Chimeric antigen receptor T cell-based targeting of CD317 as a novel immunotherapeutic strategy against glioblastoma.

BACKGROUND: Chimeric antigen receptor (CAR) T cell therapy has proven to be successful against hematological malignancies. However, exploiting CAR T cells to treat solid tumors is more challenging for various reasons including the lack of suitable target antigens. Here, we identify the transmembrane protein CD317 as a novel target antigen for CAR T cell therapy against glioblastoma, one of the most aggressive solid tumors. METHODS: CD317-targeting CAR T cells were generated by lentivirally transducing human T cells from healthy donors. The anti-glioma activity of CD317-CAR T cells toward various glioma cells was assessed in vitro in cell lysis assays. Subsequently, we determined the efficacy of CD317-CAR T cells to control tumor growth in vivo in clinically relevant mouse glioma models. RESULTS: We generated CD317-specific CAR T cells and demonstrate strong anti-tumor activity against several glioma cell lines as well as primary patient-derived cells with varying CD317 expression levels in vitro. A CRISPR/Cas9-mediated knockout of CD317 protected glioma cells from CAR T cell lysis, demonstrating the target specificity of the approach. Silencing of CD317 expression in T cells by RNA interference reduced fratricide of engineered T cells and further improved their effector function. Using orthotopic glioma mouse models, we demonstrate the antigen-specific anti-tumor activity of CD317-CAR T cells, which resulted in prolonged survival and cure of a fraction of CAR T cell-treated animals. CONCLUSIONS: These data reveal a promising role of CD317-CAR T cell therapy against glioblastoma, which warrants further evaluation to translate this immunotherapeutic strategy into clinical neuro-oncology.

Mutation position is an important determinant for predicting cancer neoantigens.

Tumor-specific mutations can generate neoantigens that drive CD8 T cell responses against cancer. Next-generation sequencing and computational methods have been successfully applied to identify mutations and predict neoantigens. However, only a small fraction of predicted neoantigens are immunogenic. Currently, predicted peptide binding affinity for MHC-I is often the major criterion for prioritizing neoantigens, although little progress has been made toward understanding the precise functional relationship between affinity and immunogenicity. We therefore systematically assessed the immunogenicity of peptides containing single amino acid mutations in mouse tumor models and divided them into two classes of immunogenic mutations. The first comprises mutations at a nonanchor residue, for which we find that the predicted absolute binding affinity is predictive of immunogenicity. The second involves mutations at an anchor residue; here, predicted relative affinity (compared with the WT counterpart) is a better predictor. Incorporating these features into an immunogenicity model significantly improves neoantigen ranking. Importantly, these properties of neoantigens are also predictive in human datasets, suggesting that they can be used to prioritize neoantigens for individualized neoantigen-specific immunotherapies.