Candidate tumor-specific CD8+ T cell subsets identified in the malignant pleural effusion of advanced lung cancer patients by single-cell analysis.

Isolation of tumor-specific T cells and their antigen receptors (TCRs) from malignant pleural effusions (MPE) may facilitate the development of TCR-transduced adoptive cellular immunotherapy products for advanced lung cancer patients. However, the characteristics and markers of tumor-specific T-cells in MPE are largely undefined. To this end, to establish the phenotypes and antigen specificities of CD8+ T cells, we performed single-cell RNA and TCR sequencing of samples from three advanced lung cancer patients. Dimensionality reduction on a total of 4,983 CD8+ T cells revealed 10 clusters including naïve, memory, and exhausted phenotypes. We focused particularly on exhausted T cell clusters and tested their TCR reactivity against neoantigens predicted from autologous cancer cell lines. Four different TCRs specific for the same neoantigen and one orphan TCR specific for the autologous cell line were identified from one of the patients. Differential gene expression analysis in tumor-specific T cells relative to the other T cells identified CXCL13, as a candidate gene expressed by tumor-specific T cells. In addition to expressing CXCL13, tumor-specific T cells were present in a higher proportion of T cells co-expressing PDCD1(PD-1)/TNFRSF9(4-1BB). Furthermore, flow cytometric analyses in advanced lung cancer patients with MPE documented that those with high PD-1/4-1BB expression have a better prognosis in the subset of 57 adenocarcinoma patients (p = .039). These data suggest that PD-1/4-1BB co-expression might identify tumor-specific CD8+ T cells in MPE, which are associated with patients' prognosis. (233 words).

Single-cell sequencing on CD8+ TILs revealed the nature of exhausted T cells recognizing neoantigen and cancer/testis antigen in non-small cell lung cancer.

BACKGROUND: CD8+tumor infiltrating lymphocytes (TILs) are often observed in non-small cell lung cancers (NSCLC). However, the characteristics of CD8+ TILs, especially T-cell populations specific for tumor antigens, remain poorly understood. METHODS: High throughput single-cell RNA sequencing and single-cell T-cell receptor (TCR) sequencing were performed on CD8+ TILs from three surgically-resected lung cancer specimens. Dimensional reduction for clustering was performed using Uniform Manifold Approximation and Projection. CD8+ TIL TCR specific for the cancer/testis antigen KK-LC-1 and for predicted neoantigens were investigated. Differentially-expressed gene analysis, Gene Set Enrichment Analysis (GSEA) and single sample GSEA was performed to characterize antigen-specific T cells. RESULTS: A total of 6998 CD8+ T cells was analyzed, divided into 10 clusters according to their gene expression profile. An exhausted T-cell (exhausted T (Tex)) cluster characterized by the expression of ENTPD1 (CD39), TOX, PDCD1 (PD1), HAVCR2 (TIM3) and other genes, and by T-cell oligoclonality, was identified. The Tex TCR repertoire (Tex-TCRs) contained nine different TCR clonotypes recognizing five tumor antigens including a KK-LC-1 antigen and four neoantigens. By re-clustering the tumor antigen-specific T cells (n=140), it could be seen that the individual T-cell clonotypes were present on cells at different stages of differentiation and functional states even within the same Tex cluster. Stimulating these T cells with predicted cognate peptide indicated that TCR signal strength and subsequent T-cell proliferation and cytokine production was variable but always higher for neoantigens than KK-LC-1. CONCLUSIONS: Our approach focusing on T cells with an exhausted phenotype among CD8+ TILs may facilitate the identification of tumor antigens and clarify the nature of the antigen-specific T cells to specify the promising immunotherapeutic targets in patients with NSCLC.

New evaluation of the tumor immune microenvironment of non-small cell lung cancer and its association with prognosis.

BACKGROUND: A better understanding of the tumor immune microenvironment (TIME) will facilitate the development of prognostic biomarkers and more effective therapeutic strategies in patients with lung cancer. However, little has been reported on the comprehensive evaluation of complex interactions among cancer cells, immune cells, and local immunosuppressive elements in the TIME. METHODS: Whole-exome sequencing and RNA sequencing were carried out on 113 lung cancers. We performed single sample gene set enrichment analysis on TIME-related gene sets to develop a new scoring system (TIME score), consisting of T-score (tumor proliferation), I-score (antitumor immunity) and S-score (immunosuppression). Lung cancers were classified according to a combination of high or low T-score, I-score, and S-scores (eight groups; G1-8). Clinical and genomic features, and immune landscape were investigated among eight groups. The external data sets of 990 lung cancers from The Cancer Genome Atlas and 76 melanomas treated with immune checkpoint inhibitors (ICI) were utilized to evaluate TIME scoring and explore prognostic and predictive accuracy. RESULTS: The representative histological type including adenocarcinoma and squamous cell carcinoma, and driver mutations such as epidermal growth factor receptor and TP53 mutations were different according to the T-score. The numbers of somatic mutations and predicted neoantigens were higher in Thi (G5-8) than Tlo (G1-4) tumors. Immune selection pressure against neoantigen expression occurred only in Thi and was dampened in Thi/Ilo (G5-6), possibly due to a reduced number of T cells with a high proportion of tumor specific but exhausted cells. Thi/Ilo/Shi (G5) displayed the lowest immune responses by additional immune suppressive mechanisms. The T-score, I-score and S-scores were independent prognostic factors, with survival curves well separated into eight groups with G5 displaying the worst overall survival, while the opposite group Tlo/Ihi/Slo (G4) had the best prognosis. Several oncogenic signaling pathways influenced on T-score and I-scores but not S-score, and PI3K pathway alteration correlated with poor prognosis in accordance with higher T-score and lower I-score. Moreover, the TIME score predicted the efficacy of ICI in patients with melanoma. CONCLUSION: The TIME score capturing complex interactions among tumor proliferation, antitumor immunity and immunosuppression could be useful for prognostic predictions or selection of treatment strategies in patients with lung cancer.

TCP: a tool for designing chimera proteins based on the tertiary structure information.

BACKGROUND: Chimera proteins are widely used for the analysis of the protein-protein interaction region. One of the major issues is the epitope analysis of the monoclonal antibody. In the analysis, a continuous portion of an antigen is sequentially substituted into a different sequence. This method works well for an antibody recognizing a linear epitope, but not for that recognizing a discontinuous epitope. Although the designing the chimera proteins based on the tertiary structure information is required in such situations, there is no appropriate tool so far. RESULTS: In light of the problem, we developed a tool named TCP (standing for a Tool for designing Chimera Proteins), which extracts some sets of mutually orthogonal cutting surfaces for designing chimera proteins using a genetic algorithm. TCP can also incorporate and consider the solvent accessible surface area information calculated by a DSSP program. The test results of our method indicate that the TCP is robust and applicable to various shapes of proteins. CONCLUSION: We developed TCP, a tool for designing chimera proteins based on the tertiary structure information. TCP is robust and possesses several favourable features, and we believe it is a useful tool for designing chimera proteins. TCP is freely available as an additional file of this manuscript for academic and non-profit organization.