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      Dynamics and specificities of T cells in cancer immunotherapy

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      Nature Reviews Cancer
      Springer Science and Business Media LLC

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          Dissecting the multicellular ecosystem of metastatic melanoma by single-cell RNA-seq.

          To explore the distinct genotypic and phenotypic states of melanoma tumors, we applied single-cell RNA sequencing (RNA-seq) to 4645 single cells isolated from 19 patients, profiling malignant, immune, stromal, and endothelial cells. Malignant cells within the same tumor displayed transcriptional heterogeneity associated with the cell cycle, spatial context, and a drug-resistance program. In particular, all tumors harbored malignant cells from two distinct transcriptional cell states, such that tumors characterized by high levels of the MITF transcription factor also contained cells with low MITF and elevated levels of the AXL kinase. Single-cell analyses suggested distinct tumor microenvironmental patterns, including cell-to-cell interactions. Analysis of tumor-infiltrating T cells revealed exhaustion programs, their connection to T cell activation and clonal expansion, and their variability across patients. Overall, we begin to unravel the cellular ecosystem of tumors and how single-cell genomics offers insights with implications for both targeted and immune therapies.
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            Molecular and genetic properties of tumors associated with local immune cytolytic activity.

            How the genomic landscape of a tumor shapes and is shaped by anti-tumor immunity has not been systematically explored. Using large-scale genomic data sets of solid tissue tumor biopsies, we quantified the cytolytic activity of the local immune infiltrate and identified associated properties across 18 tumor types. The number of predicted MHC Class I-associated neoantigens was correlated with cytolytic activity and was lower than expected in colorectal and other tumors, suggesting immune-mediated elimination. We identified recurrently mutated genes that showed positive association with cytolytic activity, including beta-2-microglobulin (B2M), HLA-A, -B and -C and Caspase 8 (CASP8), highlighting loss of antigen presentation and blockade of extrinsic apoptosis as key strategies of resistance to cytolytic activity. Genetic amplifications were also associated with high cytolytic activity, including immunosuppressive factors such as PDL1/2 and ALOX12B/15B. Our genetic findings thus provide evidence for immunoediting in tumors and uncover mechanisms of tumor-intrinsic resistance to cytolytic activity.
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              Molecular and cellular insights into T cell exhaustion.

              In chronic infections and cancer, T cells are exposed to persistent antigen and/or inflammatory signals. This scenario is often associated with the deterioration of T cell function: a state called 'exhaustion'. Exhausted T cells lose robust effector functions, express multiple inhibitory receptors and are defined by an altered transcriptional programme. T cell exhaustion is often associated with inefficient control of persisting infections and tumours, but revitalization of exhausted T cells can reinvigorate immunity. Here, we review recent advances that provide a clearer molecular understanding of T cell exhaustion and reveal new therapeutic targets for persisting infections and cancer.
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                Author and article information

                Contributors
                (View ORCID Profile)
                (View ORCID Profile)
                Journal
                Nature Reviews Cancer
                Nat Rev Cancer
                Springer Science and Business Media LLC
                1474-175X
                1474-1768
                May 2023
                April 12 2023
                May 2023
                : 23
                : 5
                : 295-316
                Article
                10.1038/s41568-023-00560-y
                37046001
                ab14e100-fe06-44c4-bbf2-bf6564dfeb52
                © 2023

                https://www.springernature.com/gp/researchers/text-and-data-mining

                https://www.springernature.com/gp/researchers/text-and-data-mining

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