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Identification of immune microenvironment subtypes that predicted the prognosis of patients with ovarian cancer.

ABSTRACT: Ovarian cancer (OC) is associated with high mortality rate. However, the correlation between immune microenvironment and prognosis of OC remains unclear. This study aimed to explore prognostic significance of OC tumour microenvironment. The OC data set was selected from the cancer genome atlas (TCGA), and 307 samples were collected. Hierarchical clustering was performed according to the expression of 756 genes. The immune and matrix scores of all immune subtypes were determined, and Kruskal-Wallis test was used to analyse the differences in the immune and matrix scores between OC samples with different immune subtypes. The model for predicting prognosis was constructed based on the expression of immune-related genes. TIDE platform was applied to predict the effect of immunotherapy on patients with OC of different immune subtypes. The 307 OC samples were classified into three immune subtypes A-C. Patients in subtype B had poorer prognosis and lower survival rate. The infiltration of helper T cells and macrophages in microenvironment indicated significant differences between immune subtypes. Enrichment analyses of immune cell molecular pathways showed that JAK-STAT3 pathway changed significantly in subtype B. Furthermore, predictive response to immunotherapy in subtype B was significantly higher than that in subtype A and C. Immune subtyping can be used as an independent predictor of the prognosis of OC patients, which may be related to the infiltration patterns of immune cells in tumour microenvironment. In addition, patients in immune subtype B have superior response to immunotherapy, suggesting that patients in subtype B are suitable for immunotherapy.

SUBMITTER: Wang X

PROVIDER: S-EPMC8051724 | biostudies-literature |

REPOSITORIES: biostudies-literature

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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:BackgroundBiomarkers based on immune context may guide prognosis prediction. T-cell inactivation, exclusion, or dysfunction could cause unfavorable tumor microenvironments, which affect immunotherapy and prognosis. However, none of the immuno-biomarkers reported to date can differentiate colorectal-cancer (CRC) patients. Thus, we aimed to classify CRC patients according to the levels of T-cell activation, exclusion, and dysfunction in the tumor microenvironment.MethodsRNAseq data of 618 CRC patients from The Cancer Genome Atlas and microarray data of 316 CRC patients from Gene Expression Omnibus were analysed using the Tumor Immune Dysfunction and Exclusion algorithm. Unsupervised clustering was used to classify patients.ResultsBased on the expression signatures of myeloid-derived suppressor cells, cancer-associated fibroblasts, M2-like tumor-associated macrophages, cytotoxic T-lymphocytes, and PD-L1, all patients were clustered into four subtypes: cluster 1 had a high level of immune dysfunction, cluster 2 had a low level of immune activation, cluster 3 had intense immune exclusion, and cluster 4 had a high level of immune activation and a moderate level of both dysfunction and exclusion signatures. Compared with cluster 1, the hazard ratios and 95% confidential intervals for overall survival were 0.63 (0.35-1.13) for cluster 2, 0.55 (0.29-1.03) for cluster 3, and 0.30 (0.14-0.64) for cluster 4 in multivariate Cox regression. Similar immune clustering and prognosis patterns were obtained upon validation in the GSE39582 cohort. In subgroup analysis, immune clustering was significantly associated with overall survival among stage I/II patients, microsatellite stable/instability-low patients, and patients not treated with adjuvant therapy.ConclusionsOur findings demonstrated that classifying CRC patients into different immune subtypes serves as a reliable prognosis predictor and may help to refine patient selection for personalized cancer immunotherapy.

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Identification of immune microenvironment subtypes that predicted the prognosis of patients with ovarian cancer.

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