Detection of ion cyclotron emission by using an ion cyclotron range of frequency antennas-based diagnostic system in experimental advanced superconducting tokamak.

In the experimental advanced superconducting tokamak (EAST), a novel ion cyclotron range of frequency (ICRF) antenna-based diagnostic system is designed to measure ion cyclotron emission (ICE) driven by high-energy ions. The diagnostic system includes ICRF antenna straps, a three-tune impedance matching system, a coaxial switching system, a direct current block, and a data acquisition and storage system. Using the coaxial switching system, the ICRF antenna can be switched from the heating mode to the coupling mode between two discharges. In the 2023 EAST experiment campaign, core ICE was observed using the ICRF antenna-based diagnostic system during neutron beam injection heating, and the obtained results agreed well with the signal detected by the previous high-frequency B-dot probe-based diagnostic system.

A bioengineered immunocompetent human leukemia chip for preclinical screening of CAR T cell immunotherapy.

Chimeric antigen receptor (CAR) T cell immunotherapy is promising for treatment of blood cancers; however, clinical benefits remain unpredictable, necessitating development of optimal CAR T cell products. Unfortunately, current preclinical evaluation platforms are inadequate due to their limited physiological relevance to humans. We herein engineered an organotypic immunocompetent chip that recapitulates microarchitectural and pathophysiological characteristics of human leukemia bone marrow stromal and immune niches for CAR T cell therapy modeling. This leukemia chip empowered real-time spatiotemporal monitoring of CAR T cell functionality, including T cell extravasation, recognition of leukemia, immune activation, cytotoxicity, and killing. We next on-chip modelled and mapped different responses post CAR T cell therapy, i.e., remission, resistance, and relapse as observed clinically and identify factors that potentially drive therapeutic failure. Finally, we developed a matrix-based analytical and integrative index to demarcate functional performance of CAR T cells with different CAR designs and generations produced from healthy donors and patients. Together, our chip introduces an enabling '(pre-)clinical-trial-on-chip' tool for CAR T cell development, which may translate to personalized therapies and improved clinical decision-making.

Spatiotemporal dissection of tumor microenvironment via in situ sensing and monitoring in tumor-on-a-chip.

Real-time monitoring in the tumor microenvironment provides critical insights of cancer progression and mechanistic understanding of responses to cancer treatments. However, clinical challenges and significant questions remain regarding assessment of limited clinical tissue samples, establishment of validated, controllable pre-clinical cancer models, monitoring of static versus dynamic markers, and the translation of insights gained from in vitro tumor microenvironments to systematic investigation and understanding in clinical practice. State-of-art tumor-on-a-chip strategies will be reviewed herein, and emerging real-time sensing and monitoring platforms for on-chip analysis of tumor microenvironment will also be examined. The integration of the sensors with tumor-on-a-chip platforms to provide spatiotemporal information of the tumor microenvironment and the associated challenges will be further evaluated. Though optimal integrated systems for in situ monitoring are still in evolution, great promises lie ahead that will open new paradigm for rapid, comprehensive analysis of cancer development and assist clinicians with powerful tools to guide the diagnosis, prognosis and treatment course in cancer.

A Computational Model of Cytokine Release Syndrome during CAR T-cell Therapy.

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 studied 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.

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.

An in silico glioblastoma microenvironment model dissects the immunological mechanisms of resistance to PD-1 checkpoint blockade immunotherapy.

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 due in large 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, we established an in-silico model 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, allowed for a comprehensive and dynamic analysis of multiple TAM-associated immunosuppression mechanisms against the anti-PD-1 immunotherapy. Our computational model demonstrated that the TAM-associated immunosuppression varied in severity across different GBM subtypes, which resulted in distinct tumor responses. Our prediction results indicated that a combination therapy co-targeting of PD-1 checkpoint and TAM-associated CSF-1R signaling could 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 would help navigate and personalize immunotherapies for GBM patients.