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Non-viral CISH locus-specific integrated FAPα CAR-T cells achieve potent anti-tumor efficacy in glioblastoma

GSE314496 Homo sapiens Expression profiling by high throughput sequencing 9 samples Submitted 2025/12/25 Platform GPL24676
Summary
CAR-T cells have been used to treat patients with glioblastoma (GBM) in clinical trial settings, by targeting GBM-associated antigens. However, the efficacy of these CAR-T cells remain limited mainly due to heterogeneous expression of tumor antigen and their anergy in tumor microenvironment (TME). Cytokine-inducible SH2-containing protein (CIS, encoded by the gene CISH) is a potent intracellular checkpoint inducing T cell anergy. Here, we identified fibroblast activation protein alpha (FAPα) as a highly attractive target for CAR-T cell therapy against GBM based on its dual expression pattern (on tumor cells and perivascular cells) in GBM. A panel of nanobodies specific for FAPα were then isolated using a yeast surface display library. FAPα-targeting CAR-T cells were developed using the isolated nanobody and verified for their specific cytotoxicity to GBM cells. Furthermore, a non-viral circular single-stranded DNA (cssDNA)-based CRISPR/Cas9 targeted genome editing (cssDNA/CRISPR/Cas9) technology was used to integrate CAR cassettes at CISH locus to generate CISH-knockout (CISH-KO) CAR-T cells. The resulted CISH-KO CAR-T cells exhibited robust proliferation and potent anti-GBM activity in vitro and in vivo compared with conventional lentivirus-transduced CAR-T cells. Our proof-of-concept study demonstrates that CISH-KO FAPα-targeting CAR-T cells engineered by non-viral locus-specific integration represent a promising therapeutic approach for GBM.
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Direct links to NCBI, no account and no request form: the whole study as GSE314496_RAW.tar, processed values as the series matrix, the supplementary file directory, and per-sample supplementary files for any of the 9 samples. Raw sequencing reads are also available from ENA.

Also filed as BioProject PRJNA1391403 and SRA study SRP656717. Searching any of these in the dataset finder brings you back here.

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