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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. 

Cytokine release syndrome (CRS) is a lethal adverse event in chimeric antigen receptor (CAR) T‐cell therapy, hindering this promising therapy for cancers, such as B‐cell acute lymphoblastic leukemia (B‐ALL). Clinical management of CRS requires a better understanding of its underlying mechanisms. In this study, a computational model of CRS during CAR T‐cell therapy is built to depict how the cellular interactions among CAR T‐cells, B‐ALL cells, and bystander monocytes, as well as the accompanying molecular interactions among various inflammatory cytokines, influence the severity of CRS. The model successfully defines the factors related to severe CRS and studies the effects of immunomodulatory therapy on CRS. The use of the model is also demonstrated as a precision medicine tool to optimize the treatment scheme, including personalized choice of CAR T‐cell products and control of switchable CAR T‐cell activity, for a more efficient and safer immunotherapy. This new computational oncology model can serve as a precision medicine tool to guide the clinical management of CRS during CAR T cell therapy.

 

The PD‐1 immune checkpoint‐based therapy has emerged as a promising therapy strategy for treating the malignant brain tumor glioblastoma (GBM). However, patient response varies in clinical trials, mainly due to the tumor heterogeneity and immunological resistance in the tumor microenvironment. To further understand how mechanistically the niche interplay and competition drive anti‐PD‐1 resistance, an in silico model is established to quantitatively describe the biological rationale of critical GBM‐immune interactions, such as tumor growth and apoptosis, T cell activation and cytotoxicity, and tumor‐associated macrophage (TAM) mediated immunosuppression. Such an in silico experimentation and predictive model, based on the in vitro microfluidic chip‐measured end‐point data and patient‐specific immunological characteristics, allows for a comprehensive and dynamic analysis of multiple TAM‐associated immunosuppression mechanisms against the anti‐PD‐1 immunotherapy. The computational model demonstrates that the TAM‐associated immunosuppression varies in severity across different GBM subtypes, which results in distinct tumor responses. The prediction results indicate that a combination therapy by co‐targeting of PD‐1 checkpoint and TAM‐associated CSF‐1R signaling can enhance the immune responses of GBM patients, especially those patients with mesenchymal GBM who are irresponsive to the single anti‐PD‐1 therapy. The development of a patient‐specific in silico–in vitro GBM model will help navigate and personalize immunotherapies for GBM patients.