Homo sapiens
Ontology highlight
ABSTRACT:
PROVIDER: PRJNA112867 | ENA |
REPOSITORIES: ENA
Similar Datasets
Project description:HLA ligand profiles of primary renal cell carcinoma maintained in metastases.sue
2025-01-24 | E-GEOD-12606 | ExpressionAtlas
Project description:In recent years, several approaches have been taken in the peptide-based immunotherapy of metastatic renal cell carcinoma (RCC), although little is known about HLA presentation on metastases compared to primary tumor and normal tissue of RCC. In this study we compared primary tumor, normal tissue and metastases with the aim of identifying similarities and differences between these tissues. We performed this comparison for two RCC patients on the level of the HLA ligandome using mass spectrometry and for three patients on the level of the transcriptome using oligonucleotide microarrays. The quantitative results show that primary tumor is more similar to metastasis than to normal tissue, both on the level of HLA ligand presentation and mRNA. We were able to characterize a total of 142 peptides in the qualitative analysis of HLA-presented peptides. Six of them were significantly overpresented on metastasis, among them a peptide derived from CD151; fourteen were overpresented on both primary tumor and metastasis compared to normal tissue, among them an HLA ligand derived from tumor protein p53. Thus, we could demonstrate that peptide-based immunotherapy might affect tumor as well as metastasis of RCC, but not healthy kidney tissue. Furthermore we were able to identify several peptides derived from tumor-associated antigens that are suitable for vaccination of metastatic RCC. Three clear cell renal cell carcinomas including autologous normal tissue and autologous metastasis were analyzed. This dataset is part of the TransQST collection.
2008-09-03 | E-GEOD-12606 | biostudies-arrayexpress
Project description:microRNA profiling in primary clear cell renal cell carcinoma
| PRJNA131881 | ENA
Project description:Gene expression profiles of head and neck squamous cell carcinoma and renal cell carcinoma
| PRJNA158253 | ENA
Project description:Genome-wide gene expression profiles of clear cell renal cell carcinoma Identification of molecular targets for treatment of renal cell carcinoma
| PRJNA170718 | ENA
Project description:In recent years, several approaches have been taken in the peptide-based immunotherapy of metastatic renal cell carcinoma (RCC), although little is known about HLA presentation on metastases compared to primary tumor and normal tissue of RCC. In this study we compared primary tumor, normal tissue and metastases with the aim of identifying similarities and differences between these tissues. We performed this comparison for two RCC patients on the level of the HLA ligandome using mass spectrometry and for three patients on the level of the transcriptome using oligonucleotide microarrays. The quantitative results show that primary tumor is more similar to metastasis than to normal tissue, both on the level of HLA ligand presentation and mRNA. We were able to characterize a total of 142 peptides in the qualitative analysis of HLA-presented peptides. Six of them were significantly overpresented on metastasis, among them a peptide derived from CD151; fourteen were overpresented on both primary tumor and metastasis compared to normal tissue, among them an HLA ligand derived from tumor protein p53. Thus, we could demonstrate that peptide-based immunotherapy might affect tumor as well as metastasis of RCC, but not healthy kidney tissue. Furthermore we were able to identify several peptides derived from tumor-associated antigens that are suitable for vaccination of metastatic RCC.
2008-09-03 | GSE12606 | GEO
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.
2024-09-02 | BIOMD0000000908 | BioModels
Project description:Novel miRNA profiles as biomarkers for renal cell carcinoma and upper tract urothelial carcinoma
| PRJNA208630 | ENA