IMMU-03. EXPLORING CANCER-SPECIFIC T CELLS AND ANTIGENS IN GLIOBLASTOMA BASED ON SINGLE-CELL SEQUENCING OF CD8+ TILS

• Published in: Neuro-oncology (Charlottesville, Va.) Va.), 2024-11, Vol.26 (Supplement_8), p.viii152-viii152
• Main Authors: Okamoto, Takanari, Mizuta, Ryo, Demachi-Okamura, Ayako, Muraoka, Daisuke, Sasaki, Eiichi, Masago, Katsuhiro, Onoguchi, Kazuhide, Yamashita, Yoshiko, Muto, Osamu, Yamaguchi, Rui, Takahashi, Yoshinobu, Hashimoto, Naoya, Matsushita, Hirokazu
• Format: Article
• Language: English
• ISSN: 1522-8517; 1523-5866
• DOI: 10.1093/neuonc/noae165.0596

Abstract BACKGROUND The effectiveness of cancer immunotherapy against glioblastoma (GBM) remains limited. This study aims to identify cancer-specific antigens to develop antigen-based cancer immunotherapies for GBM. We investigated candidate tumor antigen-specific T cells in GBM by single-cell RNA sequencing (scRNA-seq) and single-cell TCR sequencing (scTCR-seq), and we explored candidate antigens through genetic analysis of tumor tissues. METHODS Flow cytometry analysis was conducted on fresh tumor digest samples to evaluate tumor-infiltrating lymphocytes (TILs) of GBM. Single-cell RNA and TCR sequencing were performed on CD8+ T cells in TILs. Both data generated by Cell Ranger software were loaded into Seurat at R studio. The dimensional reduction for clustering was performed with uniform manifold approximation and projection (UMAP). Additionally, we performed bulk RNA sequencing (RNA-seq) and whole exome sequencing (WES) on tumor tissues. RESULTS Two out of 20 patients (10%) exhibited abundant TILs in GBM. From these two patients, approximately 20,000 CD8+ T cells in total were analyzed in single-cell analysis. Clustering based on UMAP revealed a predominance of clusters expressing exhaustion markers, with high TCR clonality centered on exhausted T cell (TEX) clusters, like our previous date of lung cancer TILs recognizing tumor antigens. Moreover, within the TEX clusters, the expression of CXCL13, often reported as an antigen-specific marker in recent years, was significantly higher in our data compared to two publicly available datasets. Subsequently, based on RNA-seq and WES, 61 candidate neoantigens were estimated using machine-learning prediction algorithms from NEC Corporation. Furthermore, 5 overexpressed cancer/testis antigens, and 3 bacterial species-derived microbial peptides using Kraken2 were selected as antigen candidates. CONCLUSIONS We identified prospective tumor antigen-specific T cell subsets with high CXCL13 expression from two GBM patients. We plan to identify cancer-specific T cells and antigens from these candidates by T cell activation assay.