Computational model of CAR T-cell immunotherapy dissects and predicts leukemia patient responses at remission, resistance, and relapse.
Computational model of CAR T-cell immunotherapy dissects and predicts leukemia patient responses at remission, resistance, and relapse.
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DOI：
10.1136/jitc-2022-005360
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发表时间：
2022-12
影响因子：
10.9
通讯作者：
中科院分区：
文献类型：
--
作者：
适应性CD19靶向嵌合抗原受体（CAR）T细胞转移已成为白血病的一种有前途的治疗方法。尽管不同临床试验的患者反应不同，但目前缺乏分析和预测患者对新疗法反应的可靠方法。最近，患者反应的描述已经使用计算机模拟计算模型实现，预测应用受到限制。我们建立了一个CAR T细胞治疗的计算模型，以概括治疗期间的关键细胞机制和动力学，包括持续缓解（CR），无应答（NR）以及CD 19阳性（CD 19+）和CD 19阴性（CD 19 −）复发。从临床研究中收集了209名患者的实时CAR T细胞和肿瘤负荷数据，并使用骨髓中的统一单位进行标准化。使用非线性混合效应建模的随机近似期望最大化算法进行参数估计。我们揭示了与缓解、耐药和复发时患者反应相关的关键决定因素。对于CR、NR和CD 19+复发，CAR T细胞的总体功能性导致了各种结局，而CD 19+抗原的丧失和CAR T细胞的旁观者杀伤效应可能部分解释了CD 19 −复发的进展。此外，我们通过结合CAR T细胞的峰值和累积值或通过输入早期CAR T细胞动力学来预测患者的反应。使用基于真实的临床患者数据集生成的虚拟患者队列进行临床试验模拟，以进一步验证预测。我们的模型剖析了白血病对CAR T细胞治疗的不同反应背后的机制。这种基于患者的计算免疫肿瘤学模型可以预测晚期反应，并可能在临床治疗和管理中提供信息。
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. 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. 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. 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.
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影响因子：
17.1
作者：
Chmielecki J;Foo J;Oxnard GR;Hutchinson K;Ohashi K;Somwar R;Wang L;Amato KR;Arcila M;Sos ML;Socci ND;Viale A;de Stanchina E;Ginsberg MS;Thomas RK;Kris MG;Inoue A;Ladanyi M;Miller VA;Michor F;Pao W
通讯作者：
Pao W
影响因子：
17.1
作者：
Brentjens RJ;Davila ML;Riviere I;Park J;Wang X;Cowell LG;Bartido S;Stefanski J;Taylor C;Olszewska M;Borquez-Ojeda O;Qu J;Wasielewska T;He Q;Bernal Y;Rijo IV;Hedvat C;Kobos R;Curran K;Steinherz P;Jurcic J;Rosenblat T;Maslak P;Frattini M;Sadelain M
通讯作者：
Sadelain M
影响因子：
20.3
作者：
Gardner, Rebecca A.;Finney, Olivia;Jensen, Michael C.
通讯作者：
Jensen, Michael C.
影响因子：
20.3
作者：
Gardner, Rebecca;Wu, David;Turtle, Cameron J.
通讯作者：
Turtle, Cameron J.
影响因子：
6.7
作者：
Holford, N.;Ma, S. C.;Ploeger, B. A.
通讯作者：
Ploeger, B. A.