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G. de Castro C, G. del Hierro A, H-Vázquez J, Cuesta-Sancho S, Bernardo D. State-of-the-art cytometry in the search of novel biomarkers in digestive cancers. Front Oncol 2024; 14:1407580. [PMID: 38868532 PMCID: PMC11167087 DOI: 10.3389/fonc.2024.1407580] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/26/2024] [Accepted: 05/10/2024] [Indexed: 06/14/2024] Open
Abstract
Despite that colorectal and liver cancer are among the most prevalent tumours in the world, the identification of non-invasive biomarkers to aid on their diagnose and subsequent prognosis is a current unmet need that would diminish both their incidence and mortality rates. In this context, conventional flow cytometry has been widely used in the screening of biomarkers with clinical utility in other malignant processes like leukaemia or lymphoma. Therefore, in this review, we will focus on how advanced cytometry panels covering over 40 parameters can be applied on the study of the immune system from patients with colorectal and hepatocellular carcinoma and how that can be used on the search of novel biomarkers to aid or diagnose, prognosis, and even predict clinical response to different treatments. In addition, these multiparametric and unbiased approaches can also provide novel insights into the specific immunopathogenic mechanisms governing these malignant diseases, hence potentially unravelling novel targets to perform immunotherapy or identify novel mechanisms, rendering the development of novel treatments. As a consequence, computational cytometry approaches are an emerging methodology for the early detection and predicting therapies for gastrointestinal cancers.
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Affiliation(s)
- Carolina G. de Castro
- Mucosal Immunology Lab, Institute of Biomedicine and Molecular Genetics (IBGM), University of Valladolid and Consejo Superior de Investigaciones Científicas (CSIC), Valladolid, Spain
| | - Alejandro G. del Hierro
- Mucosal Immunology Lab, Institute of Biomedicine and Molecular Genetics (IBGM), University of Valladolid and Consejo Superior de Investigaciones Científicas (CSIC), Valladolid, Spain
| | - Juan H-Vázquez
- Mucosal Immunology Lab, Institute of Biomedicine and Molecular Genetics (IBGM), University of Valladolid and Consejo Superior de Investigaciones Científicas (CSIC), Valladolid, Spain
| | - Sara Cuesta-Sancho
- Mucosal Immunology Lab, Institute of Biomedicine and Molecular Genetics (IBGM), University of Valladolid and Consejo Superior de Investigaciones Científicas (CSIC), Valladolid, Spain
| | - David Bernardo
- Mucosal Immunology Lab, Institute of Biomedicine and Molecular Genetics (IBGM), University of Valladolid and Consejo Superior de Investigaciones Científicas (CSIC), Valladolid, Spain
- Centro de Investigaciones Biomedicas en Red de Enfermedades Infecciosas (CIBERINFEC), Madrid, Spain
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Thomsen LCV, Kleinmanns K, Anandan S, Gullaksen SE, Abdelaal T, Iversen GA, Akslen LA, McCormack E, Bjørge L. Combining Mass Cytometry Data by CyTOFmerge Reveals Additional Cell Phenotypes in the Heterogeneous Ovarian Cancer Tumor Microenvironment: A Pilot Study. Cancers (Basel) 2023; 15:5106. [PMID: 37894472 PMCID: PMC10605295 DOI: 10.3390/cancers15205106] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/08/2023] [Revised: 10/06/2023] [Accepted: 10/17/2023] [Indexed: 10/29/2023] Open
Abstract
The prognosis of high-grade serous ovarian carcinoma (HGSOC) is poor, and treatment selection is challenging. A heterogeneous tumor microenvironment (TME) characterizes HGSOC and influences tumor growth, progression, and therapy response. Better characterization with multidimensional approaches for simultaneous identification and categorization of the various cell populations is needed to map the TME complexity. While mass cytometry allows the simultaneous detection of around 40 proteins, the CyTOFmerge MATLAB algorithm integrates data sets and extends the phenotyping. This pilot study explored the potential of combining two datasets for improved TME phenotyping by profiling single-cell suspensions from ten chemo-naïve HGSOC tumors by mass cytometry. A 35-marker pan-tumor dataset and a 34-marker pan-immune dataset were analyzed separately and combined with the CyTOFmerge, merging 18 shared markers. While the merged analysis confirmed heterogeneity across patients, it also identified a main tumor cell subset, additionally to the nine identified by the pan-tumor panel. Furthermore, the expression of traditional immune cell markers on tumor and stromal cells was revealed, as were marker combinations that have rarely been examined on individual cells. This study demonstrates the potential of merging mass cytometry data to generate new hypotheses on tumor biology and predictive biomarker research in HGSOC that could improve treatment effectiveness.
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Affiliation(s)
- Liv Cecilie Vestrheim Thomsen
- Centre for Cancer Biomarkers CCBIO, Department of Clinical Science, University of Bergen, 5021 Bergen, Norway
- Department of Obstetrics and Gynecology, Haukeland University Hospital, 5021 Bergen, Norway
- Norwegian Institute of Public Health, 5015 Bergen, Norway
| | - Katrin Kleinmanns
- Centre for Cancer Biomarkers CCBIO, Department of Clinical Science, University of Bergen, 5021 Bergen, Norway
| | - Shamundeeswari Anandan
- Centre for Cancer Biomarkers CCBIO, Department of Clinical Science, University of Bergen, 5021 Bergen, Norway
- Department of Obstetrics and Gynecology, Haukeland University Hospital, 5021 Bergen, Norway
| | - Stein-Erik Gullaksen
- Centre for Cancer Biomarkers CCBIO, Department of Clinical Science, University of Bergen, 5021 Bergen, Norway
| | - Tamim Abdelaal
- Delft Bioinformatics Laboratory, Delft University of Technology, 2628XE Delft, The Netherlands
- Department of Radiology, Leiden University Medical Center, 2333ZA Leiden, The Netherlands
| | - Grete Alrek Iversen
- Department of Obstetrics and Gynecology, Haukeland University Hospital, 5021 Bergen, Norway
| | - Lars Andreas Akslen
- Centre for Cancer Biomarkers CCBIO, Department of Clinical Medicine, University of Bergen, 5021 Bergen, Norway
- Department of Pathology, Haukeland University Hospital, 5021 Bergen, Norway
| | - Emmet McCormack
- Centre for Cancer Biomarkers CCBIO, Department of Clinical Science, University of Bergen, 5021 Bergen, Norway
- Centre for Pharmacy, Department of Clinical Science, University of Bergen, 5021 Bergen, Norway
| | - Line Bjørge
- Centre for Cancer Biomarkers CCBIO, Department of Clinical Science, University of Bergen, 5021 Bergen, Norway
- Department of Obstetrics and Gynecology, Haukeland University Hospital, 5021 Bergen, Norway
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Abstract
Mass cytometry has revolutionized immunophenotyping, particularly in exploratory settings where simultaneous breadth and depth of characterization of immune populations is needed with limited samples such as in preclinical and clinical tumor immunotherapy. Mass cytometry is also a powerful tool for single-cell immunological assays, especially for complex and simultaneous characterization of diverse intratumoral immune subsets or immunotherapeutic cell populations. Through the elimination of spectral overlap seen in optical flow cytometry by replacement of fluorescent labels with metal isotopes, mass cytometry allows, on average, robust analysis of 60 individual parameters simultaneously. This is, however, associated with significantly increased complexity in the design, execution, and interpretation of mass cytometry experiments. To address the key pitfalls associated with the fragmentation, complexity, and analysis of data in mass cytometry for immunologists who are novices to these techniques, we have developed a comprehensive resource guide. Included in this review are experiment and panel design, antibody conjugations, sample staining, sample acquisition, and data pre-processing and analysis. Where feasible multiple resources for the same process are compared, allowing researchers experienced in flow cytometry but with minimal mass cytometry expertise to develop a data-driven and streamlined project workflow. It is our hope that this manuscript will prove a useful resource for both beginning and advanced users of mass cytometry.
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Affiliation(s)
- Akshay Iyer
- Department of Pediatrics, University of Miami Miller School of Medicine, Miami, FL, United States
- Department of Microbiology and Immunology, University of Miami Miller School of Medicine, Miami, FL, United States
| | - Anouk A. J. Hamers
- Department of Pediatrics, University of Miami Miller School of Medicine, Miami, FL, United States
- Department of Microbiology and Immunology, University of Miami Miller School of Medicine, Miami, FL, United States
- Sylvester Comprehensive Cancer Center, University of Miami Miller School of Medicine, Miami, FL, United States
- *Correspondence: Anouk A. J. Hamers, ; Asha B. Pillai,
| | - Asha B. Pillai
- Department of Pediatrics, University of Miami Miller School of Medicine, Miami, FL, United States
- Department of Microbiology and Immunology, University of Miami Miller School of Medicine, Miami, FL, United States
- Sylvester Comprehensive Cancer Center, University of Miami Miller School of Medicine, Miami, FL, United States
- Sheila and David Fuente Program in Cancer Biology, University of Miami Miller School of Medicine, Miami, FL, United States
- *Correspondence: Anouk A. J. Hamers, ; Asha B. Pillai,
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Dent A, Diamandis P. Integrating computational pathology and proteomics to address tumor heterogeneity. J Pathol 2022; 257:445-453. [DOI: 10.1002/path.5905] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/11/2022] [Revised: 03/20/2022] [Accepted: 03/30/2022] [Indexed: 11/11/2022]
Affiliation(s)
- Anglin Dent
- Department of Laboratory Medicine and Pathobiology University of Toronto Toronto Ontario M5S 1A8 Canada
- Princess Margaret Cancer Center University Health Network, Toronto, Ontario, 610 University Avenue, M5G 2C1 Canada
| | - Phedias Diamandis
- Department of Laboratory Medicine and Pathobiology University of Toronto Toronto Ontario M5S 1A8 Canada
- Princess Margaret Cancer Center University Health Network, Toronto, Ontario, 610 University Avenue, M5G 2C1 Canada
- Laboratory Medicine Program University Health Network, 200 Elizabeth Street, Toronto, ON Toronto Ontario M5G 2C4 Canada
- Department of Medical Biophysics University of Toronto Toronto Ontario M5S 1A8 Canada
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Gonzalez VD, Huang YW, Fantl WJ. Mass Cytometry for the Characterization of Individual Cell Types in Ovarian Solid Tumors. Methods Mol Biol 2022; 2424:59-94. [PMID: 34918287 PMCID: PMC10509819 DOI: 10.1007/978-1-0716-1956-8_4] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/22/2023]
Abstract
Mass cytometry aka Cytometry by Time-Of-Flight (CyTOF) is one of several recently developed multiparametric single-cell technologies designed to address cellular heterogeneity within healthy and diseased tissue. Mass cytometry is an adaptation of flow cytometry in which antibodies are labeled with stable heavy metal isotopes and the readout is by time-of-flight mass spectrometry. With minimal spillover between channels, mass cytometry enables readouts of up to 60 parameters per single cell. Critically, mass cytometry can identify minority cell populations that are lost in bulk tissue analysis. Mass cytometry has been used to great effect for the study of immune cells. We have extended its use to examine single cells within disaggregated solid tissues, specifically freshly resected tubo-ovarian high-grade serous tumors. Here we detail our protocols designed to ensure the production of high-quality single-cell datasets. The methodology can be modified to accommodate the study of other solid tissues.
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Affiliation(s)
- Veronica D Gonzalez
- Baxter Laboratory for Stem Cell Biology, Department of Microbiology & Immunology, Stanford University School of Medicine, Stanford, CA, USA
- 10X Genomics, Pleasanton, CA, USA
| | - Ying-Wen Huang
- Department of Urology, Stanford University School of Medicine, Stanford, CA, USA
| | - Wendy J Fantl
- Department of Urology, Department of Obstetrics and Gynecology, Stanford Comprehensive Cancer Institute, Stanford, CA, USA.
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Pasquini L, Riccioni R, Petrucci E. Assessment of Tumor Heterogeneity in High-Grade Serous Ovarian Cancer: Mass Cytometry to Understand the Complex Tumor Biology. Methods Mol Biol 2022; 2535:105-118. [PMID: 35867226 DOI: 10.1007/978-1-0716-2513-2_9] [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] [Indexed: 06/15/2023]
Abstract
Ovarian cancer (OC) is the most deadly gynecological malignancy worldwide. OC patients undergo debulking surgery followed by platinum/taxane-based chemotherapy; however, despite recent development of new therapeutic approaches based on combination of chemotherapy and innovative targeted-therapies, most of them relapse due to chemoresistance. Many studies have been carried out to decipher the high heterogeneity of ovarian cancer cells that drives tumor treatment failure. Here, we describe our experience in the characterization of ovarian cancer cell subsets through a high-resolution technology in multiparametric analysis, such as mass cytometry (MC).
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Affiliation(s)
- Luca Pasquini
- Core Facilities, Istituto Superiore di Sanità, Rome, Italy.
| | - Roberta Riccioni
- Department of Oncology and Molecular Medicine, Istituto Superiore di Sanità, Rome, Italy
| | - Eleonora Petrucci
- Department of Oncology and Molecular Medicine, Istituto Superiore di Sanità, Rome, Italy.
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Analysis of the Single-Cell Heterogeneity of Adenocarcinoma Cell Lines and the Investigation of Intratumor Heterogeneity Reveals the Expression of Transmembrane Protein 45A (TMEM45A) in Lung Adenocarcinoma Cancer Patients. Cancers (Basel) 2021; 14:cancers14010144. [PMID: 35008313 PMCID: PMC8750076 DOI: 10.3390/cancers14010144] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/28/2021] [Revised: 12/14/2021] [Accepted: 12/24/2021] [Indexed: 11/25/2022] Open
Abstract
Simple Summary Non-small cell lung cancer (NSCLC) is one of the main causes of cancer-related deaths worldwide. Intratumoral heterogeneity (ITH) is responsible for the majority of difficulties encountered in the treatment of lung-cancer patients. Therefore, the heterogeneity of NSCLC cell lines and primary lung adenocarcinoma was investigated by single-cell mass cytometry (CyTOF). Human NSCLC adenocarcinoma cells A549, H1975, and H1650 were studied at single-cell resolution for the expression pattern of 13 markers: GLUT1, MCT4, CA9, TMEM45A, CD66, CD274, CD24, CD326, pan-keratin, TRA-1-60, galectin-3, galectin-1, and EGFR. The intra- and inter-cell-line heterogeneity of A549, H1975, and H1650 cells were demonstrated through hypoxic modeling. Additionally, human primary lung adenocarcinoma, and non-involved healthy lung tissue were homogenized to prepare a single-cell suspension for CyTOF analysis. The single-cell heterogeneity was confirmed using unsupervised viSNE and FlowSOM analysis. Our results also show, for the first time, that TMEM45A is expressed in lung adenocarcinoma. Abstract Intratumoral heterogeneity (ITH) is responsible for the majority of difficulties encountered in the treatment of lung-cancer patients. Therefore, the heterogeneity of NSCLC cell lines and primary lung adenocarcinoma was investigated by single-cell mass cytometry (CyTOF). First, we studied the single-cell heterogeneity of frequent NSCLC adenocarcinoma models, such as A549, H1975, and H1650. The intra- and inter-cell-line single-cell heterogeneity is represented in the expression patterns of 13 markers—namely GLUT1, MCT4, CA9, TMEM45A, CD66, CD274 (PD-L1), CD24, CD326 (EpCAM), pan-keratin, TRA-1-60, galectin-3, galectin-1, and EGFR. The qRT-PCR and CyTOF analyses revealed that a hypoxic microenvironment and altered metabolism may influence cell-line heterogeneity. Additionally, human primary lung adenocarcinoma and non-involved healthy lung tissue biopsies were homogenized to prepare a single-cell suspension for CyTOF analysis. The CyTOF showed the ITH of human primary lung adenocarcinoma for 14 markers; particularly, the higher expressions of GLUT1, MCT4, CA9, TMEM45A, and CD66 were associated with the lung-tumor tissue. Our single-cell results are the first to demonstrate TMEM45A expression in human lung adenocarcinoma, which was verified by immunohistochemistry.
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