Oncolytic herpes simplex virus delivery of dual CAR targets of CD19 and BCMA as well as immunomodulators to enhance therapeutic efficacy in solid tumors combined with CAR T cell therapy.

Background: The CAR T-cell therapy is a promising approach to treating hematologic malignancies. However, the application in solid tumors still has many tough challenges, including heterogenicity in antigen expressions and immunosuppressive tumor microenvironment (TME). As a new cancer treatment modality, oncolytic virotherapy can be engineered to circumvent these obstacles for CAR T cell therapy in solid tumors. Methods: In this study, an oHSV T7011 is engineered to drive ectopic expression of dual-antigens, extracellular domains of CD19 and BCMA, on the solid tumor cell surface to be targeted by approved CAR T cells. In addition, multiple immunomodulators, CCL5, IL-12, and anti-PD-1 antibody are also included to modulate the TME. The antitumor activities of T7011 in combination with CD19 or BCMA CAR T-cell were evaluated in vitro and in vivo. Results: The expression of CD19 or BMCA on the tumor cell surface could be detected after T7011 infection. The level of CCL5 in TME was also increased. Efficacy studies demonstrated that combination with T7011 and CAR-TCD19 or CAR-TBCMA cells showed significant synergistic anti-tumor responses in several solid tumor models. Conclusion: These studies indicated that the new generation of oHSV T7011 can be a promising combinational therapy with CD19 or BCMA-specific CAR T cells for the treatment of a broad range of solid tumors.

Quality-aware images.

We propose the concept of quality-aware image, in which certain extracted features of the original (high-quality) image are embedded into the image data as invisible hidden messages. When a distorted version of such an image is received, users can decode the hidden messages and use them to provide an objective measure of the quality of the distorted image. To demonstrate the idea, we build a practical quality-aware image encoding, decoding and quality analysis system, which employs: 1) a novel reduced-reference image quality assessment algorithm based on a statistical model of natural images and 2) a previously developed quantization watermarking-based data hiding technique in the wavelet transform domain.