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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.

 

B cell acute lymphoblastic leukemia (B-ALL) blasts hijack the bone marrow (BM) microenvironment to form chemoprotective leukemic BM “niches,” facilitating chemoresistance and, ultimately, disease relapse. However, the ability to dissect these evolving, heterogeneous interactions among distinct B-ALL subtypes and their varying BM niches is limited with current in vivo methods. Here, we demonstrated an in vitro organotypic “leukemia-on-a-chip” model to emulate the in vivo B-ALL BM pathology and comparatively studied the spatial and genetic heterogeneity of the BM niche in regulating B-ALL chemotherapy resistance. We revealed the heterogeneous chemoresistance mechanisms across various B-ALL cell lines and patient-derived samples. We showed that the leukemic perivascular, endosteal, and hematopoietic niche-derived factors maintain B-ALL survival and quiescence (e.g., CXCL12 cytokine signal, VCAM-1/OPN adhesive signals, and enhanced downstream leukemia-intrinsic NF-κB pathway). Furthermore, we demonstrated the preclinical use of our model to test niche-cotargeting regimens, which may translate to patient-specific therapy screening and response prediction.