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A single-cell map of dynamic chromatin landscapes of immune cells in renal cell carcinoma (scRNA-Seq and TCR-Seq)

ABSTRACT: A complete chart of the chromatin regulatory elements of immune cells in patients with cancer and their dynamic behavior is necessary to understand the developmental fates and to guide therapeutic strategies. Here, we map, at the single-cell level, the chromatin landscape of immune cells from blood, normal adjacent kidney, and malignant tissue from patients with early-stage clear cell renal cell carcinoma (ccRCC). Specifically in T cells, we catalogue the various cell states dictated by tissue- and developmental stage-specific chromatin accessibility patterns and we infer key chromatin regulators of these states. In the CD8+ T cells progression to dysfunction, we observe an extensive rewiring of the regulatory networks. Unexpectedly, we find that among the transcription factors that orchestrate the path to dysfunction, NFkB, a transcription factor with an established prosurvival role during TCR-mediated activation, is associated with a proapoptotic program in late stages of dysfunction in ccRCC infiltrating CD8+ T cells. Importantly, the epigenomic profiling can stratify RCC patients based on a NFkB-driven proapoptotic signature which was also validated at the gene expression and protein level. Our study provides a rich resource for understanding the functional states and regulatory dynamics of immune cells in RCC.

ORGANISM(S): Homo sapiens

PROVIDER: GSE181061 | GEO | 2021/12/31

REPOSITORIES: GEO

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A single-cell map of dynamic chromatin landscapes of immune cells in renal cell carcinoma (scATAC-Seq)

Project description:A complete chart of the chromatin regulatory elements of immune cells in patients with cancer and their dynamic behavior is necessary to understand the developmental fates and to guide therapeutic strategies. Here, we map, at the single-cell level, the chromatin landscape of immune cells from blood, normal adjacent kidney, and malignant tissue from patients with early-stage clear cell renal cell carcinoma (ccRCC). Specifically in T cells, we catalogue the various cell states dictated by tissue- and developmental stage-specific chromatin accessibility patterns and we infer key chromatin regulators of these states. In the CD8+ T cells progression to dysfunction, we observe an extensive rewiring of the regulatory networks. Unexpectedly, we find that among the transcription factors that orchestrate the path to dysfunction, NFkB, a transcription factor with an established prosurvival role during TCR-mediated activation, is associated with a proapoptotic program in late stages of dysfunction in ccRCC infiltrating CD8+ T cells. Importantly, the epigenomic profiling can stratify RCC patients based on a NFkB-driven proapoptotic signature which was also validated at the gene expression and protein level. Our study provides a rich resource for understanding the functional states and regulatory dynamics of immune cells in RCC.

2021-12-31 | GSE181062 | GEO

dePillis2013 - Mathematical modeling of regulatory T cell effects on renal cell carcinoma treatment

Project description:Mathematical modeling of regulatory T cell effects on renal cell carcinoma treatment Lisette dePillis 1, , Trevor Caldwell 2, , Elizabeth Sarapata 2, and Heather Williams 2, 1. Department of Mathematics, Harvey Mudd College, Claremont, CA 91711 2. Harvey Mudd College, Claremont, CA 91711, United States, United States, United States Abstract We present a mathematical model to study the effects of the regulatory T cells (Treg) on Renal Cell Carcinoma (RCC) treatment with sunitinib. The drug sunitinib inhibits the natural self-regulation of the immune system, allowing the effector components of the immune system to function for longer periods of time. This mathematical model builds upon our non-linear ODE model by de Pillis et al. (2009) [13] to incorporate sunitinib treatment, regulatory T cell dynamics, and RCC-specific parameters. The model also elucidates the roles of certain RCC-specific parameters in determining key differences between in silico patients whose immune profiles allowed them to respond well to sunitinib treatment, and those whose profiles did not. Simulations from our model are able to produce results that reflect clinical outcomes to sunitinib treatment such as: (1) sunitinib treatments following standard protocols led to improved tumor control (over no treatment) in about 40% of patients; (2) sunitinib treatments at double the standard dose led to a greater response rate in about 15% the patient population; (3) simulations of patient response indicated improved responses to sunitinib treatment when the patient's immune strength scaling and the immune system strength coefficients parameters were low, allowing for a slightly stronger natural immune response. Keywords: Renal cell carcinoma, mathematical modeling., sunitinib, immune system, regulatory T cells.

2020-01-06 | BIOMD0000000908 | BioModels

Effect of everolimus on clear cell renal cell carcinoma (ccRCC) cell lines - parental and stem cells

Project description:The aim of this study was to compare effect of everolimus on growth of different renal cell carcinoma (RCC) populations and develop design for experiments to measure the early response of everolimus in clear cell RCC (ccRCC) cell lines including renal cancer stem cells. Gene expression profiling using microarray was performed to determine the early response to everolimus after 3 days of treatment with optimizied concentration of drug in two ccRCC cell lines 1) parental clear cell renal cell carcinoma ccRCC-PCSC (HKPCSC -human parental kidney cancer stem cells) and 2) ccRCC-CSC - clear cell renal cell carcinoma -cancer stem cells (HKCSC - human kidney cancer stem cells).

2018-05-31 | GSE106819 | GEO

Integrative Epigenetic and Gene Expression Analysis of Renal Tumor Progression to Metastasis

Project description:Renal cell carcinoma (RCC) is among the ten most common malignancies. By far, the most common histology is clear cell (ccRCC). The Cancer Genome Atlas and other large scale sequencing studies of ccRCC have been integral to the current understanding of molecular events underlying RCC and its biology. However, these data sets have focused on primary RCC which often demonstrates indolent behavior. In contrast, metastatic disease is the major cause of mortality associated with ccRCC. However, data sets examining metastatic tumor are sparse. We therefore undertook an integrative analysis of gene expression and DNA methylome profiling of metastatic ccRCC in addition to primary RCC and normal kidney. Integrative analysis of the methylome and transcriptome identified over 30 RCC specific genes whose mRNA expression inversely correlated with promoter methylation including several known targets of hypoxia inducible factors (HIFs). Notably, genes encoding several metabolism-related proteins were identified as differentially regulated via methylation. Collectively, our data provide novel insight into biology of aggressive RCC. Furthermore, they demonstrate a clear role for epigenetics in the promotion of HIF signaling and invasive phenotypes in renal cancer.

2018-10-16 | GSE105288 | GEO

Expression data from 786-O renal cell cancer cells treated with pentamidine

Project description:While early stages of clear cell renal cell carcinoma (ccRCC) are curable, survival outcome for metastatic ccRCC remains poor. The purpose of the current study was to apply a new individualized bioinformatics analysis (IBA) strategy to these transcriptome data in conjunction with Gene Set Enrichment Analysis of the Connectivity Map (C-MAP) database to identify and reposition FDA-approved drugs for anti-cancer therapy. We demonstrated that one of the drugs predicted to revert the RCC gene signature towards normal kidney, pentamidine, is effective against RCC cells in culture and in a RCC xenograft model. Most importantly, pentamidine slows tumor growth in the 786-O human ccRCC xenograft mouse model. To determine which genes are regulated by pentamidine in a human RCC cell line, 786-O, we treated these cells with pentamidine and performed transcriptional profiling analysis. We used microarrays to determine the set of genes regulated by pentamidine in 786-O renal cell cancer cells. Total RNA was isolated from 786-O cells treated with 25 Î¼M pentamidine or vehicle control (DMSO) for 6 hours and hybridized to Affymetrix microarrays.

2014-08-05 | E-GEOD-54709 | biostudies-arrayexpress

Expression data from 786-O renal cell cancer cells treated with pentamidine

2014-08-05 | GSE54709 | GEO

Leveraging the Epigenomic Landscape to identify Histology-Specific Master transcription factors and to functionally annotate risk loci in renal cell carcinoma

Project description:We performed histone (ChIP-seq, the Assay for Transposase-Accessible Chromatin using sequencing (ATAC-seq), and RNA sequencing (RNA-seq) on 42 fresh frozen RCC tumor samples (24 ccRCC, 6 pRCC, 12 chRCC). We integrated 153 unique data sets to gain a holistic view of gene regulation in RCC subtypes and 50 candidate histology-specific master transcription factors (TF) to define RCC histologic subtypes. We also integrated RCC GWAS risk SNPs with H3K27ac ChIP-seq and ATAC-seq data and revealed that risk variants were significantly enriched in allelically imbalanced peaks

2022-11-01 | GSE188486 | GEO

Identification of RCC subtype-specific microRNAs – meta-analysis of high-throughput RCC tumor microRNA expression data [RNA-Seq]

Project description:Renal cell carcinoma (RCC) is one of the most common cancers worldwide with nearly non-symptomatic course till advanced stage of disease. RCC can be distinguished into three subtypes: papillary (pRCC), chromophobe (chRCC) and clear cell renal cell carcinoma (ccRCC) representing up to 75% of all RCC cases. Detection and RCC monitoring tools are limited to standard imaging techniques, in combination with non-RCC specific morphological and biochemical read-outs. RCC subtype identification relays mainly on results of pathological examination of tumor slides. Molecular, clinically applicable and ideally non-invasive tools aiding RCC management are still non-existent, although molecular characterization of RCC is relatively advanced. Hence many research efforts concentrate on identification of molecular markers that will assist with RCC sub-classification and monitoring. Due to stability and tissue-specificity miRNAs are promising candidates for such biomarkers. Here we performed a meta-analysis study, utilized seven available NGS and seven microarray RCC studies in order to identify subtype-specific expression of miRNAs. We concentrated on four potentially oncocytoma-specific miRNAs (miRNA-424-5p, miRNA-146b-5p, miRNA-183-5p, miRNA-218-5p), two pRCC (miRNA-127-3p, miRNA-139-5p) and eight ccRCC specific miRNAs (miRNA-200c-3p, miRNA-362-5p, miRNA-363-3p and miRNA-204-5p, 21-5p, miRNA-224-5p, miRNA-155-5p, miRNA-210-3p) and validated their expression in an independent sample set. Additionally, we found ccRCC-specific miRNAs to be differentially expressed in ccRCC Fuhrman grades and identified alterations in their isoform composition in tumor tissue. Our results revealed that changes in expression of selected miRNA might be potentially utilized as a tool aiding ccRCC subclass discrimination and propose a miRNA panel aiding RCC subtype distinction.

2021-02-01 | GSE151419 | GEO

Identification of RCC subtype-specific microRNAs – meta-analysis of high-throughput RCC tumor microRNA expression data [miRNA-Seq]

2021-02-01 | GSE151423 | GEO

The Urinary Peptidome Identifies Prognostic Biomarkers for Small Renal Masses

Project description:Purpose: Renal cell carcinoma (RCC) is often diagnosed incidentally as an early-stage small renal mass (SRM; pT1a, ≤ 4 cm). Increasing concerns surrounding the overtreatment of patients with benign or clinically indolent tumors has led to a shift in current treatment recommendations, especially for elderly and infirm patients. There are currently no available biomarkers to accurately stratify patients according to risk. Therefore, we set out to identify early biomarkers of RCC progression. Experimental Design: We employed label-free LC-MS/MS and targeted parallel-reaction monitoring (PRM) to identify early, non-invasive diagnostic and prognostic biomarkers for early-stage RCC-SRMs. In total, we evaluated 115 urine samples, including 33 renal oncocytoma (≤ 4 cm) cases, 30 progressive and 26 non-progressive clear cell RCC-SRM (ccRCC-SRM) cases, in addition to 26 healthy controls. Results: We identified nine endogenous peptides which displayed significantly elevated expression in ccRCC-SRMs relative to healthy controls. Peptides NVINGGSHAGNKLAMQEF, VNVDEVGGEALGRL, and VVAGVANALAHKYH showed significantly elevated expression in ccRCC-SRMs relative to renal oncocytoma. Additionally, peptides SHTSDSDVPSGVTEVVVKL and IVDNNILFLGKVNRP displayed significantly elevated expression in progressive relative to non-progressive ccRCC-SRMs. Peptide SHTSDSDVPSGVTEVVVKL showed the most significant discriminatory ability (AUC: 0.76, 95% CI: 0.62 to 0.90, p = 0.0027). Patients with elevated SHTSDSDVPSGVTEVVVKL expression had significantly shorter overall survival (HR: 4.13, 95% CI: 1.09 to 15.65, p = 0.024) compared to patients with lower expression. Conclusions: Our in-depth peptidomic analysis identified novel biomarkers for early-stage RCC-SRMs. Characterization of urinary peptides may provide insight into early RCC progression and could potentially help assign patients to appropriate management programs.

2019-08-08 | PXD013111 | Pride

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