Flow Cytometric Pattern of TCRVδ Subtype Expression Rapidly Identifies γδT Cell Lymphoma
Background: γδT cell lymphoma (γδ TCL) is a class of hematopoietic malignancy that expresses the γδ T cell receptor (TCR) with a low incidence. Determining the clonal proliferation of γδT cells is important for the diagnosis of such malignancies. Few studies have used flow cytometry to detect VδTCR...
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| Format: | Article |
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Frontiers Media S.A.
2020-06-01
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| Series: | Frontiers in Oncology |
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| Online Access: | https://www.frontiersin.org/article/10.3389/fonc.2020.00844/full |
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| author | Xiao Chen Xiao Chen Xiao Chen Sishu Zhao Sishu Zhao Sishu Zhao Lu Liu Lu Liu Lu Liu Chun Qiao Chun Qiao Chun Qiao Yan Wang Yan Wang Yan Wang Lei Fan Lei Fan Lei Fan Huimin Jin Huimin Jin Huimin Jin Yujie Wu Yujie Wu Yujie Wu |
| author_facet | Xiao Chen Xiao Chen Xiao Chen Sishu Zhao Sishu Zhao Sishu Zhao Lu Liu Lu Liu Lu Liu Chun Qiao Chun Qiao Chun Qiao Yan Wang Yan Wang Yan Wang Lei Fan Lei Fan Lei Fan Huimin Jin Huimin Jin Huimin Jin Yujie Wu Yujie Wu Yujie Wu |
| author_sort | Xiao Chen |
| collection | DOAJ |
| description | Background: γδT cell lymphoma (γδ TCL) is a class of hematopoietic malignancy that expresses the γδ T cell receptor (TCR) with a low incidence. Determining the clonal proliferation of γδT cells is important for the diagnosis of such malignancies. Few studies have used flow cytometry to detect VδTCR and its subtypes (Vδ1 and Vδ2) at the protein level, although it is a practical method for determining the neoplastic γδT cells.Methods: A TCRVδ-based 10-color protocol was designed for the detection of malignant proliferation of γδT subtype cells by multiparameter flow cytometry, and the diagnostic results were compared with the gene rearrangement results.Results: All 19 cases of γδ TCL were positive for cluster of differentiation 3 (CD3) and TCR γδ and presented with abnormal distribution patterns of Vδ1 and Vδ2, of which 16 of the 19 cases showed a restricted Vδ1 staining pattern and the remaining three cases lacked the expression of either Vδ1 or Vδ2. Among the 10 normal controls and 11 patients with reactively higher CD4 and CD8 double-negative ratio, the percentage of Vδ2 positive events (range: 16.4–99.0%) was significantly higher than that of Vδ1 (range: 0–50.5%; p < 0.0001), and all cases had a normal Vδ distribution pattern. To detect clonality, there was no difference in the detection rate between the TCRVδ analysis and the gene scanning techniques (p = 1.000) with a high degree of coincidence (Kappa = 0.850, p < 0.001). The heteroduplex analysis was less sensitive than the other methods but was more specific (100%) than the gene scanning techniques, and the TCRVδ subtype analysis had the highest sensitivity, specificity, positive predictive value, and negative predictive value. Compared with molecular methods, immunophenotyping is able to distinguish the T cell lineage.Conclusion: The γδT panel, based on the TCRVδ antibody by flow cytometry, could be advantageous for the rapid identification of suspected γδTCL. |
| first_indexed | 2024-12-13T00:46:09Z |
| format | Article |
| id | doaj.art-0a72165951e045cb925ec3272718e047 |
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| language | English |
| last_indexed | 2024-12-13T00:46:09Z |
| publishDate | 2020-06-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Oncology |
| spelling | doaj.art-0a72165951e045cb925ec3272718e0472022-12-22T00:05:01ZengFrontiers Media S.A.Frontiers in Oncology2234-943X2020-06-011010.3389/fonc.2020.00844508492Flow Cytometric Pattern of TCRVδ Subtype Expression Rapidly Identifies γδT Cell LymphomaXiao Chen0Xiao Chen1Xiao Chen2Sishu Zhao3Sishu Zhao4Sishu Zhao5Lu Liu6Lu Liu7Lu Liu8Chun Qiao9Chun Qiao10Chun Qiao11Yan Wang12Yan Wang13Yan Wang14Lei Fan15Lei Fan16Lei Fan17Huimin Jin18Huimin Jin19Huimin Jin20Yujie Wu21Yujie Wu22Yujie Wu23Department of Hematology, Jiangsu Province Hospital, The First Affiliated Hospital of Nanjing Medical University, Nanjing, ChinaKey Laboratory of Hematology of Nanjing Medical University, Nanjing, ChinaCollaborative Innovation Center for Cancer Personalized Medicine, Nanjing, ChinaDepartment of Hematology, Jiangsu Province Hospital, The First Affiliated Hospital of Nanjing Medical University, Nanjing, ChinaKey Laboratory of Hematology of Nanjing Medical University, Nanjing, ChinaCollaborative Innovation Center for Cancer Personalized Medicine, Nanjing, ChinaDepartment of Hematology, Jiangsu Province Hospital, The First Affiliated Hospital of Nanjing Medical University, Nanjing, ChinaKey Laboratory of Hematology of Nanjing Medical University, Nanjing, ChinaCollaborative Innovation Center for Cancer Personalized Medicine, Nanjing, ChinaDepartment of Hematology, Jiangsu Province Hospital, The First Affiliated Hospital of Nanjing Medical University, Nanjing, ChinaKey Laboratory of Hematology of Nanjing Medical University, Nanjing, ChinaCollaborative Innovation Center for Cancer Personalized Medicine, Nanjing, ChinaDepartment of Hematology, Jiangsu Province Hospital, The First Affiliated Hospital of Nanjing Medical University, Nanjing, ChinaKey Laboratory of Hematology of Nanjing Medical University, Nanjing, ChinaCollaborative Innovation Center for Cancer Personalized Medicine, Nanjing, ChinaDepartment of Hematology, Jiangsu Province Hospital, The First Affiliated Hospital of Nanjing Medical University, Nanjing, ChinaKey Laboratory of Hematology of Nanjing Medical University, Nanjing, ChinaCollaborative Innovation Center for Cancer Personalized Medicine, Nanjing, ChinaDepartment of Hematology, Jiangsu Province Hospital, The First Affiliated Hospital of Nanjing Medical University, Nanjing, ChinaKey Laboratory of Hematology of Nanjing Medical University, Nanjing, ChinaCollaborative Innovation Center for Cancer Personalized Medicine, Nanjing, ChinaDepartment of Hematology, Jiangsu Province Hospital, The First Affiliated Hospital of Nanjing Medical University, Nanjing, ChinaKey Laboratory of Hematology of Nanjing Medical University, Nanjing, ChinaCollaborative Innovation Center for Cancer Personalized Medicine, Nanjing, ChinaBackground: γδT cell lymphoma (γδ TCL) is a class of hematopoietic malignancy that expresses the γδ T cell receptor (TCR) with a low incidence. Determining the clonal proliferation of γδT cells is important for the diagnosis of such malignancies. Few studies have used flow cytometry to detect VδTCR and its subtypes (Vδ1 and Vδ2) at the protein level, although it is a practical method for determining the neoplastic γδT cells.Methods: A TCRVδ-based 10-color protocol was designed for the detection of malignant proliferation of γδT subtype cells by multiparameter flow cytometry, and the diagnostic results were compared with the gene rearrangement results.Results: All 19 cases of γδ TCL were positive for cluster of differentiation 3 (CD3) and TCR γδ and presented with abnormal distribution patterns of Vδ1 and Vδ2, of which 16 of the 19 cases showed a restricted Vδ1 staining pattern and the remaining three cases lacked the expression of either Vδ1 or Vδ2. Among the 10 normal controls and 11 patients with reactively higher CD4 and CD8 double-negative ratio, the percentage of Vδ2 positive events (range: 16.4–99.0%) was significantly higher than that of Vδ1 (range: 0–50.5%; p < 0.0001), and all cases had a normal Vδ distribution pattern. To detect clonality, there was no difference in the detection rate between the TCRVδ analysis and the gene scanning techniques (p = 1.000) with a high degree of coincidence (Kappa = 0.850, p < 0.001). The heteroduplex analysis was less sensitive than the other methods but was more specific (100%) than the gene scanning techniques, and the TCRVδ subtype analysis had the highest sensitivity, specificity, positive predictive value, and negative predictive value. Compared with molecular methods, immunophenotyping is able to distinguish the T cell lineage.Conclusion: The γδT panel, based on the TCRVδ antibody by flow cytometry, could be advantageous for the rapid identification of suspected γδTCL.https://www.frontiersin.org/article/10.3389/fonc.2020.00844/fullTCRγδ T cell lymphomaVδ1Vδ2flow cytometry |
| spellingShingle | Xiao Chen Xiao Chen Xiao Chen Sishu Zhao Sishu Zhao Sishu Zhao Lu Liu Lu Liu Lu Liu Chun Qiao Chun Qiao Chun Qiao Yan Wang Yan Wang Yan Wang Lei Fan Lei Fan Lei Fan Huimin Jin Huimin Jin Huimin Jin Yujie Wu Yujie Wu Yujie Wu Flow Cytometric Pattern of TCRVδ Subtype Expression Rapidly Identifies γδT Cell Lymphoma Frontiers in Oncology TCR γδ T cell lymphoma Vδ1 Vδ2 flow cytometry |
| title | Flow Cytometric Pattern of TCRVδ Subtype Expression Rapidly Identifies γδT Cell Lymphoma |
| title_full | Flow Cytometric Pattern of TCRVδ Subtype Expression Rapidly Identifies γδT Cell Lymphoma |
| title_fullStr | Flow Cytometric Pattern of TCRVδ Subtype Expression Rapidly Identifies γδT Cell Lymphoma |
| title_full_unstemmed | Flow Cytometric Pattern of TCRVδ Subtype Expression Rapidly Identifies γδT Cell Lymphoma |
| title_short | Flow Cytometric Pattern of TCRVδ Subtype Expression Rapidly Identifies γδT Cell Lymphoma |
| title_sort | flow cytometric pattern of tcrvδ subtype expression rapidly identifies γδt cell lymphoma |
| topic | TCR γδ T cell lymphoma Vδ1 Vδ2 flow cytometry |
| url | https://www.frontiersin.org/article/10.3389/fonc.2020.00844/full |
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