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Microbial mechanisms to improve immune checkpoint blockade responsiveness.

ABSTRACT: The human microbiota acts as a diverse source of molecular cues that influence the development and homeostasis of the immune system. Beyond endogenous roles in the human holobiont, host-microbial interactions also alter outcomes for immune-related diseases and treatment regimens. Over the past decade, sequencing analyses of cancer patients have revealed correlations between microbiota composition and the efficacy of cancer immunotherapies such as checkpoint inhibitors. However, very little is known about the exact mechanisms that link specific microbiota with patient responses, limiting our ability to exploit these microbial agents for improved oncology care. Here, we summarize current progress towards a molecular understanding of host-microbial interactions in the context of checkpoint inhibitor immunotherapies. By highlighting the successes of a limited number of studies focused on identifying specific, causal molecules, we underscore how the exploration of specific microbial features such as proteins, enzymes, and metabolites may translate into precise and actionable therapies for personalized patient care in the clinic.

SUBMITTER: Griffin ME

PROVIDER: S-EPMC9284443 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:BackgroundNumerous studies indicated that tumor-infiltrated immune cells (TIC) in the microenvironment are substantially linked to immunotherapy response and cancer prognosis. However, systematic studies of infiltrated immune cell characterization in uveal melanoma (UM) for prognosis and immune checkpoint blockade therapy are lacking.MethodsUM datasets were extracted from open access resources (TCGA and GEO databases). The tumor-infiltrated immune cells in the microenvironment were decoded by using the CIBERSORT algorithm, which was further applied to classify UM patients into subgroups using an unsupervised clustering method. The Boruta algorithm and principal component analysis were used to calculate the TIC scores for UM patients. Kaplan-Meier curves were plotted to prove the prognostic value of TIC scores. Besides, the correlations of the TIC score with clinical features, mutated characteristics, and the immune therapeutic response were subsequently investigated.ResultsAs a result, we defined three subtypes among 171 UM patients according to the TIC profiles and then calculated the TIC score to characterize the immune patterns for all patients. We discovered that high-TIC score patients with low BAP1 and high EIF1AX mutations have a better prognosis than low-TIC score patients. Activation of immune inflammatory response and increase in immune checkpoint-related genes in high-TIC score patients may account for good prognosis and immunotherapy response. Three melanoma cohorts received immunotherapy, proving that high-TIC score patients have substantial clinical and immune therapeutic improvements. Besides, several potential therapeutic agents were identified in the low-TIC score group.ConclusionOur study afforded a comprehensive view of infiltrated immune cell characterization to elucidate different immune patterns of UM. We also established a robust TIC-score signature, which may work as a prognostic biomarker and immune therapeutic predictor.

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Microbial mechanisms to improve immune checkpoint blockade responsiveness.

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