Prognostic Immunophenotyping Clusters of Clear Cell Renal Cell Carcinoma Defined by the Unique Tumor Immune Microenvironment

Background: The tumor microenvironment affects the occurrence and development of cancers, including clear cell renal cell carcinoma (ccRCC). However, how the immune contexture interacts with the cancer phenotype remains unclear.Methods: We identified and evaluated immunophenotyping clusters in ccRCC using machine-learning algorithms. Analyses for functional enrichment, DNA variation, immune cell distribution, association with independent clinicopathological features, and predictive responses for immune checkpoint therapies were performed and validated.Results: Three immunophenotyping clusters with gradual levels of immune infiltration were identified. The intermediate and high immune infiltration clusters (Clusters B and C) were associated with a worse prognosis for ccRCC patients. Tumors in the immune-hot Clusters B and C showed pro-tumorigenic immune infiltration, and these patients showed significantly worse survival compared with patients in the immune-cold Cluster A in the training and testing cohorts (n = 422). In addition to distinct immune cell infiltrations of immunophenotyping, we detected significant differences in DNA variation among clusters, suggesting a high degree of genetic heterogeneity. Furthermore, expressions of multiple immune checkpoint molecules were significantly increased. Clusters B and C predicted favorable outcomes in 64 ccRCC patients receiving immune checkpoint therapies from the FUSCC cohort. In 360 ccRCC patients from the FUSCC validation cohort, Clusters B and C significantly predicted worse prognosis compared with Cluster A. After immunophenotyping of ccRCC was confirmed, significantly increased tertiary lymphatic structures, aggressive phenotype, elevated glycolysis and PD-L1 expression, higher abundance of CD8+ T cells, and TCRn cell infiltration were found in the immune-hot Clusters B and C.Conclusion: This study described immunophenotyping clusters that improved the prognostic accuracy of the immune contexture in the ccRCC microenvironment. Our discovery of the novel independent prognostic indicators in ccRCC highlights the relationship between tumor phenotype and immune microenvironment.

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m6A Regulator-Mediated Methylation Modification Model Predicts Prognosis, Tumor Microenvironment Characterizations and Response to Immunotherapies of Clear Cell Renal Cell Carcinoma

Frontiers in Oncology ◽

10.3389/fonc.2021.709579 ◽

2021 ◽

Vol 11 ◽

Author(s):

Wenhao Xu ◽

Xi Tian ◽

Wangrui Liu ◽

Aihetaimujiang Anwaier ◽

Jiaqi Su ◽

...

Keyword(s):

Renal Cell Carcinoma ◽

Cell Carcinoma ◽

Immune Checkpoint ◽

Renal Cell ◽

Clustering Algorithm ◽

Clear Cell ◽

Treg Cells ◽

Consensus Clustering ◽

Cell Renal Cell Carcinoma ◽

Immune Contexture

BackgroundThis study aims to establish an N6-methyladenosine (m6A) RNA methylation regulators-mediated methylation model and explore its role in predicting prognostic accuracy of immune contexture and characterizations of clear cell renal cell carcinoma (ccRCC).MethodsThe m6A modification subclasses (m6AMS) were identified by unsupervised cluster analysis and three clusters were determined by consensus clustering algorithm in a discovering cohort. Testing and real-world validation cohorts were used to identify predictive responses for immune checkpoint therapies (ICTs) of m6AMS.ResultsPrognostic implications landscape of m6A regulators in cancers and its differential expression levels in ccRCC patients were identified. Based on discovering cohort, ccRCC were automatically divided into three m6AMS, and cluster 3 showed significant worse survival than cluster 1/2. Importantly, it was found that the immune checkpoint molecules expression was significantly elevated in cluster 3. Besides, m6A scoreLow group (cluster 1&2) have significantly elevated TIDE score compared with m6A scoreHigh group (cluster 3). There was conspicuous tertiary lymphoid tissue, aggressive phenotype, elevated glycolysis, expression of PD-L1, abundance of CD8+ T cells, CD4+ FOXP3+ Treg cells and TCRn immune cells infiltration in the high m6A score group. Interestingly, there are significantly increased patients with clinical benefit in m6A scoreHigh group in 368 patients receiving ICTs from testing IMvigor210 (n = 292) and validation FUSCC (n = 55) cohorts.ConclusionOur discovery highlights the relationship between tumor epigenetic heterogeneity and immune contexture. Immune-rejection cluster 3 has pro-tumorigenic immune infiltration, and shows significant clinical benefits for ccRCC patients receiving ICTs, enabling patient selection for future clinical treatment.

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The Correlation Between Novel Survival-related Alternative Splicing Signature and Prognostic Prediction and Immune Microenvironment in Clear Cell Renal Cell Carcinoma

10.21203/rs.3.rs-1209772/v1 ◽

2021 ◽

Author(s):

Wengang Jian ◽

Gang Wang ◽

Yipeng Yu ◽

Licheng Cai ◽

Yongchun Yu ◽

...

Keyword(s):

Renal Cell Carcinoma ◽

Immune Cell ◽

Risk Model ◽

Clear Cell ◽

Cell Infiltration ◽

Immune Checkpoints ◽

Splicing Factors ◽

Immune Cell Infiltration ◽

Immune Microenvironment ◽

Cell Renal Cell Carcinoma

Abstract BackgroundAlthough extensive researches related alternative splicing (AS) as the prognostic markers of patients in cancers, which remains unknown in clear cell renal cell carcinoma (ccRCC), especially in immunotherapy. Therefore, a novel survival-associated AS signature was established to predict prognosis of patients and explored its correlation with immune cell infiltration or immune checkpoint expression to explain the phenomenon of resistance to immunotherapy in ccRCC.Methodsccording to AS data, clinical information and gene expression data in ccRCC, overall survival-related AS events was identified and further the AS-related prognostic risk model established by LASSO regression and multi-Cox regression analysis was evaluated by Kaplan-Meier survival analysis, the ROC curves and a nomogram model. Then we clarified the biological processes and pathways by GSEA, and further measured the immune cell infiltration by ESTIMATE, CIBERSORT and ssGSEA. Finally we analysed the clinical features and immune features of different parental genes, and quested the splicing factors regulating riskScore-related AS events by Spearman correlation analysis.ResultsWe obtained the most significant 5 AS events, including C4orf19|69001|AT, UACA|31438|AP, FAM120C|89237|AT, TRIM16L|39629|AP and SEC31A|100881|ES, to establish the prognostic risk model, and further illustrated the stability and importance of the riskScore prognostic signatures. Then we found that in high risk group, most of the top 10 GO enrichments and the KEGG pathway were closely related to the immunity, and the higher immune cell infiltration, and higher expression of classic immune checkpoints such as PD1 and CTLA4. In addition, 6 different parental genes were obtained, including C4orf19, ARHGAP24 DNASE1L3, P4HA1, SLC39A14 and TAF1D. These 6 genes could not be the independent prognostic signatures, but the expression of these genes was closely related to immune cells infiltration and the expression of immune checkpoints. Finally, we got aberrant 52 splicing factors regulating riskScore-related AS events.ConclusionOur study discovered that overall survival-related AS events mediated by aberrant splicing factors can be constructed a prognostic risk model to predict prognosis of patiens and utilized to index the situation of immune cell infiltration and immune checkpoint expression that impact tumor immune microenvironment in ccRCC.

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Quantifying T- and B-cell immune receptor distribution diversity to uncover their clinical relevance in clear cell renal cell carcinoma

10.1101/2021.06.15.21258987 ◽

2021 ◽

Author(s):

Meghan C Ferrall-Fairbanks ◽

Nicholas Chakiryan ◽

Boris I Chobrutskiy ◽

Youngchul Kim ◽

Jamie K Teer ◽

...

Keyword(s):

Renal Cell Carcinoma ◽

B Cell ◽

Cell Carcinoma ◽

Renal Cell ◽

Immune Cell ◽

Clear Cell ◽

Immune Microenvironment ◽

Cell Renal Cell Carcinoma ◽

Cdr3 Sequence ◽

Sequence Distributions

Immune-modulating systemic therapies are often used to treat metastatic clear cell renal cell carcinoma (ccRCC). Used alone, sequence-based biomarkers neither accurately capture patient dynamics nor the tumor-immune microenvironment. To better understand the tumor ecology of this immune microenvironment, we used complementarity determining region-3 (CDR3) sequence recovery counts of the tumor infiltrating lymphocytes, and quantified tumor infiltration by sequences recovered from patient tumors by applying a generalized diversity index (GDI) to CDR3 sequence distributions across two distinct ccRCC cohorts. GDI can be understood as a curve over a continuum of diversity scales and allows sensitive characterization of distributions to capture richness, evenness, and subsampling uncertainty, along with other important metrics. For example, richness quantifies the total unique sequence count, while evenness quantifies similarities across sequence frequencies. We observed significant differences in receptor sequence diversity across gender and race. Further, our analysis revealed that patients with larger and more clinically aggressive tumors had increased richness of recovered tumoral CDR3 sequences, specifically in those from T-cell receptor alpha and B-cell immunoglobulin lambda light chain. We identified a novel and robust measure of distribution evenness, using GDI's inflection point (IP). High IP values associated with improved overall survival, suggesting that normal-like sequence distributions lead to better outcomes. Our results propose a new quantitative tool that can be used to better characterize patient-level differences related to immune cell infiltration and, can be used to identify unique characteristics of tumor-infiltrating lymphocyte heterogeneity in ccRCC and other malignancies.

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Construction of an Immune-related Gene Model for Predicting Prognosis and Immune Infiltration in Clear Cell Renal Cell Carcinoma

10.21203/rs.3.rs-1186623/v1 ◽

2022 ◽

Author(s):

Fengping Ji ◽

Xin Liu ◽

Yanping Zhang ◽

Erpeng Liu ◽

Jianguo Wen

Keyword(s):

Renal Cell Carcinoma ◽

Cell Carcinoma ◽

Immune Checkpoint ◽

Renal Cell ◽

Clear Cell ◽

Enrichment Analysis ◽

Functional Enrichment Analysis ◽

Functional Enrichment ◽

Cell Renal Cell Carcinoma ◽

Immune Infiltration

Abstract Background: Clear cell renal cell carcinoma (ccRCC) is a common pathological type of kidney cancer with high immune infiltration that has been proven to be treatable with immune checkpoint inhibitor (ICI) therapy. However, the role of immunity in ccRCC remains poorly understood. Therefore, this paper aimed to develop and validate a novel immune-related prognostic marker to predict both the overall survival rate (OS) of ccRCC patients and the response to ICI therapy.Methods: Based on the transcriptome and clinicopathological data of ccRCC from The Cancer Genome Atlas (TCGA) dataset and immune-related genes (IRGs) from immune datasets, IRGs related to prognosis were screened to construct an IRG prognostic index (IRGPI) via coexpression analysis and Cox regression. After verifying that IRGPI was a prognostic indicator independent of clinical parameters, a nomogram was established. In addition, functional enrichment analysis, the CIBERSORT algorithm and single-sample gene set enrichment analysis (ssGSEA) were performed to compare the molecular and immune characteristics of IRGPI-defined subgroups. Finally, the expression of immunosuppressive genes, tumor mutational burden (TMB) and the TIDE algorithm were used to predict the response of ICI therapy in different IRGPI subgroups. Results: A total of 11 IRGs (IFNG, XCL1, APOBEC3G, CD86, CXCR3, IL10RA, IL2RG, CD244, SH2D1A, CD3D and FCER1G) were included in the IRGPI module. IRGPIhigh patients had a worse OS and had poorer clinical pathological status than IRGPIlow patients. A nomogram containing clinical features and IRGPI scores may guide the clinical practice of ccRCC. Chemokine signaling pathways were mainly involved in functional enrichment analysis. Furthermore, the IRGPI could effectively reflect the immune characteristics and immune checkpoint gene expression of ccRCC and the response to ICI therapy.Conclusions: The IRGPI is a promising biomarker for determining prognosis and has the potential to be used to predict immunotherapy response in ccRCC.

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