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We are analyzing https://link.springer.com/article/10.1007/s00262-023-03618-w.

Title:
A CAR-T response prediction model for r/r B-NHL patients based on a T cell subset nomogram | Cancer Immunology, Immunotherapy
Description:
Background Chimeric antigen receptor (CAR) T cells for refractory or relapsed (r/r) B cell no-Hodgkin lymphoma (NHL) patients have shown promising clinical effectiveness. However, the factors impacting the clinical response of CAR-T therapy have not been fully elucidated. We here investigate the independent influencing factors of the efficacy of CD19 CAR-T cell infusion in the treatment of r/r B-NHL and to establish an early prediction model. Methods A total of 43 r/r B-NHL patients were enrolled in this retrospective study. The patients’ general data were recorded, and the primary endpoint is the patients’ treatment response. The independent factors of complete remission (CR) and partial remission (PR) were investigated by univariate and binary logistic regression analysis, and the prediction model of the probability of CR was constructed according to the determined independent factors. Receiver operating characteristic (ROC) and calibration plot were used to assess the discrimination and calibration of the established model. Furthermore, we collected 15 participators to validate the model. Results Univariate analysis and binary logistic regression analysis of 43 patients showed that the ratio of central memory T cell (Tcm) and naïve T cell (Tn) in cytotoxic T cells (Tc) was an independent risk factor for response to CD19 CAR-T cell therapy in r/r B-NHL. On this basis, the area under the curve (AUC) of Tcm in the Tc and Tn in the Tc nomogram model was 0.914 (95%CI 0.832–0.996), the sensitivity was 83%, and the specificity was 74.2%, which had excellent predictive value. We did not found the difference of the progression-free survival (PFS). Conclusions The ratio of Tcm and Tn in Tc was found to be able to predict the treatment response of CD19 CAR-T cells in r/r B-NHL. We have established a nomogram model for the assessment of the CD19 CAR-T therapy response presented high specificity and sensitivity.
Website Age:
28 years and 1 months (reg. 1997-05-29).

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🌠 Phenomenal Traffic: 5M - 10M visitors per month


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Keywords {🔍}

cart, cells, patients, cell, treatment, response, therapy, model, article, pubmed, clinical, lymphoma, tcm, central, factors, google, scholar, proportion, blood, baseline, infusion, study, analysis, prediction, efficacy, curve, cas, early, results, predictive, tumor, fig, nomogram, car, remission, found, cohort, table, data, zhang, showed, auc, tianjin, bnhl, supplementary, cancer, univariate, logistic, roc, independent,

Topics {✒️}

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Schema {🗺️}

WebPage:
      mainEntity:
         headline:A CAR-T response prediction model for r/r B-NHL patients based on a T cell subset nomogram
         description:Chimeric antigen receptor (CAR) T cells for refractory or relapsed (r/r) B cell no-Hodgkin lymphoma (NHL) patients have shown promising clinical effectiveness. However, the factors impacting the clinical response of CAR-T therapy have not been fully elucidated. We here investigate the independent influencing factors of the efficacy of CD19 CAR-T cell infusion in the treatment of r/r B-NHL and to establish an early prediction model. A total of 43 r/r B-NHL patients were enrolled in this retrospective study. The patients’ general data were recorded, and the primary endpoint is the patients’ treatment response. The independent factors of complete remission (CR) and partial remission (PR) were investigated by univariate and binary logistic regression analysis, and the prediction model of the probability of CR was constructed according to the determined independent factors. Receiver operating characteristic (ROC) and calibration plot were used to assess the discrimination and calibration of the established model. Furthermore, we collected 15 participators to validate the model. Univariate analysis and binary logistic regression analysis of 43 patients showed that the ratio of central memory T cell (Tcm) and naïve T cell (Tn) in cytotoxic T cells (Tc) was an independent risk factor for response to CD19 CAR-T cell therapy in r/r B-NHL. On this basis, the area under the curve (AUC) of Tcm in the Tc and Tn in the Tc nomogram model was 0.914 (95%CI 0.832–0.996), the sensitivity was 83%, and the specificity was 74.2%, which had excellent predictive value. We did not found the difference of the progression-free survival (PFS). The ratio of Tcm and Tn in Tc was found to be able to predict the treatment response of CD19 CAR-T cells in r/r B-NHL. We have established a nomogram model for the assessment of the CD19 CAR-T therapy response presented high specificity and sensitivity.
         datePublished:2024-01-27T00:00:00Z
         dateModified:2024-01-27T00:00:00Z
         pageStart:1
         pageEnd:11
         license:http://creativecommons.org/licenses/by/4.0/
         sameAs:https://doi.org/10.1007/s00262-023-03618-w
         keywords:
            Refractory/relapsed B cell non-Hodgkin’s lymphoma
            Chimeric antigen receptor T cells
            Predictive models
            Treatment response
            Oncology
            Immunology
            Cancer Research
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                        type:PostalAddress
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                        name:First Central Clinical College, Tianjin Medical University, Tianjin, China
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               name:Xingzhong Liu
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                        name:Department of Hematology, Tianjin First Central Hospital, School of Medicine, Nankai University, Tianjin, China
                        type:PostalAddress
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ScholarlyArticle:
      headline:A CAR-T response prediction model for r/r B-NHL patients based on a T cell subset nomogram
      description:Chimeric antigen receptor (CAR) T cells for refractory or relapsed (r/r) B cell no-Hodgkin lymphoma (NHL) patients have shown promising clinical effectiveness. However, the factors impacting the clinical response of CAR-T therapy have not been fully elucidated. We here investigate the independent influencing factors of the efficacy of CD19 CAR-T cell infusion in the treatment of r/r B-NHL and to establish an early prediction model. A total of 43 r/r B-NHL patients were enrolled in this retrospective study. The patients’ general data were recorded, and the primary endpoint is the patients’ treatment response. The independent factors of complete remission (CR) and partial remission (PR) were investigated by univariate and binary logistic regression analysis, and the prediction model of the probability of CR was constructed according to the determined independent factors. Receiver operating characteristic (ROC) and calibration plot were used to assess the discrimination and calibration of the established model. Furthermore, we collected 15 participators to validate the model. Univariate analysis and binary logistic regression analysis of 43 patients showed that the ratio of central memory T cell (Tcm) and naïve T cell (Tn) in cytotoxic T cells (Tc) was an independent risk factor for response to CD19 CAR-T cell therapy in r/r B-NHL. On this basis, the area under the curve (AUC) of Tcm in the Tc and Tn in the Tc nomogram model was 0.914 (95%CI 0.832–0.996), the sensitivity was 83%, and the specificity was 74.2%, which had excellent predictive value. We did not found the difference of the progression-free survival (PFS). The ratio of Tcm and Tn in Tc was found to be able to predict the treatment response of CD19 CAR-T cells in r/r B-NHL. We have established a nomogram model for the assessment of the CD19 CAR-T therapy response presented high specificity and sensitivity.
      datePublished:2024-01-27T00:00:00Z
      dateModified:2024-01-27T00:00:00Z
      pageStart:1
      pageEnd:11
      license:http://creativecommons.org/licenses/by/4.0/
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      keywords:
         Refractory/relapsed B cell non-Hodgkin’s lymphoma
         Chimeric antigen receptor T cells
         Predictive models
         Treatment response
         Oncology
         Immunology
         Cancer Research
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                     name:First Central Clinical College, Tianjin Medical University, Tianjin, China
                     type:PostalAddress
                  type:Organization
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                     name:First Central Clinical College, Tianjin Medical University, Tianjin, China
                     type:PostalAddress
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                     type:PostalAddress
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            name:Cuicui Lyu
            affiliation:
                  name:Nankai University
                  address:
                     name:Department of Hematology, Tianjin First Central Hospital, School of Medicine, Nankai University, Tianjin, China
                     type:PostalAddress
                  type:Organization
            email:[email protected]
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            name:Mingfeng Zhao
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            address:
               name:First Central Clinical College, Tianjin Medical University, Tianjin, China
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            name:Tianjin Medical University
            address:
               name:First Central Clinical College, Tianjin Medical University, Tianjin, China
               type:PostalAddress
            type:Organization
      name:Yi Zhang
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            name:Tianjin Medical University
            address:
               name:First Central Clinical College, Tianjin Medical University, Tianjin, China
               type:PostalAddress
            type:Organization
      name:Xingzhong Liu
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            name:Nankai University
            address:
               name:State Key Laboratory of Medicinal Chemical Biology, Key Laboratory of Molecular Microbiology and Technology of the Ministry of Education, Department of Microbiology, College of Life Sciences, Nankai University, Tianjin, China
               type:PostalAddress
            type:Organization
      name:Cuicui Lyu
      affiliation:
            name:Nankai University
            address:
               name:Department of Hematology, Tianjin First Central Hospital, School of Medicine, Nankai University, Tianjin, China
               type:PostalAddress
            type:Organization
      email:[email protected]
      name:Mingfeng Zhao
      affiliation:
            name:Nankai University
            address:
               name:Department of Hematology, Tianjin First Central Hospital, School of Medicine, Nankai University, Tianjin, China
               type:PostalAddress
            type:Organization
      email:[email protected]
PostalAddress:
      name:School of Medicine, Nankai University, Tianjin, China
      name:School of Medicine, Nankai University, Tianjin, China
      name:First Central Clinical College, Tianjin Medical University, Tianjin, China
      name:First Central Clinical College, Tianjin Medical University, Tianjin, China
      name:First Central Clinical College, Tianjin Medical University, Tianjin, China
      name:First Central Clinical College, Tianjin Medical University, Tianjin, China
      name:First Central Clinical College, Tianjin Medical University, Tianjin, China
      name:State Key Laboratory of Medicinal Chemical Biology, Key Laboratory of Molecular Microbiology and Technology of the Ministry of Education, Department of Microbiology, College of Life Sciences, Nankai University, Tianjin, China
      name:Department of Hematology, Tianjin First Central Hospital, School of Medicine, Nankai University, Tianjin, China
      name:Department of Hematology, Tianjin First Central Hospital, School of Medicine, Nankai University, Tianjin, China

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