and predicts both the diagnosis of the testing samples and the identity of the diagnostic cells in the sample, simultaneously. The cell-level density differences between samples are linked to the sample diagnosis, which allows the probabilities of individual cells being diagnostic to be calculated using backpropagation. We applied CSNN to two independent clinical flow cytometry datasets for leukemia diagnosis. In both qualitative and quantitative assessments, CSNN outperformed preexisting neural network modeling approaches for both cancer diagnosis and cell-level classification. Post hoc decision trees and 2D dot plots were generated for interpretation of the identified cancer cells, showing that the identified cell phenotypes match the cancer endotypes observed clinically in patient cohorts. Independent data clustering analysis confirmed the identified cancer cell populations. Availability The source code of CSNN and datasets used in the experiments are publicly available on GitHub and FlowRepository. Contact Edgar E. Robles: roblesee@uci.edu and Yu Qian: mqian@jcvi.org. Supplementary information Supplementary data are available on GitHub and at Bioinformatics online.
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Spurgeon BEJ, Frelinger AL. Platelet Phenotyping by Full Spectrum Flow Cytometry. Curr Protoc 2023; 3:e687. [PMID: 36779850 DOI: 10.1002/cpz1.687] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/14/2023]
Abstract
Platelets play key roles in hemostasis, immunity, and inflammation, and tests of platelet phenotype and function are useful in studies of disease biology and pathology. Full spectrum flow cytometry offers distinct advantages over standard tests and enables the sensitive and simultaneous detection of many biomarkers. A typical assay provides a wealth of information on platelet biology and allows the assessment of in vivo activation and in vitro reactivity, as well as the discovery of novel phenotypes. Here, we describe the analysis of platelets by full spectrum flow cytometry and discuss a range of controls and methods for interpreting results. © 2023 Wiley Periodicals LLC. Basic Protocol: Platelet phenotyping by full spectrum flow cytometry Support Protocol 1: Spectral unmixing Support Protocol 2: Data preprocessing.
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Affiliation(s)
- Benjamin E J Spurgeon
- Center for Platelet Research Studies, Dana-Farber/Boston Children's Cancer and Blood Disorders Center, Harvard Medical School, Boston, Massachusetts
| | - Andrew L Frelinger
- Center for Platelet Research Studies, Dana-Farber/Boston Children's Cancer and Blood Disorders Center, Harvard Medical School, Boston, Massachusetts
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7
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De Biasi S, Paolini A, Lo Tartaro D, Gibellini L, Cossarizza A. Analysis of Antigen-Specific T and B Cells for Monitoring Immune Protection Against SARS-CoV-2. Curr Protoc 2023; 3:e636. [PMID: 36598346 DOI: 10.1002/cpz1.636] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/05/2023]
Abstract
Immunological memory is the basis of protection against most pathogens. Long-living memory T and B cells able to respond to specific stimuli, as well as persistent antibodies in plasma and in other body fluids, are crucial for determining the efficacy of vaccination and for protecting from a second infection by a previously encountered pathogen. Antigen-specific cells are represented at a very low frequency in the blood, and indeed, they can be considered "rare events" present in the memory T-cell pool. Therefore, such events should be analyzed with careful attention. In the last 20 years, different methods, mostly based upon flow cytometry, have been developed to identify such rare antigen-specific cells, and the COVID-19 pandemic has given a dramatic impetus to characterize the immune response against the virus. In this regard, we know that the identification, enumeration, and characterization of SARS-CoV-2-specific T and B cells following infection and/or vaccination require i) the use of specific peptides and adequate co-stimuli, ii) the use of appropriate inhibitors to avoid nonspecific activation, iii) the setting of appropriate timing for stimulation, and iv) the choice of adequate markers and reagents to identify antigen-specific cells. Optimization of these procedures allows not only determination of the magnitude of SARS-CoV-2-specific responses but also a comparison of the effects of different combinations of vaccines or determination of the response provided by so-called "hybrid immunity," resulting from a combination of natural immunity and vaccine-generated immunity. Here, we present two methods that are largely used to monitor the response magnitude and phenotype of SARS-CoV-2-specific T and B cells by polychromatic flow cytometry, along with some tips that can be useful for the quantification of these rare events. © 2023 Wiley Periodicals LLC. Basic Protocol 1: Identification of antigen-specific T cells Basic Protocol 2: Identification of antigen-specific B cells.
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Affiliation(s)
- Sara De Biasi
- Department of Medical and Surgical Sciences for Children & Adults, University of Modena and Reggio Emilia, via Campi, Modena, Italy
| | - Annamaria Paolini
- Department of Medical and Surgical Sciences for Children & Adults, University of Modena and Reggio Emilia, via Campi, Modena, Italy
| | - Domenico Lo Tartaro
- Department of Medical and Surgical Sciences for Children & Adults, University of Modena and Reggio Emilia, via Campi, Modena, Italy
| | - Lara Gibellini
- Department of Medical and Surgical Sciences for Children & Adults, University of Modena and Reggio Emilia, via Campi, Modena, Italy
| | - Andrea Cossarizza
- Department of Medical and Surgical Sciences for Children & Adults, University of Modena and Reggio Emilia, via Campi, Modena, Italy.,Istituto Nazionale per le Ricerche Cardiovascolari - INRC, via Irnerio, Bologna, Italy
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8
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Ouyang K, Zheng DX, Agak GW. T-Cell Mediated Immunity in Merkel Cell Carcinoma. Cancers (Basel) 2022; 14:cancers14246058. [PMID: 36551547 PMCID: PMC9775569 DOI: 10.3390/cancers14246058] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/11/2022] [Revised: 12/01/2022] [Accepted: 12/04/2022] [Indexed: 12/13/2022] Open
Abstract
Merkel cell carcinoma (MCC) is a rare and frequently lethal skin cancer with neuroendocrine characteristics. MCC can originate from either the presence of MCC polyomavirus (MCPyV) DNA or chronic ultraviolet (UV) exposure that can cause DNA mutations. MCC is predominant in sun-exposed regions of the body and can metastasize to regional lymph nodes, liver, lungs, bone, and brain. Older, light-skinned individuals with a history of significant sun exposure are at the highest risk. Previous studies have shown that tumors containing a high number of tumor-infiltrating T-cells have favorable survival, even in the absence of MCPyV DNA, suggesting that MCPyV infection enhances T-cell infiltration. However, other factors may also play a role in the host antitumor response. Herein, we review the impact of tumor infiltrating lymphocytes (TILs), mainly the CD4+, CD8+, and regulatory T-cell (Tregs) responses on the course of MCC, including their role in initiating MCPyV-specific immune responses. Furthermore, potential research avenues related to T-cell biology in MCC, as well as relevant immunotherapies are discussed.
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Affiliation(s)
- Kelsey Ouyang
- Division of Dermatology, Department of Medicine, David Geffen School of Medicine at UCLA, Los Angeles, CA 90095, USA
- Cleveland Clinic Lerner College of Medicine, Case Western Reserve University, Cleveland, OH 44195, USA
| | - David X. Zheng
- Department of Dermatology, University Hospitals Cleveland Medical Center, Case Western Reserve University, Cleveland, OH 44106, USA
| | - George W. Agak
- Division of Dermatology, Department of Medicine, David Geffen School of Medicine at UCLA, Los Angeles, CA 90095, USA
- Correspondence:
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Bechi Genzano C, Bezzecchi E, Carnovale D, Mandelli A, Morotti E, Castorani V, Favalli V, Stabilini A, Insalaco V, Ragogna F, Codazzi V, Scotti GM, Del Rosso S, Mazzi BA, De Pellegrin M, Giustina A, Piemonti L, Bosi E, Battaglia M, Morelli MJ, Bonfanti R, Petrelli A. Combined unsupervised and semi-automated supervised analysis of flow cytometry data reveals cellular fingerprint associated with newly diagnosed pediatric type 1 diabetes. Front Immunol 2022; 13:1026416. [PMID: 36389771 PMCID: PMC9647173 DOI: 10.3389/fimmu.2022.1026416] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/23/2022] [Accepted: 10/11/2022] [Indexed: 11/03/2023] Open
Abstract
An unbiased and replicable profiling of type 1 diabetes (T1D)-specific circulating immunome at disease onset has yet to be identified due to experimental and patient selection limitations. Multicolor flow cytometry was performed on whole blood from a pediatric cohort of 107 patients with new-onset T1D, 85 relatives of T1D patients with 0-1 islet autoantibodies (pre-T1D_LR), 58 patients with celiac disease or autoimmune thyroiditis (CD_THY) and 76 healthy controls (HC). Unsupervised clustering of flow cytometry data, validated by a semi-automated gating strategy, confirmed previous findings showing selective increase of naïve CD4 T cells and plasmacytoid DCs, and revealed a decrease in CD56brightNK cells in T1D. Furthermore, a non-selective decrease of CD3+CD56+ regulatory T cells was observed in T1D. The frequency of naïve CD4 T cells at disease onset was associated with partial remission, while it was found unaltered in the pre-symptomatic stages of the disease. Thanks to a broad cohort of pediatric individuals and the implementation of unbiased approaches for the analysis of flow cytometry data, here we determined the circulating immune fingerprint of newly diagnosed pediatric T1D and provide a reference dataset to be exploited for validation or discovery purposes to unravel the pathogenesis of T1D.
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Affiliation(s)
| | - Eugenia Bezzecchi
- Diabetes Research Institute, IRCCS Ospedale San Raffaele, Milan, Italy
- Center for Omics Sciences, IRCCS Ospedale San Raffaele, Milan, Italy
| | - Debora Carnovale
- Diabetes Research Institute, IRCCS Ospedale San Raffaele, Milan, Italy
| | | | - Elisa Morotti
- Diabetes Research Institute, IRCCS Ospedale San Raffaele, Milan, Italy
- Department of Pediatrics, IRCCS Ospedale San Raffaele, Milan, Italy
| | - Valeria Castorani
- Diabetes Research Institute, IRCCS Ospedale San Raffaele, Milan, Italy
| | - Valeria Favalli
- Diabetes Research Institute, IRCCS Ospedale San Raffaele, Milan, Italy
- Department of Pediatrics, IRCCS Ospedale San Raffaele, Milan, Italy
| | - Angela Stabilini
- Diabetes Research Institute, IRCCS Ospedale San Raffaele, Milan, Italy
| | - Vittoria Insalaco
- Diabetes Research Institute, IRCCS Ospedale San Raffaele, Milan, Italy
| | - Francesca Ragogna
- Diabetes Research Institute, IRCCS Ospedale San Raffaele, Milan, Italy
| | - Valentina Codazzi
- Diabetes Research Institute, IRCCS Ospedale San Raffaele, Milan, Italy
| | | | - Stefania Del Rosso
- Laboratory Medicine, Autoimmunity Section, IRCCS Ospedale San Raffaele, Milan, Italy
| | - Benedetta Allegra Mazzi
- Immuno-Hematology and Transfusion Medicine (ITMS), IRCCS Ospedale San Raffaele, Milan, Italy
| | - Maurizio De Pellegrin
- Pediatric Orthopedic and Traumatology Unit, IRCCS Ospedale San Raffaele, Milan, Italy
| | - Andrea Giustina
- Institute of Endocrine and Metabolic Sciences, IRCCS Ospedale San Raffaele, Milan, Italy
- Università Vita-Salute San Raffaele, Milan, Italy
| | - Lorenzo Piemonti
- Diabetes Research Institute, IRCCS Ospedale San Raffaele, Milan, Italy
- Università Vita-Salute San Raffaele, Milan, Italy
| | - Emanuele Bosi
- Diabetes Research Institute, IRCCS Ospedale San Raffaele, Milan, Italy
- Department of General Medicine, Diabetes and Endocrinology, IRCCS Ospedale San Raffaele, Milan, Italy
- Università Vita-Salute San Raffaele, Milan, Italy
| | - Manuela Battaglia
- Diabetes Research Institute, IRCCS Ospedale San Raffaele, Milan, Italy
| | - Marco J. Morelli
- Center for Omics Sciences, IRCCS Ospedale San Raffaele, Milan, Italy
| | - Riccardo Bonfanti
- Diabetes Research Institute, IRCCS Ospedale San Raffaele, Milan, Italy
- Department of Pediatrics, IRCCS Ospedale San Raffaele, Milan, Italy
- Università Vita-Salute San Raffaele, Milan, Italy
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Barber B, Mair F, Prlic M. A path forward to improving the specificity of immunotherapies. Clin Transl Med 2022; 12:e1051. [PMID: 36101938 PMCID: PMC9471041 DOI: 10.1002/ctm2.1051] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/19/2022] [Accepted: 08/25/2022] [Indexed: 11/09/2022] Open
Affiliation(s)
- Brittany Barber
- Department of OtolaryngologyUniversity of WashingtonSeattleWashington
| | - Florian Mair
- Department of BiologyInstitute of Molecular Health SciencesETH ZurichSwitzerland
- Vaccine and Infectious Disease DivisionFred Hutchinson Cancer CenterSeattleWashingtonUnited States
| | - Martin Prlic
- Vaccine and Infectious Disease DivisionFred Hutchinson Cancer CenterSeattleWashingtonUnited States
- Department of Global HealthUniversity of WashingtonSeattleWashington
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Zearfoss R. Tumor biology gets smart. Cell 2022; 185:2611-2612. [DOI: 10.1016/j.cell.2022.06.021] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/09/2022] [Revised: 06/09/2022] [Accepted: 06/09/2022] [Indexed: 11/25/2022]
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12
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Gálvez-Cancino F, Lladser A, Quezada SA. Deciphering immunoregulatory vulnerabilities in human cancers. Nat Immunol 2022; 23:995-996. [PMID: 35726061 DOI: 10.1038/s41590-022-01251-w] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Affiliation(s)
- Felipe Gálvez-Cancino
- Cancer Immunology Unit, Research Department of Haematology, University College London Cancer Institute, London, UK
| | - Alvaro Lladser
- Centro Cientifico y Tecnologico de Excelencia Ciencia & Vida, Fundacion Ciencia & Vida, Santiago, Chile.
- Facultad de Medicina y Ciencia, Universidad San Sebastián, Santiago, Chile.
| | - Sergio A Quezada
- Cancer Immunology Unit, Research Department of Haematology, University College London Cancer Institute, London, UK.
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Zelin E, Maronese CA, Dri A, Toffoli L, Di Meo N, Nazzaro G, Zalaudek I. Identifying Candidates for Immunotherapy among Patients with Non-Melanoma Skin Cancer: A Review of the Potential Predictors of Response. J Clin Med 2022; 11:3364. [PMID: 35743435 PMCID: PMC9225110 DOI: 10.3390/jcm11123364] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/05/2022] [Revised: 06/02/2022] [Accepted: 06/09/2022] [Indexed: 02/04/2023] Open
Abstract
BACKGROUND Non-melanoma skin cancer (NMSC) stands as an umbrella term for common cutaneous malignancies, including basal cell carcinoma (BCC) and cutaneous squamous cell carcinoma (cSCC), together with rarer cutaneous cancers, such as Merkel cell carcinoma (MCC) and other forms of adnexal cancers. The majority of NMSCs can be successfully treated with surgery or radiotherapy, but advanced and metastatic stages may require systemic approaches such as immunotherapy with immune checkpoint inhibitors (ICIs). SUMMARY Since immunotherapy is not effective in all patients and can potentially lead to severe adverse effects, an important clinical question is how to properly identify those who could be suitable candidates for this therapeutic choice. In this paper, we review the potential features and biomarkers used to predict the outcome of ICIs therapy for NMSCs. Moreover, we analyze the role of immunotherapy in special populations, such as the elderly, immunocompromised patients, organ transplant recipients, and subjects suffering from autoimmune conditions. KEY MESSAGES Many clinical, serum, histopathological, and genetic features have been investigated as potential predictors of response in NMSCs treated with ICIs. Although this field of research is very promising, definitive, cost-effective, and reproducible biomarkers are still lacking and further efforts are needed to validate the suggested predictors in larger cohorts.
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Affiliation(s)
- Enrico Zelin
- Dermatology Clinic, Maggiore Hospital, University of Trieste, 34125 Trieste, Italy; (E.Z.); (L.T.); (N.D.M.); (I.Z.)
| | - Carlo Alberto Maronese
- Dermatology Unit, Fondazione IRCCS Cà Granda Ospedale Maggiore Policlinico, 20122 Milan, Italy;
- Department of Pathophysiology and Transplantation, Università degli Studi di Milano, 20122 Milan, Italy
| | - Arianna Dri
- Department of Medicine (DAME), University of Udine, 33100 Udine, Italy;
- Department of Medical Oncology, Azienda Sanitaria Friuli Centrale (ASUFC), 33100 Udine, Italy
| | - Ludovica Toffoli
- Dermatology Clinic, Maggiore Hospital, University of Trieste, 34125 Trieste, Italy; (E.Z.); (L.T.); (N.D.M.); (I.Z.)
| | - Nicola Di Meo
- Dermatology Clinic, Maggiore Hospital, University of Trieste, 34125 Trieste, Italy; (E.Z.); (L.T.); (N.D.M.); (I.Z.)
| | - Gianluca Nazzaro
- Dermatology Unit, Fondazione IRCCS Cà Granda Ospedale Maggiore Policlinico, 20122 Milan, Italy;
| | - Iris Zalaudek
- Dermatology Clinic, Maggiore Hospital, University of Trieste, 34125 Trieste, Italy; (E.Z.); (L.T.); (N.D.M.); (I.Z.)
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14
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De Biasi S, Guida A, Lo Tartaro D, Fanelli M, Depenni R, Dominici M, Finak G, Porta C, Paolini A, Borella R, Bertoldi C, Cossarizza A, Sabbatini R, Gibellini L. Redistribution of CD8+ T cell subsets in metastatic renal cell carcinoma patients treated with anti-PD-1 therapy. Cytometry A 2022; 101:597-605. [PMID: 35507402 PMCID: PMC9542732 DOI: 10.1002/cyto.a.24562] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/19/2021] [Revised: 04/19/2022] [Accepted: 04/22/2022] [Indexed: 11/20/2022]
Abstract
Renal‐cell carcinoma (RCC) is responsible for the majority of tumors arising from the kidney parenchyma. Although a progressive improvement in median overall survival has been observed after the introduction of anti‐PD‐1 therapy, many patients do not benefit from this treatment. Therefore, we have investigated T cell dynamics to find immune modification induced by anti‐PD‐1 therapy. Here, we show that, after therapy, RCC patients (5 responders and 14 nonresponders) are characterized by a redistribution of different subsets across the memory T cell compartment.
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Affiliation(s)
- Sara De Biasi
- Department of Medical and Surgical Sciences for Children & Adults, University of Modena and Reggio Emilia, Emilia-Romagna
| | - Annalisa Guida
- Azienda Ospedaliera Santa Maria, Terni, Italy.,Department of Oncology, University of Modena & Reggio Emilia, Modena, Italy
| | - Domenico Lo Tartaro
- Department of Medical and Surgical Sciences for Children & Adults, University of Modena and Reggio Emilia, Emilia-Romagna.,Clinical and Experimental Medicine PhD Program, University of Modena and Reggio Emilia, Modena, Italy
| | - Martina Fanelli
- Department of Medical and Surgical Sciences for Children & Adults, University of Modena and Reggio Emilia, Emilia-Romagna.,Department of Oncology, University of Modena & Reggio Emilia, Modena, Italy
| | - Roberta Depenni
- Department of Oncology, University of Modena & Reggio Emilia, Modena, Italy
| | - Massimo Dominici
- Department of Medical and Surgical Sciences for Children & Adults, University of Modena and Reggio Emilia, Emilia-Romagna.,Department of Oncology, University of Modena & Reggio Emilia, Modena, Italy
| | - Greg Finak
- Fred Hutchinson Cancer Research Center, Seattle, USA
| | | | - Annamaria Paolini
- Department of Medical and Surgical Sciences for Children & Adults, University of Modena and Reggio Emilia, Emilia-Romagna.,Clinical and Experimental Medicine PhD Program, University of Modena and Reggio Emilia, Modena, Italy
| | - Rebecca Borella
- Department of Medical and Surgical Sciences for Children & Adults, University of Modena and Reggio Emilia, Emilia-Romagna.,Clinical and Experimental Medicine PhD Program, University of Modena and Reggio Emilia, Modena, Italy
| | - Carlo Bertoldi
- Department of Surgery, Medicine, Dentistry and Morphological Sciences, University of Modena and Reggio Emilia, Italy
| | - Andrea Cossarizza
- Department of Medical and Surgical Sciences for Children & Adults, University of Modena and Reggio Emilia, Emilia-Romagna.,National Institute for Cardiovascular Research, Bologna
| | - Roberto Sabbatini
- Department of Oncology, University of Modena & Reggio Emilia, Modena, Italy
| | - Lara Gibellini
- Department of Medical and Surgical Sciences for Children & Adults, University of Modena and Reggio Emilia, Emilia-Romagna
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15
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Mair F, Erickson JR, Frutoso M, Konecny AJ, Greene E, Voillet V, Maurice NJ, Rongvaux A, Dixon D, Barber B, Gottardo R, Prlic M. Extricating human tumour immune alterations from tissue inflammation. Nature 2022; 605:728-735. [PMID: 35545675 PMCID: PMC9132772 DOI: 10.1038/s41586-022-04718-w] [Citation(s) in RCA: 47] [Impact Index Per Article: 23.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/23/2021] [Accepted: 04/01/2022] [Indexed: 12/17/2022]
Abstract
Immunotherapies have achieved remarkable successes in the treatment of cancer, but major challenges remain1,2. An inherent weakness of current treatment approaches is that therapeutically targeted pathways are not restricted to tumours, but are also found in other tissue microenvironments, complicating treatment3,4. Despite great efforts to define inflammatory processes in the tumour microenvironment, the understanding of tumour-unique immune alterations is limited by a knowledge gap regarding the immune cell populations in inflamed human tissues. Here, in an effort to identify such tumour-enriched immune alterations, we used complementary single-cell analysis approaches to interrogate the immune infiltrate in human head and neck squamous cell carcinomas and site-matched non-malignant, inflamed tissues. Our analysis revealed a large overlap in the composition and phenotype of immune cells in tumour and inflamed tissues. Computational analysis identified tumour-enriched immune cell interactions, one of which yields a large population of regulatory T (Treg) cells that is highly enriched in the tumour and uniquely identified among all haematopoietically-derived cells in blood and tissue by co-expression of ICOS and IL-1 receptor type 1 (IL1R1). We provide evidence that these intratumoural IL1R1+ Treg cells had responded to antigen recently and demonstrate that they are clonally expanded with superior suppressive function compared with IL1R1- Treg cells. In addition to identifying extensive immunological congruence between inflamed tissues and tumours as well as tumour-specific changes with direct disease relevance, our work also provides a blueprint for extricating disease-specific changes from general inflammation-associated patterns.
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Affiliation(s)
- Florian Mair
- Fred Hutchinson Cancer Research Center, Vaccine and Infectious Disease Division, Seattle, WA, USA
| | - Jami R Erickson
- Fred Hutchinson Cancer Research Center, Vaccine and Infectious Disease Division, Seattle, WA, USA
- Department of Immunology, University of Washington, Seattle, WA, USA
| | - Marie Frutoso
- Fred Hutchinson Cancer Research Center, Vaccine and Infectious Disease Division, Seattle, WA, USA
| | - Andrew J Konecny
- Fred Hutchinson Cancer Research Center, Vaccine and Infectious Disease Division, Seattle, WA, USA
- Department of Immunology, University of Washington, Seattle, WA, USA
| | - Evan Greene
- Fred Hutchinson Cancer Research Center, Vaccine and Infectious Disease Division, Seattle, WA, USA
| | - Valentin Voillet
- Fred Hutchinson Cancer Research Center, Vaccine and Infectious Disease Division, Seattle, WA, USA
- Cape Town HVTN Immunology Laboratory, Hutchinson Centre Research Institute of South Africa, NPC (HCRISA), Cape Town, South Africa
| | - Nicholas J Maurice
- Fred Hutchinson Cancer Research Center, Vaccine and Infectious Disease Division, Seattle, WA, USA
- Molecular and Cellular Biology Graduate Program, University of Washington, Seattle, WA, USA
| | - Anthony Rongvaux
- Department of Immunology, University of Washington, Seattle, WA, USA
- Fred Hutchinson Cancer Research Center, Clinical Research Division, Seattle, WA, USA
| | - Douglas Dixon
- Department of Periodontics, School of Dentistry, University of Washington, Seattle, WA, USA
- Department of Periodontics, University of Tennessee Health Science Center, College of Dentistry, Memphis, TN, USA
| | - Brittany Barber
- Department of Otolaryngology-Head and Neck Surgery, University of Washington, Seattle, WA, USA
| | - Raphael Gottardo
- Fred Hutchinson Cancer Research Center, Vaccine and Infectious Disease Division, Seattle, WA, USA
- Department of Statistics, University of Washington, Seattle, WA, USA
- University of Lausanne and Lausanne University Hospital, Switzerland, Lausanne, Switzerland
| | - Martin Prlic
- Fred Hutchinson Cancer Research Center, Vaccine and Infectious Disease Division, Seattle, WA, USA.
- Department of Global Health, University of Washington, Seattle, WA, USA.
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16
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Ru B, Jiang P. Discover immunotherapy biomarkers from single-cell cytometry data. PATTERNS (NEW YORK, N.Y.) 2021; 2:100384. [PMID: 34950905 PMCID: PMC8672134 DOI: 10.1016/j.patter.2021.100384] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 12/02/2022]
Abstract
Currently, identifying novel biomarkers remains a crucial need for cancer immunotherapy. By leveraging single-cell cytometry data, Greene et al. developed an interpretable machine learning method, FAUST, to discover cell populations associated with clinical outcomes.
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Affiliation(s)
- Beibei Ru
- Cancer Data Science Lab, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
| | - Peng Jiang
- Cancer Data Science Lab, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
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17
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Vick SC, Frutoso M, Mair F, Konecny AJ, Greene E, Wolf CR, Logue JK, Franko NM, Boonyaratanakornkit J, Gottardo R, Schiffer JT, Chu HY, Prlic M, Lund JM. A regulatory T cell signature distinguishes the immune landscape of COVID-19 patients from those with other respiratory infections. SCIENCE ADVANCES 2021; 7:eabj0274. [PMID: 34757794 PMCID: PMC8580318 DOI: 10.1126/sciadv.abj0274] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/15/2021] [Accepted: 09/22/2021] [Indexed: 06/01/2023]
Abstract
Despite recent studies of immunity to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), little is known about how the immune response against SARS-CoV-2 differs from other respiratory infections. We compare the immune signature from hospitalized SARS-CoV-2–infected patients to patients hospitalized prepandemic with influenza or respiratory syncytial virus (RSV). Our in-depth profiling indicates that the immune landscape in SARS-CoV-2 patients is largely similar to flu or RSV patients. Unique to patients infected with SARS-CoV-2 who had the most critical clinical disease were changes in the regulatory T cell (Treg) compartment. A Treg signature including increased frequency, activation status, and migration markers was correlated COVID-19 severity. These findings are relevant as Tregs are considered for therapy to combat the severe inflammation seen in COVID-19 patients. Likewise, having defined the overlapping immune landscapes in SARS-CoV-2, existing knowledge of flu and RSV infections could be leveraged to identify common treatment strategies.
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Affiliation(s)
- Sarah C. Vick
- Vaccine and Infectious Disease Division, Fred Hutchinson Cancer Research Center, Seattle, WA 98109, USA
| | - Marie Frutoso
- Vaccine and Infectious Disease Division, Fred Hutchinson Cancer Research Center, Seattle, WA 98109, USA
| | - Florian Mair
- Vaccine and Infectious Disease Division, Fred Hutchinson Cancer Research Center, Seattle, WA 98109, USA
| | - Andrew J. Konecny
- Vaccine and Infectious Disease Division, Fred Hutchinson Cancer Research Center, Seattle, WA 98109, USA
- Department of Immunology, University of Washington, Seattle, WA 98195, USA
| | - Evan Greene
- Vaccine and Infectious Disease Division, Fred Hutchinson Cancer Research Center, Seattle, WA 98109, USA
| | - Caitlin R. Wolf
- Department of Medicine, University of Washington, Seattle, WA 98195, USA
| | - Jennifer K. Logue
- Department of Medicine, University of Washington, Seattle, WA 98195, USA
| | - Nicholas M. Franko
- Department of Medicine, University of Washington, Seattle, WA 98195, USA
| | - Jim Boonyaratanakornkit
- Vaccine and Infectious Disease Division, Fred Hutchinson Cancer Research Center, Seattle, WA 98109, USA
- Department of Medicine, University of Washington, Seattle, WA 98195, USA
| | - Raphael Gottardo
- Vaccine and Infectious Disease Division, Fred Hutchinson Cancer Research Center, Seattle, WA 98109, USA
| | - Joshua T. Schiffer
- Vaccine and Infectious Disease Division, Fred Hutchinson Cancer Research Center, Seattle, WA 98109, USA
- Department of Medicine, University of Washington, Seattle, WA 98195, USA
| | - Helen Y. Chu
- Department of Medicine, University of Washington, Seattle, WA 98195, USA
| | - Martin Prlic
- Vaccine and Infectious Disease Division, Fred Hutchinson Cancer Research Center, Seattle, WA 98109, USA
- Department of Immunology, University of Washington, Seattle, WA 98195, USA
| | - Jennifer M. Lund
- Vaccine and Infectious Disease Division, Fred Hutchinson Cancer Research Center, Seattle, WA 98109, USA
- Department of Global Health, University of Washington, Seattle, WA 98195, USA
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