Dataset Information

Analysis of Tumor Microenvironment Characteristics in Bladder Cancer: Implications for Immune Checkpoint Inhibitor Therapy.

ABSTRACT: The tumor microenvironment (TME) plays a crucial role in cancer progression and recent evidence has clarified its clinical significance in predicting outcomes and efficacy. However, there are no studies on the systematic analysis of TME characteristics in bladder cancer. In this study, we comprehensively evaluated the TME invasion pattern of bladder cancer in 1,889 patients, defined three different TME phenotypes, and found that different subtypes were associated with the clinical prognosis and pathological characteristics of bladder cancer. We further explored the signaling pathways, cancer-immunity cycle, copy number, and somatic mutation differences among the different subtypes and used the principal component analysis algorithm to calculate the immune cell (IC) score, a tool for comprehensive evaluation of TME. Univariate and multivariate Cox regression analyses showed that ICscore is a reliable and independent prognostic biomarker. In addition, the use of anti-programmed death-ligand (PD-L1) treatment cohort, receiver operating characteristic (ROC) curve, Tumor Immune Dysfunction and Exclusion (TIDE), Subnetwork Mappings in Alignment of Pathways (SubMAP), and other algorithms confirmed that ICscore is a reliable prognostic biomarker for immune checkpoint inhibitor response. Patients with higher ICscore showed a significant therapeutic advantage in immunotherapy. In conclusion, this study improves our understanding of the characteristics of TME infiltration in bladder cancer and provides guidance for more effective personalized immunotherapy strategies.

SUBMITTER: Chen X

PROVIDER: S-EPMC8082152 | biostudies-literature |

REPOSITORIES: biostudies-literature

ACCESS DATA

Similar Datasets

Project description:BackgroundStudies have shown that N6-methyl adenosine (m6A) plays an important role in cancer progression; however, the underlying mechanism of m6A modification in tumor microenvironment (TME) cell infiltration of bladder cancer remains unclear. This study aimed to investigate the role of m6A modification in TME cell infiltration of bladder cancer.MethodsThe RNA expression profile and clinical data of bladder cancer were obtained from The Cancer Genome Atlas and Gene Expression Omnibus. We assessed the m6A modification patterns of 664 bladder cancer samples based on 20 m6A regulators through unsupervised clustering analysis and systematically linked m6A modification patterns to TME cell infiltration characteristics. Gene ontology and gene set variation analyses were conducted to analyze the underlying mechanism based on the assessment of m6A methylation regulators. Principal component analysis was used to construct the m6A score to quantify m6A modification patterns of bladder cancer.ResultsThe genetic and expression alterations in m6A regulators were highly heterogeneous between normal and bladder tissues. Three m6A modification patterns were identified. The cell infiltration characteristics were highly consistent with the three immune phenotypes, including immune rejection, immune inflammation, and immune desert. The biological functions of three m6A modification patterns were different. Cox regression analyses revealed that the m6A score was an independent signature with patient prognosis (HR = 1.198, 95% CI: 1.031-1.390). Patients with a low-m6A score were characterized by increased tumor mutation burden, PD-L1 expression, and poorer survival. Patients in the low-m6A score group also showed significant immune responses and clinical benefits in the CTLA-4 immunotherapy cohort (p =0.0069).ConclusionsThe m6A methylation modification was related to the formation of TME heterogeneity and complexity. Assessing the m6A modification pattern of individual bladder cancer will improve the understanding of TME infiltration characteristics.

Project description:In the last decade, the treatment of non-small cell lung cancer (NSCLC) has been revolutionized by the introduction of immune checkpoint inhibitors (ICI) directed against programmed death protein 1 (PD-1) and its ligand (PD-L1), or cytotoxic T lymphocyte antigen 4 (CTLA-4). In spite of these improvements, some patients do not achieve any benefit from ICI, and inevitably develop resistance to therapy over time. Tumor microenvironment (TME) might influence response to immunotherapy due to its prominent role in the multiple interactions between neoplastic cells and the immune system. Studies investigating lung cancer from the perspective of TME pointed out a complex scenario where tumor angiogenesis, soluble factors, immune suppressive/regulatory elements and cells composing TME itself participate to tumor growth. In this review, we point out the current state of knowledge involving the relationship between tumor cells and the components of TME in NSCLC as well as their interactions with immunotherapy providing an update on novel predictors of benefit from currently employed ICI or new therapeutic targets of investigational agents. In first place, increasing evidence suggests that TME might represent a promising biomarker of sensitivity to ICI, based on the presence of immune-modulating cells, such as Treg, myeloid derived suppressor cells, and tumor associated macrophages, which are known to induce an immunosuppressive environment, poorly responsive to ICI. Consequently, multiple clinical studies have been designed to influence TME towards a pro-immunogenic state and subsequently improve the activity of ICI. Currently, the mostly employed approach relies on the association of "classic" ICI targeting PD-1/PD-L1 and novel agents directed on molecules, such as LAG-3 and TIM-3. To date, some trials have already shown promising results, while a multitude of prospective studies are ongoing, and their results might significantly influence the future approach to cancer immunotherapy.

Dataset Information

Analysis of Tumor Microenvironment Characteristics in Bladder Cancer: Implications for Immune Checkpoint Inhibitor Therapy.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets