Chimeric antigen receptor (CAR) T-cells targeting CD19 demonstrate remarkable efficacy in treating B-lineage acute lymphoblastic leukemia (BL-ALL), yet up to 39% of treated patients relapse with CD19(−) disease. We report that CD19(−) escape is associated with downregulation, but preservation, of targetable expression of CD20 and CD22. Accordingly, we reasoned that broadening the spectrum of CD19CAR T-cells to include both CD20 and CD22 would enable them to target CD19(−) escape BL-ALL while preserving their upfront efficacy. We created a CD19/20/22-targeting CAR T-cell by coexpressing individual CAR molecules on a single T-cell using one tricistronic transgene. CD19/20/22CAR T-cells killed CD19(−) blasts from patients who relapsed after CD19CAR T-cell therapy and CRISPR/Cas9 CD19 knockout primary BL-ALL both in vitro and in an animal model, while CD19CAR T-cells were ineffective. At the subcellular level, CD19/20/22CAR T-cells formed dense immune synapses with target cells that mediated effective cytolytic complex formation, were efficient serial killers in single-cell tracking studies, and were as efficacious as CD19CAR T-cells against primary CD19(+) disease. In conclusion, independent of CD19 expression, CD19/20/22CAR T-cells could be used as salvage or front-line CAR therapy for patients with recalcitrant disease.
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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. 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.
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- Award ID(s):
- 2103219
- NSF-PAR ID:
- 10408434
- Date Published:
- Journal Name:
- Journal for ImmunoTherapy of Cancer
- Volume:
- 10
- Issue:
- 12
- ISSN:
- 2051-1426
- Page Range / eLocation ID:
- e005360
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Abstract CAR‐T therapy is a particularly effective treatment for some types of cancer that uses retroviruses to deliver the gene for a chimeric antigen receptor (CAR) to a patient's T cells ex vivo. The CAR enables the T cells to bind and eradicate cells with a specific surface marker (e.g., CD19+B cells) after they are transfused back into the patient. This treatment was proven to be particularly effective in treating non‐Hodgkin's lymphoma (NHL) and acute lymphoblastic leukemia (ALL), but the current CAR‐T cell manufacturing process has a few significant drawbacks. For example, while lentiviral and gammaretroviral transduction are both relatively effective, the process of producing viral vectors is time‐consuming and costly. Additionally, patients must undergo follow up appointments for several years to monitor them for any unanticipated side effects associated with the virus. Therefore, several studies have endeavored to find alternative non‐viral gene delivery methods that are less expensive, more precise, simple, and safe. This review focuses on the current state of the most promising non‐viral gene delivery techniques, including electroporation and transfection with cationic polymers or lipids.
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Abstract 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.
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null (Ed.)Background Adoptive cell therapy based on the infusion of chimeric antigen receptor (CAR) T cells has shown remarkable efficacy for the treatment of hematologic malignancies. The primary mechanism of action of these infused T cells is the direct killing of tumor cells expressing the cognate antigen. However, understanding why only some T cells are capable of killing, and identifying mechanisms that can improve killing has remained elusive. Methods To identify molecular and cellular mechanisms that can improve T-cell killing, we utilized integrated high-throughput single-cell functional profiling by microscopy, followed by robotic retrieval and transcriptional profiling. Results With the aid of mathematical modeling we demonstrate that non-killer CAR T cells comprise a heterogeneous population that arise from failure in each of the discrete steps leading to the killing. Differential transcriptional single-cell profiling of killers and non-killers identified CD137 as an inducible costimulatory molecule upregulated on killer T cells. Our single-cell profiling results directly demonstrate that inducible CD137 is feature of killer (and serial killer) T cells and this marks a different subset compared with the CD107a pos (degranulating) subset of CAR T cells. Ligation of the induced CD137 with CD137 ligand (CD137L) leads to younger CD19 CAR T cells with sustained killing and lower exhaustion. We genetically modified CAR T cells to co-express CD137L, in trans, and this lead to a profound improvement in anti-tumor efficacy in leukemia and refractory ovarian cancer models in mice. Conclusions Broadly, our results illustrate that while non-killer T cells are reflective of population heterogeneity, integrated single-cell profiling can enable identification of mechanisms that can enhance the function/proliferation of killer T cells leading to direct anti-tumor benefit.more » « less
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Abstract Objective To develop an automated, physiologic metric of immune effector cell‐associated neurotoxicity syndrome among patients undergoing chimeric antigen receptor‐T cell therapy.
Methods We conducted a retrospective observational cohort study from 2016 to 2020 at two tertiary care centers among patients receiving chimeric antigen receptor‐T cell therapy with a CD19 or B‐cell maturation antigen ligand. We determined the daily neurotoxicity grade for each patient during EEG monitoring via chart review and extracted clinical variables and outcomes from the electronic health records. Using quantitative EEG features, we developed a machine learning model to detect the presence and severity of neurotoxicity, known as the EEG immune effector cell‐associated neurotoxicity syndrome score.
Results The EEG immune effector cell‐associated neurotoxicity syndrome score significantly correlated with the grade of neurotoxicity with a median Spearman's
R 2of 0.69 (95% CI of 0.59–0.77). The mean area under receiving operator curve was greater than 0.85 for each binary discrimination level. The score also showed significant correlations with maximum ferritin (R 20.24,p = 0.008), minimum platelets (R 2–0.29,p = 0.001), and dexamethasone usage (R 20.42,p < 0.0001). The score significantly correlated with duration of neurotoxicity (R 20.31,p < 0.0001).Interpretation The EEG immune effector cell‐associated neurotoxicity syndrome score possesses high criterion, construct, and predictive validity, which substantiates its use as a physiologic method to detect the presence and severity of neurotoxicity among patients undergoing chimeric antigen receptor T‐cell therapy.
- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1136/jitc-2022-005360
