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Regulation of Ovarian Cancer Prognosis by Immune Cells in the Tumor Microenvironment.

ABSTRACT: It is estimated that in the United States in 2018 there will be 22,240 new cases of ovarian cancer and 14,070 deaths due to this malignancy. The most common subgroup of this disease is high-grade serous ovarian cancer (HGSOC), which is known for its aggressiveness, high recurrence rate, metastasis to other sites, and the development of resistance to conventional therapy. It is important to understand the ovarian cancer tumor microenvironment (TME) from the viewpoint of the function of pre-existing immune cells, as immunocompetent cells are crucial to mounting robust antitumor responses to prevent visible tumor lesions, disease progression, or recurrence. Networks consisting of innate and adaptive immune cells, metabolic pathways, intracellular signaling molecules, and a vast array of soluble factors, shape the pathogenic nature of the TME and are useful prognostic indicators of responses to conventional therapy and immunotherapy, and subsequent survival rates. This review highlights key immune cells and soluble molecules in the TME of ovarian cancer, which are important in the development of effective antitumor immunity, as well as those that impair effector T cell activity. A more insightful knowledge of the HGSOC TME will reveal potential immune biomarkers to aid in the early detection of this disease, as well as biomarkers that may be targeted to advance the design of novel therapies that induce potent antitumor immunity and survival benefit.

SUBMITTER: Drakes ML

PROVIDER: S-EPMC6162424 | biostudies-literature | 2018 Sep

REPOSITORIES: biostudies-literature

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Regulation of Ovarian Cancer Prognosis by Immune Cells in the Tumor Microenvironment.

Drakes Maureen L ML Stiff Patrick J PJ

Cancers 20180901 9

It is estimated that in the United States in 2018 there will be 22,240 new cases of ovarian cancer and 14,070 deaths due to this malignancy. The most common subgroup of this disease is high-grade serous ovarian cancer (HGSOC), which is known for its aggressiveness, high recurrence rate, metastasis to other sites, and the development of resistance to conventional therapy. It is important to understand the ovarian cancer tumor microenvironment (TME) from the viewpoint of the function of pre-existi ...[more]

PMID: 30200478

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Project description:Objective: Because of the modest immunotherapeutic response among ovarian carcinoma (OC) patients, it is significant to evaluate antitumor immune response and develop more effective precision immunotherapeutic regimens. Here, this study aimed to determine diverse immune subtypes of OC. Methods: This study curated the expression profiles of prognostic immunologically relevant genes and conducted consensus clustering analyses for determining immune subtypes among OC patients in TCGA cohort. With Boruta algorithm, characteristic genes were screened for conducting an immune scoring system through principal component analysis algorithm. The single-sample gene set enrichment analysis and ESTIAMTE methods were adopted for quantifying the immune infiltrates and responses to chemotherapeutic agents were estimated with pRRophetic algorithm. Two immunotherapeutic cohorts were used for investigating the efficacy of immune score in predicting therapeutic benefits. Results: Two immune subtypes were conducted among 377 OC patients. Immune subtype 2 was characterized by worse clinical prognosis, more frequent genetic variations and mutations, enhanced immune infiltrates, and increased expression of MHC molecules and programmed cell death protein 1/programmed death ligand 1 (PD-1/PD-L1). In total, 30 prognosis-relevant characteristic immune subtype-derived genes were identified for constructing the immune score of OC patients. High immune score was linked with more dismal prognosis, decreased immune infiltrations, and expression of MHC molecules. High immune score presented favorable sensitivity to doxorubicin and vinorelbine and reduced sensitivity to cisplatin. In addition, immune score possessed the potential in predicting benefits from anti-PD-1/PD-L1 therapy. Conclusion: Collectively, our findings propose two complex and diverse immune subtypes of OC. Quantitative assessment of immune subtypes in individual patients strengthens the understanding of tumor microenvironment features and promotes more effective immunotherapeutic regimens.

Project description:BackgroundThe tumor immune microenvironment (TIME) has been recognized to be an imperative factor facilitating the acquisition of many cancer-related hallmarks and is a critical target for targeted biological therapy. This research intended to construct a risk score model premised on TIME-associated genes for prediction of survival and identification of potential drugs for ovarian cancer (OC) patients.Methods and resultsThe stromal and immune scores were computed utilizing the ESTIMATE algorithm in OC patient samples from The Cancer Genome Atlas (TCGA) database. Weighted gene co-expression network and differentially expressed genes analyses were utilized to detect stromal-and immune-related genes. The Least Absolute Shrinkage and Selection Operator (LASSO)-Cox regression was utilized for additional gene selection. The genes that were selected were utilized as the input for a stepwise regression to construct a TIME-related risk score (TIMErisk), which was then validated in Gene Expression Omnibus (GEO) database. For the evaluation of the protein expression levels of TIME regulators, the Human Protein Atlas (HPA) dataset was utilized, and for their biological functions, the TIMER and CIBERSORT algorithm, immunoreactivity, and Immune Cell Abundance Identifier (ImmuCellAI) were used. Possible OC medications were forecasted utilizing the Genomics of Drug Sensitivity in Cancer (GDSC) database and connectivity map (CMap). TIMErisk was developed based on ALPK2, CPA3, PTGER3, CTHRC1, PLA2G2D, CXCL11, and ZNF683. High TIMErisk was recognized as a poor factor for survival in the GEO and TCGA databases; subgroup analysis with FIGO stage, grade, lymphatic and venous invasion, debulking, and tumor site also indicated similar results. Functional immune cells corresponded to more incisive immune reactions, including secretion of chemokines and interleukins, natural killer cell cytotoxicity, TNF signaling pathway, and infiltration of activated NK cells, eosinophils, and neutrophils in patients with low TIMErisk. Several small molecular medications which may enhance the prognosis of patients in the TIMErisk subgroup were identified. Lastly, an enhanced predictive performance nomogram was constructed by compounding TIMErisk with the FIGO stage and debulking.ConclusionThese findings may offer a valuable indicator for clinical stratification management and personalized therapeutic options for OC patients and may be a foundation for future mechanistic research of their association.

Project description:ObjectiveTo explore the regulatory role of miR-4772 in the formation of tumor immune microenvironment in ovarian cancer.MethodsThe optimal cutoff level of PD-L1 expression was calculated based on data from 294 ovarian cancer patients in the TCGA database. The differentially expressed genes (DEGs) between high and low PD-L1 expression groups were screened, and the important DEGs were identified by correlation analysis. WGCNA analysis was performed to select the weighted genes and PD-L1-related miRNAs, from which the hub genes were obtained by intersection analysis. ssGSEA analysis was used to evaluate the effect of PD-L1 and miR-4772 expressions on the tumor immune microenvironment in ovarian cancer. KEGG analysis was used to identify the involved signal pathways, and the interactions between the hub genes were mapped by protein-protein interaction (PPI) analysis. Survival analysis was carried out to identify the survival-related hub genes, and the results were validated using the data of 399 patients with ovarian cancer from GEO database and the sequencing results of SKOV3 cells transfected with miR-4772 mimics or inhibitor.ResultsAccording the optimal cutoff level of PD-L1 expression of 1.31582 (90th quantile), the patients were divided into high- and low-PD-L1 expression groups. A total of 840 DEGs were identified, including 549 significantly up-regulated genes and 291 down-regulated genes. Among them, 20 important DEGs were found to closely correlate with miR-4772 expression, and WGCNA analysis identified 48 weighted genes significantly correlated with miR-4772. Twelve genes were identified as both key DEGs and weighted genes and were treated as the hub genes. ssGSEA analysis showed that both the patients with high PD-L1 expressions and those with high miR-4772 expressions showed more active immune infiltration and functional activity. The 12 hub genes were involved mainly in immune-related signaling pathways, and PPI analysis suggested significant interactions among the hub genes. The two hub genes CD96 and TBX21 showed close correlation with the survival of ovarian cancer patients. The sequencing results of SKOV3 cells transfected with miR-4772 mimics or inhibitor showed that the changes in miR-4772 expression level caused obvious changes in the expressions of the 12 hub genes and PD-L1.ConclusionMiR-4772 plays a regulatory role in the formation of tumor immune microenvironment in ovarian cancer by regulating 12 hub genes.

Dataset Information

Regulation of Ovarian Cancer Prognosis by Immune Cells in the Tumor Microenvironment.

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Regulation of Ovarian Cancer Prognosis by Immune Cells in the Tumor Microenvironment.

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