Nonviral mcDNA-mediated bispecific CAR T cells kill tumor cells in an experimental mouse model of hepatocellular carcinoma

Abstract

Background Hepatocellular carcinoma (HCC) is one of the most common cancers worldwide and the adoptive immunotherapy of which is worth studying. CD133, a kind of cancer stem cell (CSC) antigen, together with glypican-3 (GPC3) has been proved to be highly expressed in HCC cells and both of them are used as targets to generate chimeric antigen receptor (CAR) T cells. But there are limitations like “off-target” toxicity, low transfection efficacy and weak antitumor ability in CAR T cells treatment. Methods The peripheral blood was acquired from healthy donors and T cells were separated by density-gradient centrifugation. We used an electroporation system to deliver anti-CD133 and anti-GPC3 single chain Fragment variable (scFv) structures as target genes into the T cells. The cell membrane was opened by the momentary electric current effect, and the target gene was delivered into the cell by non-viral minicircle DNA (mcDNA) vector. The flow cytometry and western blot assays were used to detect whether the two scFv were simultaneously transfected and the transfection efficacy of this bispecific CAR T cell generation method. We respectively detected the in vitro and in vivo tumor-suppression efficacy of CAR T cells through the CCK-8 assays and the HCC xenograft mice models. The CoG133-CAR T cells containing both CD133 and GPC3 antigen recognition sites were the effector cells. CD133-CAR T cells and GPC3-CAR T cells were defined as single-targeted control groups, normal T and mock T cells were defined as blank control groups. Results The mcDNA vector accommodated two target gene structures successfully transfected to generate bispecific CAR T cells. The detection methods on gene level and protein level confirmed that CoG133-CAR T cells had considerable transfection efficiency and exhibited both antigen-binding capacity of CD133 and GPC3. Compared to single-targeted CAR T cells or control T cells, CoG133-CAR T cells performed enhanced eliminated efficacy against CD133 and GPC3 double-positive HCC cell line in vitro and HCC xenograft mice in vivo. Hematoxylin and eosin (H&E) staining indicated no fatal “off-target” combination existed on CoG133-CAR T cells and major organs. Conclusion Our study suggests that it is with higher efficiency and more safety to prepare bispecific CAR T cells through non-viral mcDNA vectors. CoG133-CAR T cells have enhanced tumor-suppression capacity through dual antigen recognition and internal activation. It provides an innovative strategy for CAR T therapy of HCC, even solid tumors.

Survival Prediction Landscape: An In-Depth Systematic Literature Review on Activities, Methods, Tools, Diseases, and Databases

“…These methods are broadly categorized into five categories, namely supervised methods, incorporating L1 regularized Cox regression 29 , RSF algorithm 29 , Cox regression 103 , least absolute shrinkage and selection operator (lasso) regression 120 , cascaded Wx 105 , recursive feature elimination 38 , Boruta 31 , Akaike information criterion (AIC) regression 114 , variance 72 , lasso analysis 40 , multivariate regression 40 , Chi-squared 118 , mutual information 118 , and ANOVA 39,118 . Additionally, Network based methods include network based stratification (NBS) 83 , weighted correlation network analysis (WGCNA) 86 , canonical correlation analyses (CCA) 67 , patient similarity networks 38 , and neighborhood component analysis (NCA) 23 . Dimensionality reduction methods include non-negative matrix factorization (NMF) 40 , autoencoders (AEs) 28 , variational autoencoders (VAEs) 43 , principal component analysis (PCA) 39 , and dominant effect of the cancer driver genes (DEOD) 75,132 .…”

“…A comprehensive analysis of feature engineering methods across a range of disease-specific survival prediction studies unveils that supervised methods, such as Cox regression, L1 regularized Cox regression, and RSF algorithm, have been prevalent in diseases like ASCVD, trauma, and ovarian cancer 103,120 . On the other hand, network based methods including NBS and WGCNA, have been applied in diseases like KIRP, and hepatocellular carcinoma, which shows the significance of network structures in certain medical contexts 86 . Univariate analyses, including ANOVA, chi-squared, and univariate Cox regression, have been prevalent in diseases such as pancreatic cancer and heart failure, underscoring the significance of statistical testing in identifying relevant features 71,118 .…”

Application of adoptive cell therapy in hepatocellular carcinoma

“…To overcome single antigen CAR-T cell antigen escape, lack of affinity and off-target toxicity, two markers specifically expressed in hepatocellular carcinoma might be good choices (GPC3 and CD133). It was found that bispecific CAR-T cells (CoG133-CAR T) were successfully transformed with a nonviral minicircle DNA (mcDNA) vector and achieved good lysis results [77]. Targeting two or even multiple antigens may be a new direction for future CAR-T cell research.…”

Mechanisms and strategies for safe chimeric antigen receptor T‐cell activity control

“…117,118 This can be performed by administering a combination of two different CAR-transduced T-cell populations in a specific ratio (NCT03620058). 29,30 Another possible approach is the transduction of T-cells with two viral vectors encoding for two different CAR [30][31][32] or the transduction of TCR-specific T-cells with a CAR to generate dual-targeting T-cells. 33 In such situations, each CAR/TCR is intact and can mediate full activation upon antigen contact.…”