Background: T cells exhibit heterogeneous functional states in the tumor microenvironment. Immune checkpoint inhibitors (ICIs) can reinvigorate only the stem cell-like progenitor exhausted T cells, which suggests that inhibiting the exhaustion progress will improve the efficacy of immunotherapy. Thus, regulatory factors promoting T cell exhaustion could serve as potential targets for delaying the process and improving ICI efficacy. Methods: We analyzed the single-cell transcriptome data derived from human melanoma and non-small cell lung cancer (NSCLC) samples and classified the tumor-infiltrating (TI) CD8 + T cell population based on PDCD1 (PD-1) levels, i.e., PDCD1-high and PDCD1-low cells. Additionally, we identified differentially expressed genes as candidate factors regulating intra-tumoral T cell exhaustion. The co-expression of candidate genes with immune checkpoint (IC) molecules in the TI CD8 + T cells was confirmed by single-cell trajectory and flow cytometry analyses. The lossof-function effect of the candidate regulator was examined by a cell-based knockdown assay. The clinical effect of the candidate regulator was evaluated based on the overall survival and anti-PD-1 responses. Results:We retrieved many known factors for regulating T cell exhaustion among the differentially expressed genes between PDCD1-high and PDCD1-low subsets of the TI CD8 + T cells in human melanoma and NSCLC. TOX was the only transcription factor (TF) predicted in both tumor types. TOX levels tend to increase as CD8 + T cells become more exhausted. Flow cytometry analysis revealed a correlation between TOX expression and severity of intra-tumoral T cell exhaustion. TOX knockdown in the human TI CD8 + T cells resulted in downregulation of PD-1, TIM-3, TIGIT, and CTLA-4, which suggests that TOX promotes intra-tumoral T cell exhaustion by upregulating IC proteins in cancer. Finally, the TOX level in the TI T cells was found to be highly predictive of overall survival and anti-PD-1 efficacy in melanoma and NSCLC.

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Inhibition of immune checkpoint proteins like programmed death 1 (PD-1) is a promising therapeutic approach for several cancers, including non-small cell lung cancer (NSCLC). Although PD-1 ligand (PD-L1) expression is used to predict anti-PD-1 therapy responses in NSCLC, its accuracy is relatively less. Therefore, we sought to identify a more accurate predictive blood biomarker for evaluating anti-PD-1 response. We evaluated the frequencies of T cells, B cells, natural killer (NK) cells, polymorphonuclear myeloid-derived suppressor cells (PMN-MDSCs), mononuclear myeloid-derived suppressor cells (M-MDSCs), and Lox-1+ PMN-MDSCs in peripheral blood samples of 62 NSCLC patients before and after nivolumab treatment. Correlation of immune-cell population frequencies with treatment response, progression-free survival, and overall survival was also determined. After the first treatment, the median NK cell percentage was significantly higher in responders than in non-responders, while the median Lox-1+ PMN-MDSC percentage showed the opposite trend. NK cell frequencies significantly increased in responders but not in non-responders. NK cell frequency inversely correlated with that of Lox-1+ PMN-MDSCs after the first treatment cycle. The NK cell-to-Lox-1+ PMN-MDSC ratio (NMR) was significantly higher in responders than in non-responders. Patients with NMRs ≥ 5.75 after the first cycle had significantly higher objective response rates and longer progression-free and overall survival than those with NMRs <5.75. NMR shows promise as an early predictor of response to further anti-PD-1 therapy.

Mass spectrometric (MS) data of human cell secretomes are usually run through the conventional human database for identification. However, the search may result in false identifications due to contamination of the secretome with fetal bovine serum (FBS) proteins. To overcome this challenge, here we provide a composite protein database including human as well as 199 FBS protein sequences for MS data search of human cell secretomes. Searching against the human-FBS database returned more reliable results with fewer false-positive and false-negative identifications compared to using either a human only database or a human-bovine database. Furthermore, the improved results validated our strategy without complex experiments like SILAC. We expect our strategy to improve the accuracy of human secreted protein identification and to also add value for general use.