Immunophenotyping Of The Tumor Microenvironment

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Tumor tissue is often dependent on the microenvironment on which it depends, and the tumor immune microenvironment (TIME) is an extremely complex system, in which various immune cells, stromal cells and cytokines can interact with tumors. The regulation of these immune system networks has complex interactions with tumors and has an important impact on tumor development and immunotherapy response.

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Overall immunophenotype of the tumor microenvironment

The overall status of tumor-infiltrating lymphocytes (TILs) in the tumor microenvironment (TME) is correlated with the efficacy of immunotherapy. Standard pathological cancer images can be obtained based on deep learning-derived "computer staining" and used for the Identification of tumor TILs. According to the status of immune cells in TIME, tumor immune infiltration patterns can be roughly divided into three types: immune inflammation type, immune exclusion type and immune desert type. The immune-inflammatory type is characterized by the presence of CD8+ and CD4+ T cells in tumor parenchyma, accompanied by the expression of immune checkpoint molecules, suggesting that such tumors have a potential antitumor immune response to ICIs treatment. The immune-excluded type is characterized by the presence of distinct immune cell types in the aggressive margin or stroma of the tumor, but not infiltrating the tumor parenchyma. Studies have analyzed samples before treatment against programmed cell death 1 (PD-1)/and its ligand (programmed cell death-ligand 1, PD-L1), and the results show that responders invade The CD8+ T cell abundance was relatively high at the sexual margin, and serial sampling during treatment showed an increase in CD8+ T cell infiltration of the tumor parenchyma. The immune desert type is characterized by a lack of abundant T cells in the tumor parenchyma or stroma, which is poorly responsive to ICIs. Recently, an immune score has been proposed as an effective indicator to characterize TME immune status, tumor classification, and to predict treatment response and prognosis, involving the expression of two lymphocyte populations (CD8+ and memory [CD45RO+] T cells) in the center and tumor invasive margins. density. MLECNIK et al evaluated the immune score in 599 cases of stage I-IV colorectal cancer specimens and confirmed that it was significantly correlated with progression-free survival (PFS) and overall survival (OS) of tumor patients, and many Factor analyses also showed the superiority of the immune score in predicting disease recurrence and survival. The value of immune scores in predicting the efficacy of ICIs therapy is being validated in clinical trials of melanoma and non-small cell lung cancer (NSCLC).

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A study used transcriptome analysis to classify the TME and confirmed that microenvironmental subtypes can be used as predictors of immunotherapy efficacy. The researchers first searched the published literature for functional gene expression signatures (Fges) of TME components (such as tumor main components, immune cells, stromal cells, and other cell populations); then constructed a comprehensive Delineate a single model of TME; then utilize multiple databases such as the Cancer Genome Atlas (TCGA), International Cancer Genome Consortium (ICGC), or Genotype-Tissue Expression (GTEx) The accuracy of the Fges classification was validated with the dataset in and found to be highly cell-type specific, such as the expression of genes characteristic of tumor proliferation (including cell cycle and tumor progression-related genes) compared with normal tissue and moles. Malignant melanoma has a strong correlation; finally, 29 Fges were used to classify melanoma TME and divided into the following 4 microenvironmental subtypes: (1) immune-enriched and fibrotic (Immune-enriched, fibrotic, IE/F); (2) Immune enrichment, non-fibrotic (immune-enriched, fibrotic, IE); (3) fibrotic (fibrotic, F); (4) immune-depleted (immune-depleted, D). The above-mentioned different TME subtypes are significantly different, have a correlation with immunotherapy, and can be used as potential predictive biomarkers. For example, in anti-cytotoxic T-lymphocyte-associated protein 4 (CTLA-4) immunotherapy, patients with cutaneous melanoma type IE had an 82% response rate to treatment, compared with 10 for type F %, and IE OS is the longest. In addition, by analyzing before and after immunotherapy, the dynamic evolution of TME for immunotherapy can also be observed. For example, among melanoma patients receiving anti-PD-1 therapy, the responders were basically IE/F or IE, which remained unchanged during treatment or developed into an immune-enriched environment; while non-responders were mostly F TMEs type, which appears to maintain or move toward the immunosuppressive TME during treatment. This TME classification system may also explain the efficacy of immunotherapy in patients with low tumor mutational burden (TMB), and in an expanded cohort with no TMB data, TME classification using pre-and on-treatment biopsies still Has good predictive potential. Therefore, the two classification systems, TME typing and TMB analysis, can complement each other as immunotherapy biomarkers.

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Specific microenvironmental immunophenotypes

In addition to the overall immune phenotype of the microenvironment, the expression of some molecules on immune cells or stromal cells in the TME can also characterize specific immune phenotypes in the microenvironment, thereby affecting the efficacy of immunotherapy, and has been gradually explored as a predictive marker for immunotherapy. . Recent studies have found that CD28 can be used as a predictive marker for the efficacy of immunotherapy, and based on this, new immunotherapies can be developed. In contrast to CTLA-4, CD28 is a co-stimulatory receptor that interacts with CD80/86 to modulate immune function, activating multiple mechanisms that promote immune responses. T lymphocyte tyrosine kinase (LCK), phosphatidylinositol 3-kinase (PI3K) pathway and protein kinase C (PKC) and other signaling pathways can activate CD28, and then Activates a variety of downstream transcription factors, such as activator protein-1 (AP-1) and nuclear factor-κB (nuclear transcription factor-κB, NF-κB). These transcription factors are critical for IL-2 production and T cell activation and survival. KAMPHORST et al. found that CD28 signaling plays an important role in the activation and recovery of CD8+ T cell proliferation and anti-tumor response. Anti-PD-1 therapy has a selective proliferation effect on CD28+ cells. Therefore, it is recommended to use CD28 as a predictor of CD8+ T cells in tumor patients. Potential biomarkers of response. In addition, PD-1 inhibits signaling through the T cell receptor (TCR) after being activated by its ligand PD-L1. By detecting PD-1 signaling in a biochemical reconstitution system, HUI et al. found that the co-stimulatory receptor CD28 is more likely than TCR as a target for PD-1 to recruit Shp2 phosphatase dephosphorylation; PD-1 activation by L1 preferentially dephosphorylates, rather than TCR. Therefore, it is believed that PD-1 mainly inhibits T cell function by inactivating CD28 signaling, indicating that the co-stimulatory pathway plays a key role in regulating effector T cell (Teffs) function and anti-PD-L1/PD-1 therapeutic response effect. In the future, the rescue of T cell depletion phenotype by better blocking PD-1 binding to CD28 may be a potentially effective antitumor immunotherapy strategy.

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In addition to activating molecules, the expression of immune checkpoints on the surface of immune cells is also a reflection of the immune phenotype of a specific microenvironment, which can influence and predict the efficacy of immunotherapy. Among them, PD-1 is the most studied and widely used checkpoint molecule. Studies have found that the balance of PD-1 expression between effector T cells and regulatory T cells (Tregs) in the TME can predict the clinical efficacy of PD-1 blockade therapy. The study used flow cytometry to detect TILs and found that the ratio of PD-1+CD8+T cells/PD-1+Tregs cells in the TME of patients with effective and ineffective PD-1 monoclonal antibody treatment was significantly different, and the TME of patients with effective treatment was significantly different. There is a high infiltration of PD-1+CD8+ T cells in TME, and CD8+ T cells with high PD-1 expression have high-affinity antigen peptides; on the contrary, PD-1 is highly expressed in effector Treg cells in the TME of treatment-ineffective patients. Another novel immune checkpoint protein is T-cell immunoglobulin and the ITIM domain (T-cell immunoglobulin and immunoreceptor tyrosine-based inhibitory motif domain, TIGHT), which are involved in lymphocytes, especially in effector CD8+ T cells and natural killers It is highly expressed in natural killer cells (NK). TIGIT can inhibit immune cells in multiple steps of the tumor immune cycle. When TIGIT on the surface of NK cells and T cells binds to the highly expressed poliovirus receptor (PVR, or CD155) on the surface of tumor cells, the tumor cell killing effects of NK cells and T cells were inhibited. Anti-tumor immunity can be restored when preventing TIGIT from binding to its ligands. For example, MK-7684 is a humanized IgG1 monoclonal antibody that binds TIGIT and blocks its interaction with its ligands CD112 and CD155 (NCT02964013, NCT04305041). In addition, immune cells can also express other inhibitory receptors, such as CTLA-4, T cell immunoglobulin and mucin-containing molecule 3 (TIM-3), etc., which lead to tumor immune resistance. THOMSEN et al. found that increased co-expression of multiple immune checkpoints, such as PD-1, TIM-3, CTLA-4, and lymphocyte activation gene-3 (LAG-3), can promote T cell progression Sexual severe exhaustion, which further mediates the generation of resistance to ICIs. Taken together, the expression of these immune-activating molecules and checkpoint molecules may represent microenvironment-specific immunophenotypic signatures, which not only have potential value in predicting the therapeutic efficacy of ICIs but also help facilitate the development of immunotherapeutic strategies targeting these molecules.