Landscape of immune cell infiltration in clear cell renal cell carcinoma to aid immunotherapy.
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ABSTRACT:
SUBMITTER: Bai D
PROVIDER: S-EPMC8177771 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:The malignant phenotypes of cancer are defined not only by its intrinsic tumor cells but also by the tumor infiltrating immune cells (TIICs) recruited to the cancer microenvironment. Clear cell renal cell carcinoma (ccRCC) immune microenvironment plays an important role in the tumorigenesis. This research investigated the characteristics of immune cell invasion of renal cell carcinoma and provided clues for future clinical implementation. Retrospectively, ccRCC gene expression was analyzed with appropriate clinicopathological data from the Cancer Genome Atlas (TCGA) and GEO database up to December 2019. The CIBERSORT algorithm, meta-analysis, principal component analysis (PCA), Single-Sample Gene Set Enrichment Analysis (ssGSEA) and hierarchical agglomerative clustering were used to measure and evaluate the respective proportions of 22 cell types of immune infiltration using normalized gene expression data. We also focused on evaluating the association with TIICs subpopulations and clinical features and molecular subtypes. TIICs subpopulation, especially Macrophages subgroup, T follicular helper (Tfh) cells and CD8 T cells, all contribute to tumorigenesis. Unsupervised clustering analysis revealed that there existed two distinct TIICs subgroups with different survival patterns. TIICs are extensively involved in the pathogenesis and development of the ccRCC. Characterizing the composition of TIICs influences the metabolism of tumors, activity, level, stage, and survival of patients. Collectively, the TIIC analysis has the potential to assist in the assessment and selection of ccRCC prognosis and treatment.
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Project description:Several clinicopathological features of clear cell renal cell carcinomas (ccRCC) contribute to make an "atypical" cancer, including resistance to chemotherapy, sensitivity to anti-angiogenesis therapy and ICIs despite a low mutational burden, and CD8+ T cell infiltration being the predictor for poor prognosis-normally CD8+ T cell infiltration is a good prognostic factor in cancer patients. These "atypical" features have brought researchers to investigate the molecular and immunological mechanisms that lead to the increased T cell infiltrates despite relatively low molecular burdens, as well as to decipher the immune landscape that leads to better response to ICIs. In the present study, we summarize the past and ongoing pivotal clinical trials of immunotherapies for ccRCC, emphasizing the potential molecular and cellular mechanisms that lead to the success or failure of ICI therapy. Single-cell analysis of ccRCC has provided a more thorough and detailed understanding of the tumor immune microenvironment and has facilitated the discovery of molecular biomarkers from the tumor-infiltrating immune cells. We herein will focus on the discussion of some major immune cells, including T cells and tumor-associated macrophages (TAM) in ccRCC. We will further provide some perspectives of using molecular and cellular biomarkers derived from these immune cell types to potentially improve the response rate to ICIs in ccRCC patients.
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Project description:A comprehensive evaluation of the clear cell renal cell carcinoma (ccRCC) immune landscape was found using 584 RNA-sequencing datasets from The Cancer Genome Atlas (TCGA), we identified 17 key dysregulated immune-associated genes in ccRCC based on association with clinical variables and important immune pathways. Of the numerous findings from our analyses, we found that several of the 17 key dysregulated genes are heavily involved in interleukin and NF-kB signaling and that somatic copy number alteration (SCNA) hotspots may be causally associated with gene dysregulation. More importantly, we also found that key immune-associated genes and pathways are strongly upregulated in ccRCC. Our study may lend novel insights into the clinical implications of immune dysregulation in ccRCC and suggests potential immunotherapeutic targets for further evaluation.
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Project description:The tumor microenvironment (TME) chiefly consists of tumor cells and tumor-infiltrating immune cells admixed with the stromal component. A recent clinical trial has shown that the tumor immune cell infiltration (ICI) is correlated with the sensitivity to immunotherapy and the head and neck squamous cell carcinoma (HNSC) prognosis. However, to date, the immune infiltrative landscape of HNSC has not yet been elucidated. Herein, we proposed two computational algorithms to unravel the ICI landscape of 1,029 HNSC patients. Three ICI patterns were defined, and the ICI scores were determined by using principal-component analysis. A high ICI score was characterized by an increased tumor mutation burden (TMB) and the immune-activating signaling pathways. Activation of transforming growth factor-β (TGF-β) and WNT signaling pathways were observed in low ICI score subtypes, indicating T cell suppression, and may be responsible for poor prognosis. Two immunotherapy cohorts confirmed patients with higher ICI scores demonstrated significant therapeutic advantages and clinical benefits. This study demonstrated that the ICI scores serve as an effective prognostic biomarker and predictive indicator for immunotherapy. Evaluating the ICI patterns of a larger cohort of samples will extend our understanding of TME, and it may provide directions to the current research investigations on immunotherapeutic strategies for HNSC.
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Project description:Clear cell renal cell carcinoma (ccRCC) is the most common subtype of kidney cancer, and it is the major cause of kidney cancer death. Understanding tumor immune microenvironments (TMEs) is critical in cancer immunotherapies. Here, we studied the immune characterization at single-cell resolution by integrating public data of ccRCC across different tissue types, and comparing the transcriptome features and tumor TME differences in tumors, normal adjacent tissue, and peripheral blood. A total of 16 different types of cell components of ccRCC were identified. We revealed that there is an overall increase in T-cell and myeloid populations in tumor-infiltrated immune cells compared to normal renal tissue, and the B-cell population in the tumor showed a sharp decrease, which indicates that the cells in tumor tissue undergo strong immune stress. In addition, the cell-cell communication analysis revealed specific or conserved signals in different tissue types, which may aid to uncover the distinct immune response. By combining and analyzing publicly available ccRCC bulk RNA-seq datasets, 10 genes were identified as marker genes in specific cell types, which were significantly associated with poor prognosis. Of note, UBE2C, which may be a good indicator of tumor proliferation, is positively associated with reductions in overall survival and highly associated with tumor grade. Our integrated analysis provides single-cell transcriptomic profiling of ccRCC and their TME, and it unmasked new correlations between gene expression, survival outcomes, and immune cell-type components, enabling us to dissect the dynamic variables in the tumor development process. This resource provides deeper insight into the transcriptome features and immune response of ccRCC and will be helpful in kidney cancer immunotherapy.
| S-EPMC9263726 | biostudies-literature
Project description:Background: Clear cell renal cell carcinoma (ccRCC) is the most common histological subtype of renal cell carcinoma (RCC). Anoikis plays an essential function in tumourigenesis, whereas the role of anoikis in ccRCC remains unclear. Methods: Anoikis-related genes (ARGs) were collected from the MSigDB database. According to univariate Cox regression analysis, the least absolute shrinkage and selection operator (LASSO) algorithm was utilized to select the ARGs associated with the overall rate (OS). Multivariate Cox regression analysis was conducted to identify 5 prognostic ARGs, and a risk model was established. The Kaplan-Meier survival analysis was used to evaluate the OS rate of ccRCC patients. Gene ontology (GO), Kyoto encyclopedia of genes and genomes (KEGG), and Gene set enrichment analysis (GSVA) were utilized to investigate the molecular mechanism of patients in the low- and high-risk group. ESTIMATE, CIBERSOT, and single sample gene set enrichment analysis (ssGSEA) algorithms were conducted to estimate the immune infiltration landscape. Consensus clustering analysis was performed to divide the patients into different subgroups. Results: A fresh risk model was constructed based on the 5 prognostic ARGs (CHEK2, PDK4, ZNF304, SNAI2, SRC). The Kaplan-Meier survival analysis indicated that the OS rate of patients with a low-risk score was significantly higher than those with a high-risk score. Consensus clustering analysis successfully clustered the patients into two subgroups, with a remarkable difference in immune infiltration landscape and prognosis. The ESTIMATE, CIBERSORT, and ssGSEA results illustrated a significant gap in immune infiltration landscape of patients in the low- and high-risk group. Enrichment analysis and GSVA revealed that immune-related signaling pathways might mediate the role of ARGs in ccRCC. The nomogram results illustrated that the ARGs prognostic signature was an independent prognostic predictor that distinguished it from other clinical characteristics. TIDE score showed a promising immunotherapy response of ccRCC patients in different risk subgroups and cluster subgroups. Conclusion: Our study revealed that ARGs play a carcinogenic role in ccRCC. Additionally, we firstly integrated multiple ARGs to establish a risk-predictive model. This study highlights that ARGs could be implemented as a stratification factor for individualized and precise treatment in ccRCC patients.
| S-EPMC9627172 | biostudies-literature
Project description:Ovarian cancer (OC) is the second leading cause of death in gynecological cancer. Multiple study have shown that the efficacy of tumor immunotherapy is related to tumor immune cell infiltration (ICI). However, so far, the Immune infiltration landscape of tumor microenvironment (TME) in OC has not been elucidated. In this study, We organized the transcriptome data of OC in the Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases, evaluated the patient's TME information, and constructed the ICI scores to predict the clinical benefits of patients undergoing immunotherapy. Immune-related genes were further used to construct the prognostic model. After clustering analysis of ICI genes, we found that patients in ICI gene cluster C had the best prognosis, and their tumor microenvironment had the highest proportion of macrophage M1 and T cell follicular helper cells. This result was consistent with that of multivariate cox (multi-cox) analysis. The prognostic model constructed by immune-related genes had good predictive performance. By estimating Tumor mutation burden (TMB), we also found that there were multiple genes with statistically different mutation frequencies in the high and low ICI score groups. The model based on the ICI score may help to screen out patients who would benefit from immunotherapy. The immune-related genes screened may be used as biomarkers and therapeutic targets.
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Project description:BackgroundIntratumoral fibrosis was positively correlated with histological grade of renal clear cell carcinoma (ccRCC) and intratumoral inflammation. However, the association of intratumoral fibrosis with the immune infiltration of ccRCC was few evaluated.MethodsWe used the second harmonic generation (SHG)-based imaging technology and evaluated the intratumoral fibrosis in ccRCC, and then divided the patients into the high fibrosis group (HF) and the low fibrosis group (LF). Meanwhile, the Kaplan-Meier survival curve analysis was performed to analyze the relationship between intratumoral fibrosis and the disease-free survival rate. Antibody arrays were used for seeking difference in cytokines and immune infiltration between the HF group (N = 11) and LF group (N = 11). The selected immune infiltration marker was then verified by immunohistochemistry (IHC) staining in 45 ccRCC samples.ResultsOut of 640 cytokines and immune infiltration markers, we identified 115 proteins that were significantly different in quantity between ccRCC and adjacent normal tissues. In addition, the Venn diagram indicated that six proteins, including Cytotoxic T-Lymphocyte Associated Protein 4 (CTLA4), were significantly associated with intratumoral fibrosis (p < 0.05). The GO/KEGG enrichment analysis indicated that the proteins associated with intratumoral fibrosis were involved in the immunity and tumor-infiltrating lymphocytes. The expression of the CTLA4 was negatively correlated with collagen level, confirmed by IHC staining of CTLA4 (p < 0.05).ConclusionsThe study indicated that the intratumoral fibrosis level was negatively correlated with the expression of CTLA4 in the tumor immune microenvironment of the ccRCC, which posed the potential value of targeting the stroma of the tumor, a supplement to immunotherapy. However, the specific mechanism of this association is still unclear and needs further investigation.
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Project description:The tumor microenvironment (TME) is comprised of tumor cells, infiltrating immune cells, and stroma. Multiple reports suggest that the immune cell infiltration (ICI) in TME is strongly associated with responsiveness to immunotherapy and prognosis of certain cancers. Thus far, the ICI profile of pancreatic carcinoma (PC) remains unclear. Here, we employed two algorithms to characterize the ICI profile of PC patients. Based on our results, we identified 2 ICI patterns and calculated the ICI score by using principal component analysis. Furthermore, we revealed that patients with low ICI scores had a better prognosis, compared to high ICI scores. Moreover, we discovered that a low tumor mutation burden (TMB) offered better overall survival (OS), relative to high TMB. In this study, a high ICI score referred to elevated PD-L1/TGF-β levels, increased activation of cell cycle pathway and DNA repair pathway, as well as reduced expression of immune-activation-related genes. We also demonstrated that three metabolic pathways were suppressed in the low ICI score group. These data may explain why a high ICI score equates to a poor prognosis. Based on our analysis, the ICI score can be used as an effective predictor of PC prognosis. Hence, establishing an ICI profile, based on a large patient population, will not only enhance our knowledge of TME but also aid in the development of immunotherapies specific to PC.
| S-EPMC8155731 | biostudies-literature
Project description:Mammalian target of rapamycin (mTOR), a serine/threonine kinase involved in cell proliferation, survival, metabolism and immunity, was reportedly activated in various cancers. However, the clinical role of mTOR in renal cell carcinoma (RCC) is controversial. Here we detected the expression and prognosis of total mTOR and phosphorylated mTOR (p-mTOR) in clear cell RCC (ccRCC) patients, and explored the interactions between mTOR and immune infiltrates in ccRCC. The protein level of mTOR and p-mTOR was determined by western blotting (WB), and their expression was evaluated in 145 ccRCC and 13 non-tumor specimens by immunohistochemistry (IHC). The relationship to immune infiltration of mTOR was further investigated using TIMER and TISIDB databases, respectively. WB demonstrated the ratio of p-mTOR to mTOR was higher in ccRCC than adjacent specimens (n = 3), and IHC analysis elucidated that p-mTOR expression was positively correlated with tumor size, stage and metastasis status, and negatively correlated with cancer-specific survival (CSS). In univariate analysis, high grade, large tumor, advanced stage, metastasis, and high p-mTOR expression were recognized as prognostic factors of poorer CSS, and multivariate survival analysis elucidated that tumor stage, p-mTOR and metastasis were of prognostic value for CSS in ccRCC patients. Further TIMER and TISIDB analyses uncovered that mTOR gene expression was significantly associated with numerous immune cells and immunoinhibitors in patients with ccRCC. Collectively, these findings revealed p-mTOR was identified as an independent predictor of poor survival, and mTOR was associated with tumor immune infiltrates in ccRCC patients, which validated mTOR could be implicated in the initiation and progression of ccRCC.
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