Computational model of CAR T-cell immunotherapy dissects and predicts leukemia patient responses at remission, resistance, and relapse
Background Adaptive CD19-targeted chimeric antigen receptor (CAR) T-cell transfer has become a promising treatment for leukemia. Although patient responses vary across different clinical trials, reliable methods to dissect and predict patient responses to novel therapies are currently lacking. Recen...
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| Language: | English |
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BMJ Publishing Group
2022-12-01
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| Series: | Journal for ImmunoTherapy of Cancer |
| Online Access: | https://jitc.bmj.com/content/10/12/e005360.full |
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| author | Chao Ma Weiqiang Chen Lunan Liu Zhuoyu Zhang Matthew T Witkowski Iannis Aifantis Saba Ghassemi |
| author_facet | Chao Ma Weiqiang Chen Lunan Liu Zhuoyu Zhang Matthew T Witkowski Iannis Aifantis Saba Ghassemi |
| author_sort | Chao Ma |
| collection | DOAJ |
| description | Background Adaptive CD19-targeted chimeric antigen receptor (CAR) T-cell transfer has become a promising treatment for leukemia. Although patient responses vary across different clinical trials, reliable methods to dissect and predict patient responses to novel therapies are currently lacking. Recently, the depiction of patient responses has been achieved using in silico computational models, with prediction application being limited.Methods We established a computational model of CAR T-cell therapy to recapitulate key cellular mechanisms and dynamics during treatment with responses of continuous remission (CR), non-response (NR), and CD19-positive (CD19+) and CD19-negative (CD19−) relapse. Real-time CAR T-cell and tumor burden data of 209 patients were collected from clinical studies and standardized with unified units in bone marrow. Parameter estimation was conducted using the stochastic approximation expectation maximization algorithm for nonlinear mixed-effect modeling.Results We revealed critical determinants related to patient responses at remission, resistance, and relapse. For CR, NR, and CD19+ relapse, the overall functionality of CAR T-cell led to various outcomes, whereas loss of the CD19+ antigen and the bystander killing effect of CAR T-cells may partly explain the progression of CD19− relapse. Furthermore, we predicted patient responses by combining the peak and accumulated values of CAR T-cells or by inputting early-stage CAR T-cell dynamics. A clinical trial simulation using virtual patient cohorts generated based on real clinical patient datasets was conducted to further validate the prediction.Conclusions Our model dissected the mechanism behind distinct responses of leukemia to CAR T-cell therapy. This patient-based computational immuno-oncology model can predict late responses and may be informative in clinical treatment and management. |
| format | Article |
| id | doaj-art-0f7bed6b23064c45b3867d327a93ca03 |
| institution | Kabale University |
| issn | 2051-1426 |
| language | English |
| publishDate | 2022-12-01 |
| publisher | BMJ Publishing Group |
| record_format | Article |
| series | Journal for ImmunoTherapy of Cancer |
| spelling | doaj-art-0f7bed6b23064c45b3867d327a93ca032024-11-24T04:40:09ZengBMJ Publishing GroupJournal for ImmunoTherapy of Cancer2051-14262022-12-01101210.1136/jitc-2022-005360Computational model of CAR T-cell immunotherapy dissects and predicts leukemia patient responses at remission, resistance, and relapseChao Ma0Weiqiang Chen1Lunan Liu2Zhuoyu Zhang3Matthew T Witkowski4Iannis Aifantis5Saba Ghassemi6Department of Mechanical and Aerospace Engineering, New York University, Brooklyn, New York, USADepartment of Anesthesiology, Shantou Central Hospital, Shantou, Guangdong, ChinaDepartment of Mechanical and Aerospace Engineering, New York University, Brooklyn, New York, USADepartment of Mechanical and Aerospace Engineering, New York University, Brooklyn, New York, USAPerlmutter Cancer Center, NYU Langone Health, New York City, New York, USAPerlmutter Cancer Center, NYU Langone Health, New York City, New York, USA1Center for Cellular Immunotherapies, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA, USABackground Adaptive CD19-targeted chimeric antigen receptor (CAR) T-cell transfer has become a promising treatment for leukemia. Although patient responses vary across different clinical trials, reliable methods to dissect and predict patient responses to novel therapies are currently lacking. Recently, the depiction of patient responses has been achieved using in silico computational models, with prediction application being limited.Methods We established a computational model of CAR T-cell therapy to recapitulate key cellular mechanisms and dynamics during treatment with responses of continuous remission (CR), non-response (NR), and CD19-positive (CD19+) and CD19-negative (CD19−) relapse. Real-time CAR T-cell and tumor burden data of 209 patients were collected from clinical studies and standardized with unified units in bone marrow. Parameter estimation was conducted using the stochastic approximation expectation maximization algorithm for nonlinear mixed-effect modeling.Results We revealed critical determinants related to patient responses at remission, resistance, and relapse. For CR, NR, and CD19+ relapse, the overall functionality of CAR T-cell led to various outcomes, whereas loss of the CD19+ antigen and the bystander killing effect of CAR T-cells may partly explain the progression of CD19− relapse. Furthermore, we predicted patient responses by combining the peak and accumulated values of CAR T-cells or by inputting early-stage CAR T-cell dynamics. A clinical trial simulation using virtual patient cohorts generated based on real clinical patient datasets was conducted to further validate the prediction.Conclusions Our model dissected the mechanism behind distinct responses of leukemia to CAR T-cell therapy. This patient-based computational immuno-oncology model can predict late responses and may be informative in clinical treatment and management.https://jitc.bmj.com/content/10/12/e005360.full |
| spellingShingle | Chao Ma Weiqiang Chen Lunan Liu Zhuoyu Zhang Matthew T Witkowski Iannis Aifantis Saba Ghassemi Computational model of CAR T-cell immunotherapy dissects and predicts leukemia patient responses at remission, resistance, and relapse Journal for ImmunoTherapy of Cancer |
| title | Computational model of CAR T-cell immunotherapy dissects and predicts leukemia patient responses at remission, resistance, and relapse |
| title_full | Computational model of CAR T-cell immunotherapy dissects and predicts leukemia patient responses at remission, resistance, and relapse |
| title_fullStr | Computational model of CAR T-cell immunotherapy dissects and predicts leukemia patient responses at remission, resistance, and relapse |
| title_full_unstemmed | Computational model of CAR T-cell immunotherapy dissects and predicts leukemia patient responses at remission, resistance, and relapse |
| title_short | Computational model of CAR T-cell immunotherapy dissects and predicts leukemia patient responses at remission, resistance, and relapse |
| title_sort | computational model of car t cell immunotherapy dissects and predicts leukemia patient responses at remission resistance and relapse |
| url | https://jitc.bmj.com/content/10/12/e005360.full |
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