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Interactions between tumor mutation burden and immune infiltration in ovarian cancer.

ABSTRACT: The aim of the study was to evaluate the relationship between tumor mutational burden (TMB) and immune infiltration in ovarian cancer. We extracted somatic mutational data and gene expression profiles of ovarian cancer from The Cancer Genome Atlas (TCGA). The samples were separated into low and high TMB groups. Correlations between TMB and cancer prognosis were analyzed and immune cell infiltration in the high and low TMB subgroups was calculated using the CIBERSORT package software. High TMB was significantly related to an improved survival rate. We identified 4 TMB-related core genes that were significantly associated with prognosis. Furthermore, mutations in the 4 genes were associated with immune cell infiltration. We also found a high proportion of naive B cells and activated NK cells in the high TMB group, while increased proportions of memory B cells and plasma cells were found in the low TMB group. Overall, our study indicated that patients with a higher TMB level experienced a favorable survival outcome and this may influence immune infiltration in ovarian cancer. Furthermore, the 4 TMB-related core genes were highly correlated with prognosis and the level of immune cell infiltration.

SUBMITTER: Mi JL

PROVIDER: S-EPMC7642696 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature

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Interactions between tumor mutation burden and immune infiltration in ovarian cancer.

Mi Jing-Lin JL Xu Meng M Liu Chang C Wang Ren-Sheng RS

International journal of clinical and experimental pathology 20201001 10

The aim of the study was to evaluate the relationship between tumor mutational burden (TMB) and immune infiltration in ovarian cancer. We extracted somatic mutational data and gene expression profiles of ovarian cancer from The Cancer Genome Atlas (TCGA). The samples were separated into low and high TMB groups. Correlations between TMB and cancer prognosis were analyzed and immune cell infiltration in the high and low TMB subgroups was calculated using the CIBERSORT package software. High TMB wa ...[more]

PMID: 33165430

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Project description:BackgroundColon cancer has a huge incidence and mortality worldwide every year. Immunotherapy could be a new therapeutic option for patients with advanced colon cancer. Tumor mutation burden (TMB) and immune infiltration are considered critical in immunotherapy but their characteristics in colon cancer are still controversial.MethodsThe somatic mutation, transcriptome, and clinical data of patients with colon cancer were obtained from the TCGA database. Patients were divided into low or high TMB groups using the median TMB value. Somatic mutation landscape, differentially expressed genes, and immune-related hub genes, Gene Ontology and KEGG, gene set enrichment, and immune infiltration analyses were investigated between the two TMB groups. Univariate and multivariate Cox analyses were utilized to construct a prognostic gene signature. The differences in immune infiltration, and the expression of HLA-related genes and checkpoint genes were investigated between the two immunity groups based on single sample gene set enrichment analysis. Finally, a nomogram of the prognostic prediction model integrating TMB, immune infiltration, and clinical parameters was established. Calibration plots and receiver operating characteristic curves (ROC) were drawn, and the C-index was calculated to assess the predictive ability.ResultsMissense mutations and single nucleotide polymorphisms were the major variant characteristics in colon cancer. The TMB level showed significant differences in N stage, M stage, pathological stage, and immune infiltration. CD8+ T cells, activated memory CD4+ T cells, activated NK cells, and M1 macrophages infiltrated more in the high-TMB group. The antigen processing and presentation signaling pathway was enriched in the high-TMB group. Two immune related genes (CHGB and SCT) were identified to be correlated with colon cancer survival (HR = 1.39, P = 0.01; HR = 1.26, P = 0.02, respectively). Notably, the expression of SCT was identified as a risk factor in the immune risk model, in which high risk patients showed poorer survival (P = 0.04). High immunity status exhibited significant correlations with immune response pathways, HLA-related genes, and immune checkpoint genes. Finally, including nine factors, our nomogram prediction model showed better calibration (C-index = 0.764) and had an AUC of 0.737.ConclusionIn this study, we investigated the patterns and prognostic roles of TMB and immune infiltration in colon cancer, which provided new insights into the tumor microenvironment and immunotherapies and the development of a novel nomogram prognostic prediction model for patients with colon cancer.

Project description:Background: Melanoma is highly immunogenic and therefore suitable for immunotherapy, but the efficacy is limited by response rate. In several types of tumor, tumor mutation burden (TMB) and immune infiltration have been reported to predict the response to immunotherapy, although each has its limitations. In the current study, we aimed to explore the association of TMB with immune infiltration and prognosis in cutaneous melanoma. Methods: The data of cutaneous melanoma used for analyses was downloaded from The Cancer Genome Atlas (TCGA) database. The mutation data was sorted using "maftools" R package. TMB was estimated and then patients were divided into two groups based on TMB. The association of TMB with prognosis and clinical characteristics was explored. Differential analysis between two TMB groups was performed using "DESeq2" R package to identify differentially expressed genes (DEGs). The function enrichment analyses of DEGs were conducted to screen critical pathways. Besides, DEGs were further filtered to identify two hub genes, based on which a risk score model and nomogram for predicting prognosis were conducted, and the validation was performed using three datasets from Gene Expression Omnibus (GEO) database. Finally, CIBERSORT algorithm and TIMER database were used to assess the effect of TMB and hub genes on immune infiltration. Results: The most common mutation was C > T, and the top three frequently mutated genes were TTN, MUC16, and BRAF. Higher TMB indicated better survival outcomes and lower pathological stages. 735 DEGs were identified and mainly involved in immune-related and adhesion-related pathways. The risk score model and nomogram were validated using receiver operating characteristic (ROC) curves and calibration curves, and exhibited relatively high predictive capability. Decision curve analysis (DCA) was used to assess clinical benefit. As for immune infiltration, the proportion was higher for macrophages M1 and M2 in the high-TMB group, while lower for memory B cells and regulatory T cells. Conclusions: In cutaneous melanoma, TMB was positively correlated with prognosis. The risk score model and nomogram can be conveniently used to predict prognosis. The association of TMB with immune infiltration can help improve the predicting methods for the response to immunotherapy.

Project description:IntroductionFemale breast cancer is the most common malignancy worldwide, with a high disease burden. The degradome is the most abundant class of cellular enzymes that play an essential role in regulating cellular activity. Dysregulation of the degradome may disrupt cellular homeostasis and trigger carcinogenesis. Thus we attempted to understand the prognostic role of degradome in breast cancer by means of establishing a prognostic signature based on degradome-related genes (DRGs) and assessed its clinical utility in multiple dimensions.MethodsA total of 625 DRGs were obtained for analysis. Transcriptome data and clinical information of patients with breast cancer from TCGA-BRCA, METABRIC and GSE96058 were collected. NetworkAnalyst and cBioPortal were also utilized for analysis. LASSO regression analysis was employed to construct the degradome signature. Investigations of the degradome signature concerning clinical association, functional characterization, mutation landscape, immune infiltration, immune checkpoint expression and drug priority were orchestrated. Cell phenotype assays including colony formation, CCK8, transwell and wound healing were conducted in MCF-7 and MDA-MB-435S breast cancer cell lines, respectively.ResultsA 10-gene signature was developed and verified as an independent prognostic predictor combined with other clinicopathological parameters in breast cancer. The prognostic nomogram based on risk score (calculated based on the degradome signature) showed favourable capability in survival prediction and advantage in clinical benefit. High risk scores were associated with a higher degree of clinicopathological events (T4 stage and HER2-positive) and mutation frequency. Regulation of toll-like receptors and several cell cycle promoting activities were upregulated in the high-risk group. PIK3CA and TP53 mutations were dominant in the low- and high-risk groups, respectively. A significantly positive correlation was observed between the risk score and tumor mutation burden. The infiltration levels of immune cells and the expressions of immune checkpoints were significantly influenced by the risk score. Additionally, the degradome signature adequately predicted the survival of patients undergoing endocrinotherapy or radiotherapy. Patients in the low-risk group may achieve complete response after the first round of chemotherapy with cyclophosphamide and docetaxel, whereas patients in the high-risk group may benefit from 5-flfluorouracil. Several regulators of the PI3K/AKT/mTOR signaling pathway and the CDK family/PARP family were identified as potential molecular targets in the low- and high-risk groups, respectively. In vitro experiments further revealed that the knockdown of ABHD12 and USP41 significantly inhibit the proliferation, invasion and migration of breast cancer cells.ConclusionMultidimensional evaluation verified the clinical utility of the degradome signature in predicting prognosis, risk stratification and guiding treatment for patients with breast cancer.

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.

Dataset Information

Interactions between tumor mutation burden and immune infiltration in ovarian cancer.

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Interactions between tumor mutation burden and immune infiltration in ovarian cancer.

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