Tumor infiltrating mast cells determine oncogenic HIF-2α-conferred immune evasion in clear cell renal cell carcinoma

Xiong, Ying; Liu, Li; Yu, Xinbing; Qi, Yangyang; Chen, Yifan; Chen, Lingli; Zhang, Peipei; Kong, Yunyi; Qu, Yang; Wang, Zewei; Lin, Zhiyuan; Chen, Xiang; Xiang, Zhuoyi; Bai, Qi; Zhang, Weijuan; Yang, Yuanfeng; Guo, Jianming

doi:10.1007/s00262-019-02314-y

Cited by 40 publications

(37 citation statements)

References 39 publications

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“…Immunological analysis of the TIME (immunoscore) shows great promise for improved prognosis and prediction of response to immunotherapy. Several reports have demonstrated that immune scores and stromal scores calculated based on the ESTIMATE algorithm could predict the infiltration of non-tumor cells, by analyzing specific gene expression signature of immune and stromal cells (10)(11)(12)(13).…”

Section: Introductionmentioning

confidence: 99%

A signature of tumor immune microenvironment genes associated with the prognosis of non‑small cell lung cancer

Zhang

et al. 2020

Oncol Rep

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Establishing a prognostic genetic signature closely related to the tumor immune microenvironment (TIME) to predict clinical outcomes is necessary. Using the Gene Expression Omnibus (GEO) database of a non-small cell lung cancer (NSCLC) cohort and the immune score derived from the Estimation of Stromal and Immune cells in Malignant Tumours using Expression data (ESTIMATE) algorithm, we applied the least absolute shrinkage and selection operator (LASSO) Cox regression model to screen a 10-gene signature among the 448 differentially expressed genes and found that the risk prediction models constructed by 10 genes could be more sensitive to prognosis than TNM (Tumor, Lymph node and Metastasis) stage (P=0.006). The CIBERSORT method was applied to quantify the relative levels of different immune cell types. It was found that the ratio of eosinophils, mast cells (MCs) resting and CD4 T cells memory activated in the low-risk group was higher than that in the high-risk group, and the difference was statistically significant (P= 0.003, P= 0.014 and P= 0.018, respectively). Inconsistently, the ratio of resting natural killer (NK) cells and activated plasma cells in the low-risk group was significantly lower than that in the high-risk group (P= 0.05 and P=0.009, respectively). Kaplan-Meier survival results showed that patients of the high-risk group had significantly shorter overall survival (OS) than those of the low-risk group in the training set (P<0.001). Furthermore, Kaplan-Meier survival showed that patients of the high-risk group had significantly shorter OS than those of the low-risk group (P=0.0025 and P=0.0157, respectively) in the validation set [GSE31210 and TCGA (The Cancer Genome Atlas)]. The 10-gene signature was found to be an independent risk factor for prognosis in univariate and multivariate Cox proportional hazard regression analyses (P<0.001). In addition, it was found that the risk model constructed by the 10-gene signature was related to the clinical related factors in logistic regression analysis. The genetic signature closely related to the immune microenvironment was found to be able to predict differences in the proportion of immune cells (eosinophils, resting MCs, memory activated CD4 T cells, resting NK cells and plasma cells) in the risk model. Our findings suggest that the genetic signature closely related to TIME could predict the prognosis of NSCLC patients, and provide some reference for immunotherapy.

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Section: Introductionmentioning

confidence: 99%

A signature of tumor immune microenvironment genes associated with the prognosis of non‑small cell lung cancer

Zhang

et al. 2020

Oncol Rep

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“…erefore, the exact immune cell content in different tumors of NSCLC remains accurately undetermined. Several reports indicated that the relative levels of distinct immune cell types by the analytical platform CIBERSORT could estimate the immune cell composition in a tumor [21][22][23].…”

Section: Introductionmentioning

confidence: 99%

A 5-Gene Signature Is Closely Related to Tumor Immune Microenvironment and Predicts the Prognosis of Patients with Non-Small Cell Lung Cancer

Wang

et al. 2020

BioMed Research International

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Purpose. Establishing prognostic gene signature to predict clinical outcomes and guide individualized adjuvant therapy is necessary. Here, we aim to establish the prognostic efficacy of a gene signature that is closely related to tumor immune microenvironment (TIME). Methods and Results. There are 13,035 gene expression profiles from 130 tumor samples of the non-small cell lung cancer (NSCLC) in the data set GSE103584. A 5-gene signature was identified by using univariate survival analysis and Least Absolute Shrinkage and Selection Operator (LASSO) to build risk models. Then, we used the CIBERSORT method to quantify the relative levels of different immune cell types in complex gene expression mixtures. It was found that the ratio of dendritic cells (DCs) activated and mast cells (MCs) resting in the low-risk group was higher than that in the high-risk group, and the difference was statistically significant (P<0.001 and P=0.03). Pathway enrichment results which were obtained by performing Gene Set Variation Analysis (GSVA) showed that the high-risk group identified by the 5-gene signature had metastatic-related gene expression, resulting in lower survival rates. Kaplan–Meier survival results showed that patients of the high-risk group had shorter disease-free survival (DFS) and overall survival (OS) than those of the low-risk group in the training set (P=0.0012 and P<0.001). The sensitivity and specificity of the gene signature were better and more sensitive to prognosis than TNM (tumor/lymph node/metastasis) staging, in spite of being not statistically significant (P=0.154). Furthermore, Kaplan–Meier survival showed that patients of the high-risk group had shorter OS and PFS than those of the low-risk group (P=0.0035, P<0.001, and P<0.001) in the validating set (GSE31210, GSE41271, and TCGA). At last, univariate and multivariate Cox proportional hazard regression analyses were used to evaluate independent prognostic factors associated with survival, and the gene signature, lymphovascular invasion, pleural invasion, chemotherapy, and radiation were employed as covariates. The 5-gene signature was identified as an independent predictor of patient survival in the presence of clinical parameters in univariate and multivariate analyses (P<0.001) (hazard ratio (HR): 3.93, 95% confidence interval CI (2.17–7.1), P=0.001, (HR) 5.18, 95% CI (2.6995–9.945), P<0.001), respectively. Our 5-gene signature was also related to EGFR mutations (P=0.0111), and EGFR mutations were mainly enriched in low-risk group, indicating that EGFR mutations affect the survival rate of patients. Conclusion. The 5-gene signature is a powerful and independent predictor that could predict the prognosis of NSCLC patients. In addition, our gene signature is correlated with TIME parameters, such as DCs activated and MCs resting. Our findings suggest that the 5-gene signature closely related to TIME could predict the prognosis of NSCLC patients and provide some reference for immunotherapy.

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“…However, these tumours also display elevated scores for monocytes, neutrophils, and mast cells, which in some settings contribute to suppression of anti-tumour CD8 T-cell responses. Immunosuppressive mast cells were shown to correlate with HIF2A mRNA abundance in human ccRCC 49 . Thus, the extent of T cell mediated anti-tumour immunity is likely to be determined by the balance of the abundance and activities of several different immune cell types in a manner that is partly influenced by HIF2A expression.…”

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confidence: 95%

HIF-1α and HIF-2α differently regulate tumour development and inflammation of clear cell renal cell carcinoma in mice

et al. 2020

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Mutational inactivation of VHL is the earliest genetic event in the majority of clear cell renal cell carcinomas (ccRCC), leading to accumulation of the HIF-1α and HIF-2α transcription factors. While correlative studies of human ccRCC and functional studies using human ccRCC cell lines have implicated HIF-1α as an inhibitor and HIF-2α as a promoter of aggressive tumour behaviours, their roles in tumour onset have not been functionally addressed. Herein we show using an autochthonous ccRCC model that Hif1a is essential for tumour formation whereas Hif2a deletion has only minor effects on tumour initiation and growth. Both HIF-1α and HIF-2α are required for the clear cell phenotype. Transcriptomic and proteomic analyses reveal that HIF-1α regulates glycolysis while HIF-2α regulates genes associated with lipoprotein metabolism, ribosome biogenesis and E2F and MYC transcriptional activities. HIF-2α-deficient tumours are characterised by increased antigen presentation, interferon signalling and CD8 + T cell infiltration and activation. Single copy loss of HIF1A or high levels of HIF2A mRNA expression correlate with altered immune microenvironments in human ccRCC. These studies reveal an oncogenic role of HIF-1α in ccRCC initiation and suggest that alterations in the balance of HIF-1α and HIF-2α activities can affect different aspects of ccRCC biology and disease aggressiveness.

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Tumor infiltrating mast cells determine oncogenic HIF-2α-conferred immune evasion in clear cell renal cell carcinoma

Cited by 40 publications

References 39 publications

A signature of tumor immune microenvironment genes associated with the prognosis of non‑small cell lung cancer

A signature of tumor immune microenvironment genes associated with the prognosis of non‑small cell lung cancer

A 5-Gene Signature Is Closely Related to Tumor Immune Microenvironment and Predicts the Prognosis of Patients with Non-Small Cell Lung Cancer

HIF-1α and HIF-2α differently regulate tumour development and inflammation of clear cell renal cell carcinoma in mice

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