RESUMEN
As a key sensor of double-stranded DNA (dsDNA), cyclic GMP-AMP synthase (cGAS) detects cytosolic dsDNA and initiates the synthesis of 2'3' cyclic GMP-AMP (cGAMP) that activates the stimulator of interferon genes (STING). This finally promotes the production of type I interferons (IFN-I) that is crucial for bridging innate and adaptive immunity. Recent evidence show that several antitumor therapies, including radiotherapy (RT), chemotherapy, targeted therapies and immunotherapies, activate the cGAS-STING pathway to provoke the antitumor immunity. In the last decade, the development of STING agonists has been a major focus in both basic research and the pharmaceutical industry. However, up to now, none of STING agonists have been approved for clinical use. Considering the broad expression of STING in whole body and the direct lethal effect of STING agonists on immune cells in the draining lymph node (dLN), research on the optimal way to activate STING in tumor microenvironment (TME) appears to be a promising direction. Moreover, besides enhancing IFN-I signaling, the cGAS-STING pathway also plays roles in senescence, autophagy, apoptosis, mitotic arrest, and DNA repair, contributing to tumor development and metastasis. In this review, we summarize the recent advances on cGAS-STING pathway's response to antitumor therapies and the strategies involving this pathway for tumor treatment.
RESUMEN
BACKGROUND: Cutaneous T-cell lymphoma (CTCL) is highly heterogeneous, and its prognosis is closely related to the disease stage. The tumor microenvironment (TME) is an important component of tumor tissue, driving cancer cell growth, progression, and metastasis. However, the diagnostic value of TME in CTCL has not yet been studied in-depth. To date, no study has performed a comprehensive evaluation of the significance of the TME in CTCL. METHODS: Using xCell methods based on bulk RNA sequencing data, we inferred immune cell fraction in the TME in 126 patients and assessed the prognostic importance of immune cells. Consensus clustering was performed to determine the TME subtypes and characterize the transcriptome of each subtype. Based on the TME subtypes, we established the disease progression model using random forest algorithms and logistic regression. The efficacy of the model was examined using an additional 49-patient cohort. Finally, we validated our finding at the protein level using immunochemistry in a 16-patient cohort. RESULTS: Patients with advanced CTCL presented with a more active immunity overall than those with early stage. Random forest algorithms revealed that the immune cells CD4, macrophages, and dendritic cells (DCs) were the most effective prognosis predictors. Therefore, we constructed a risk model using logistic regression based on these immune cells. The TME score could be used to effectively predict disease conditions in three datasets with the AUC of 0.9414, 0.7912, and 0.7665, respectively. Immunochemistry at the protein level revealed that helper T cells and the macrophage markers CD4 and CD68 could successfully distinguish different CTCL stages in patients, whereas the DC marker langerin showed no change with disease progression. CONCLUSION: We found advanced-stage CTCL was associated with an active immune microenvironment, and the immune signatures CD4 and CD68 showed a relatively high accuracy in predicting CTCL disease progression.