Aichi Cancer Center and NEC Corporation’s research group with Gifu University, Toyama University and Kitasato University Medical Center, have developed a method for efficiently identifying the lung cancer antigens and the antigen-specific T cells that recognize the antigens through both a single-cell analysis of Tumor Infiltrating Lymphocytes (TILs) and NEC’s AI-based antigen prediction system that predicts immune response. Our paper describing the results of this study was published on August 6, 2023 in the “Journal for ImmunoTherapy of Cancer,” which is the official journal of the Society of Immunotherapy of Cancer (SITC) in the United States.
Lung cancer is one of the most common cancers and one of the leading causes of cancer death worldwide. There are many types of cancer treatment, such as surgery, chemotherapy, radiation therapy, molecular targeted therapy, immunotherapy, and combinations of these. Recently developed immune checkpoint inhibitors (ICI) have attracted attention as a new therapy, and lung cancer is one of the most sensitive cancers to ICI, but it is effective in only a subset of individuals. Accordingly, new effective immunotherapies are required for lung cancer.
Cytotoxic T lymphocytes (CTLs) in TIL are crucial immune cells that can specifically recognize and eliminate tumor cells. Antigens targeted by CTL include patient-specific neoantigens and common antigens commonly expressed among patients such as cancer-testis antigens (CTA). In general, it is not easy to identify any antigens. If these antigens can be efficiently identified, a combination therapy with ICIs and antigen-specific immunotherapy may enhance the efficacy of treatment.
In this study by Aichi Cancer Center and NEC, we performed a single-cell analysis to determine the TILs characteristics of patients with surgically resected non-small cell lung cancer (NSCLC) (n=3) (Figure. 1). Then, we divided the TILs into 10 clusters based on gene expression profile, and identified the exhausted T cell cluster (Tex cluster) characterized by the expression of the genes called exhaustion markers (Figure. 2). We synthesized the TCRs contained in the identified exhausted T cell cluster and induced each of the TCRs into each corresponding T cell, and examined the immune responses to neoantigens predicted by NEC’s AI-based antigen prediction system and typical CTAs. It was confirmed that NEC’s AI-based antigen prediction system can accurately predict the antigens that cause the immune responses, and we identified four TCRs recognizing KK-LC-1 (one of the CTAs, *2), and five TCRs recognizing the neoantigens (Figure. 3).
In addition, by re-clustering of TCR clones (n=140) that express nine TCRs, it was discovered that even antigen-specific TCR clones have different differentiation stage and functional status among individual TCR clones (Figure 4A), and that there is a bias in differentiation and function of TCRs for each antigen (Figure 4B).
TIL single-cell analysis and AI to predict cancer antigens will facilitate the identification of lung cancer antigens and may lead to the development of personalized cancer vaccine therapies and engineered T cell therapies in NSCLC in the future.