Dataset Information

Advances in Immunotherapy and the TGF-β Resistance Pathway in Metastatic Bladder Cancer.

ABSTRACT: Bladder cancer accounts for nearly 200,000 deaths worldwide yearly. Urothelial carcinoma (UC) accounts for nearly 90% of cases of bladder cancer. Cisplatin-based chemotherapy has remained the mainstay of treatment in the first-line setting for locally advanced or metastatic UC. More recently, the treatment paradigm in the second-line setting was drastically altered with the approval of several immune checkpoint inhibitors (ICIs). Given that only a small subset of patients respond to ICI, further studies have been undertaken to understand potential resistance mechanisms to ICI. One potential resistance mechanism that has been identified in the setting of metastatic UC is the TGF-β signaling pathway. Several pre-clinical and ongoing clinical trials in multiple advanced tumor types have evaluated several therapies that target the TGF-β pathway. In addition, there are ongoing and planned clinical trials combining TGF-β inhibition with ICI, which may provide a promising therapeutic approach for patients with advanced and metastatic UC.

SUBMITTER: Benjamin DJ

PROVIDER: S-EPMC8616345 | biostudies-literature | 2021 Nov

REPOSITORIES: biostudies-literature

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Advances in Immunotherapy and the TGF-β Resistance Pathway in Metastatic Bladder Cancer.

Benjamin David J DJ Lyou Yung Y

Cancers 20211116 22

Bladder cancer accounts for nearly 200,000 deaths worldwide yearly. Urothelial carcinoma (UC) accounts for nearly 90% of cases of bladder cancer. Cisplatin-based chemotherapy has remained the mainstay of treatment in the first-line setting for locally advanced or metastatic UC. More recently, the treatment paradigm in the second-line setting was drastically altered with the approval of several immune checkpoint inhibitors (ICIs). Given that only a small subset of patients respond to ICI, further ...[more]

PMID: 34830879

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Project description:IntroductionThe TGF-β signaling pathway (TSP) is pivotal in tumor progression. Nonetheless, the connection between genes associated with the TSP and the clinical outcomes of breast cancer, as well as their impact on the tumor microenvironment and immunotherapeutic responses, remains elusive.MethodsBreast cancer transcriptomic and single-cell sequencing data were obtained from the The Cancer Genome Atlas (TCGA) and the Gene Expression Omnibus (GEO) databases. We identified 54 genes associated with the TSP from the Molecular Signatures Database (MSigDB) and analyzed both data types to evaluate TSP activity. Using weighted gene co-expression network analysis (WGCNA), we identified modules linked to TSP activity. To assess patient risk, we used 101 machine learning algorithms to develop an optimal TGF-β pathway-related prognostic signature (TSPRS). We then examined immune activity and response to immune checkpoint inhibitors and chemotherapy in these groups. Finally, we validated ZMAT3 expression levels clinically and confirmed its relevance in breast cancer using CCK-8 and migration assays.ResultsAt the single-cell level, TSP activity was most notable in endothelial cells, with higher activity in normal tissues compared to tumors. TSPRS was developed. This signature's accuracy was confirmed through internal and external validations. A nomogram incorporating the TSPRS was created to improve prediction accuracy. Further studies showed that breast cancer patients categorized as low-risk by the TSPRS had higher immune phenotype scores and more immune cell infiltration, leading to better prognosis and enhanced immunotherapy response. Additionally, a strong link was found between the TSPRS risk score and the effectiveness of anti-tumor agents. Silencing the ZMAT3 gene in the TSPRS significantly reduced the proliferation and invasiveness of breast cancer cells.DiscussionOur study developed a TSPRS, which emerges as a potent predictive instrument for the prognosis of breast cancer, offering novel perspectives on the immunotherapeutic approach to the disease.

Project description:BackgroundColon cancer is a malignant tumor with high malignancy and a low survival rate whose heterogeneity limits systemic immunotherapy. Transforming growth factor-β (TGF-β) signaling pathway-related genes are associated with multiple tumors, but their role in prognosis prediction and tumor microenvironment (TME) regulation in colon cancer is poorly understood. Using bioinformatics, this study aimed to construct a risk prediction signature for colon cancer, which may provide a means for developing new effective treatment strategies.MethodsUsing consensus clustering, patients in The Cancer Genome Atlas (TCGA) with colon adenocarcinoma were classified into several subtypes based on the expression of TGF-β signaling pathway-related genes, and differences in survival, molecular, and immunological TME characteristics and drug sensitivity were examined in each subtype. Ten genes that make up a TGF-β-related predictive signature were found by least absolute shrinkage and selector operation (LASSO) regression using colon cancer data from the TCGA database and confirmed using a Gene Expression Omnibus (GEO) dataset. A nomogram incorporating risk scores and clinicopathologic factors was developed to stratify the prognosis of patients with colon cancer for accurate clinical diagnosis and therapy.ResultsTwo TGF-β subtypes were identified, with the TGF-β-high subtype being associated with a poorer prognosis and superior sensitivity to immunotherapy. Mutation analyses showed a high incidence of gene mutations in the TGF-β-high subtype. After completing signature construction, patients with colon cancer were categorized into high- and low-risk subgroups based on the median risk score of the TGF-β-related predictive signature. The risk score exhibited superior predictive performance relative to age, gender, and stage, as evidenced by its AUC of 0.686. Patients in the high-risk subgroup had higher levels of immunosuppressive cell infiltration and immune checkpoints in the TME, suggesting that these patients had better responses to immunotherapy.ConclusionsPatients with colon cancer were divided into two subtypes with different survival and immune characteristics using consensus clustering analysis based on TGF-β signaling pathway-related genes. The constructed risk prediction signature may show promise as a biomarker for evaluating the prognosis of colon cancer, with potential utility for screening individuals for immunotherapy.

Dataset Information

Advances in Immunotherapy and the TGF-β Resistance Pathway in Metastatic Bladder Cancer.

Publications

Advances in Immunotherapy and the TGF-β Resistance Pathway in Metastatic Bladder Cancer.

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