Immune infiltration in renal cell carcinoma

Zhang, Shichao; Zhang, Erdong; Long, Ju; Hu, Ziyi; Peng, Jian; Liu, Lina; Tang, Fuzhou; Li, Long; Ouyang, Yu; Zeng, Zhu

doi:10.1111/cas.13996

Cited by 270 publications

(290 citation statements)

References 45 publications

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“…However, traditional methods for detecting immune-infiltrating cells, such as flow cytometry or immunohistochemistry (17), have the defection of not being able to comprehensively assessing different immune cells or discriminate the closely related cell subpopulations. These methods are heavily limited by the number of fluorescent channels and immunophenotypic markers available (16). CIBERSORT, a bioinformatics tool, was known as the most accurate method available (17) to integrate transcriptomics profiles and analyze the large-scale immune landscape.…”

Section: Discussionmentioning

confidence: 99%

“…CIBERSORT, a deconvolution algorithm based on normalized gene expression profiles, has been validated by fluorescenceactivated cell sorting (FACS), which can be used to characterize 22 types of immune infiltration cell composition of complex samples (16). Each gene expression series was separately uploaded to the CIBERSORT web tool (https://cibersort.stanford.…”

Section: Cibersort Estimationmentioning

confidence: 99%

“…Recently, a newly proposed computational algorithm, known as "Cell type Identification By Estimating Relative Subsets Of RNA Transcripts (CIBERSORT), " was developed (15) and successfully applied to analyse the immune cell types in several malignant tumors like colorectal cancer (12), gastric cancer (13), and renal cell carcinoma (16). Therefore, in the present study, we employed CIBERSORT, for the first time, to determine the relative fractions of 22 immune cell subsets in GBM using gene expression profiles.…”

Section: Introductionmentioning

confidence: 99%

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Development of an Immune Infiltration-Related Prognostic Scoring System Based on the Genomic Landscape Analysis of Glioblastoma Multiforme

Tang

2020

Front. Oncol.

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Introduction: Glioblastoma multiforme (GBM) is the most common deadly brain malignancy and lacks effective therapies. Immunotherapy acts as a promising novel strategy, but not for all GBM patients. Therefore, classifying these patients into different prognostic groups is urgent for better personalized management. Materials and Methods: The Cell type Identification by Estimating Relative Subsets of RNA Transcripts (CIBERSORT) algorithm was used to estimate the fraction of 22 types of immune-infiltrating cells, and least absolute shrinkage and selection operator (LASSO) Cox regression analysis was performed to construct an immune infiltration-related prognostic scoring system (IIRPSS). Additionally, a quantitative predicting survival nomogram was also established based on the immune risk score (IRS) derived from the IIRPSS. Moreover, we also preliminarily explored the differences in the immune microenvironment between different prognostic groups. Results: There was a total of 310 appropriate GBM samples (239 from TCGA and 71 from CGGA) included in further analyses after CIBERSORT filtering and data processing. The IIRPSS consisting of 17 types of immune cell fractions was constructed in TCGA cohort, the patients were successfully classified into different prognostic groups based on their immune risk score (p = 1e-10). What's more, the prognostic performance of the IIRPSS was validated in CGGA cohort (p = 0.005). The nomogram also showed a superior predicting value. (The predicting AUC for 1-, 2-, and 3-year were 0.754, 0.813, and 0.871, respectively). The immune microenvironment analyses reflected a significant immune response and a higher immune checkpoint expression in high-risk immune group. Conclusion: Our study constructed an IIRPSS, which maybe valuable to help clinicians select candidates most likely to benefit from immunological checkpoint inhibitors (ICIs) and laid the foundation for further improving personalized immunotherapy in patients with GBM.

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Section: Discussionmentioning

confidence: 99%

Section: Cibersort Estimationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Development of an Immune Infiltration-Related Prognostic Scoring System Based on the Genomic Landscape Analysis of Glioblastoma Multiforme

Tang

2020

Front. Oncol.

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“…Again, the immune cell populations and their prognostic implications are subtype-specific; for instance, CD8+ T-lymphocyte infiltration associated with improved overall-survival in chRCC, while in pRCC a high infiltration by M1 macrophages associated with better patient outcome. In ccRCC, increased populations of regulatory T-lymphocytes resulted in poorer survival [78]. This stresses that the heterogeneity of RCC is also extended into the immune microenvironment, in a subtype-specific way.…”

Section: The Connection Between Genetic Epigenetic and Immune Landsmentioning

confidence: 98%

Targeting the Immune system and Epigenetic Landscape of Urological Tumors

Lobo

Jerónimo

Henrique

2020

IJMS

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In the last years, we have witnessed remarkable advances in targeted therapies for cancer patients. There is a growing effort to either replace or reduce the dose of unspecific, systemic (chemo)therapies, given the associated short- and long-term side effects, by introducing more specific targeted therapies as single or combination agents. Due to the well-known implications of the immune system and epigenetic landscape in modulating cancer development, both have been explored as potential targets in several malignancies, including those affecting the genitourinary tract. As the immune system function is also epigenetically regulated, there is rationale for combining both strategies. However, this is still rather underexplored, namely in urological tumors. We aim to briefly review the use of immune therapies in prostate, kidney, bladder, and testicular cancer, and further describe studies providing supporting evidence on their combination with epigenetic-based therapies.

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“…For instance, tumor‐associated macrophages are significant for promoting or blocking tumor progression . In pRCC, M1 macrophages were associated with a favorable outcome, while M2 macrophages indicated a worse outcome . In addition, CD8+ T cells have been associated with improved clinical outcomes and response to immunotherapy.…”

Section: Introductionmentioning

confidence: 99%

High‐dimensional single‐cell proteomics analysis reveals the landscape of immune cells and stem‐like cells in renal tumors

Zhao

et al. 2019

Clinical Laboratory Analysis

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3 Guangxi key laboratory for genomic and personalized medicine, Guangxi collaborative innovation center for genomic and personalized medicine, Abstract Background: Renal tumors are highly heterogeneous, and identification of tumor heterogeneity is an urgent clinical need for effective treatment. Mass cytometry (MC) can be used to perform high-dimensional single-cell proteomics analysis of heterogeneous samples via cytometry by time-of-flight (CyTOF), in order to achieve more accurate observation and classification of phenotypes within a cell population. This study aimed to develop a high-dimensional MC method for the detection and analysis of heterogeneity in renal tumors. Materials and Methods: We collected tissue samples from 8 patients with different types of renal tumors. Single-cell suspensions were prepared and stained using a panel of 28 immune cell-centric antibodies and a panel of 21 stem-like cell-centric antibodies. The stained cells were detected using CyTOF. Result: Renal tumors were divided into 25 immune cell subsets (4 CD4+ T cells, 7 CD8+ T cells, 1 B cells, 8 macrophages, 1 dendritic cells, 2 natural killer (NK) cells, 1 granulocyte, and 1 other subset) and 7 stem-like cells subsets (based on positivity of vimentin, CD326, CD34, CD90, CD13, CD44, and CD47). Different types of renal tumors have different cell subsets with significantly different characteristics. Conclusion: High-dimensional single-cell proteomics analysis using MC aids in the discovery and analysis of renal tumors heterogeneity. Additionally, it can be used to accurately classify the immune cell population and analyze the expression of stem cell-related markers in renal tumors. Our findings provide a valuable resource for deciphering tumor heterogeneity and might improve the clinical management of patients with renal tumors. K E Y W O R D S cancer stem cells, mass cytometry, renal tumors, tumor heterogeneity, tumor microenvironment How to cite this article: Li Z, Hu J, Qin Z, et al. Highdimensional single-cell proteomics analysis reveals the landscape of immune cells and stem-like cells in renal tumors. J Clin Lab Anal. 2020;34:e23155. https ://doi.

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Immune infiltration in renal cell carcinoma

Cited by 270 publications

References 45 publications

Development of an Immune Infiltration-Related Prognostic Scoring System Based on the Genomic Landscape Analysis of Glioblastoma Multiforme

Development of an Immune Infiltration-Related Prognostic Scoring System Based on the Genomic Landscape Analysis of Glioblastoma Multiforme

Targeting the Immune system and Epigenetic Landscape of Urological Tumors

High‐dimensional single‐cell proteomics analysis reveals the landscape of immune cells and stem‐like cells in renal tumors

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