Homo sapiens
Ontology highlight
ABSTRACT:
PROVIDER: PRJNA266413 | ENA |
REPOSITORIES: ENA
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Project description:In this study, 19 tumor samples from patients with renal cell carcinoma (RCC)-end-stage renal disease (ESRD) were analyzed by array comparative genomic hybridization (array CGH) using the Agilent Whole Human Genome 4× Array. 19 cystic disease samples from patients with RCC-ESRD
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Project description:Renal cells Carcinoma (RCC) Genome variation profiling by genome tiling array with Nimblegen CGX6 platform
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Project description:Intraperitoneal administration of ferric nitrilotriacetate (Fe-NTA) initiates Fenton reaction in the renal proximal tubules of rodents that ultimately leads to a high incidence of renal cell carcinoma (RCC) after repeated treatment. We performed high-resolution microarray comparative genomic hybridization to identify characteristics in the genomic profiles of oxidative stress-induced rat RCCs. The results revealed extensive large-scale genomic alterations with a preference for deletion. Carcinogenesis protocol was performed using male F1 hybrid rats between Fischer344 and Brown-Norway strains. 13 primary tumors and 2 cell lines of Fe-NTA induced RCCs were profiled on Agilent 185K rat genome CGH microarrays. One RCC sample of a female Eker rat was also analyzed with the same Agilent 185K rat genome CGH microarray.
2012-08-30 | E-GEOD-36101 | biostudies-arrayexpress
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:Accurate diagnostic discrimination of benign renal oncocytoma (OC) and malignant renal cell carcinomas (RCC) is not only useful for planning appropriate treatment strategies of patients with renal masses but also for estimating prognosis. Classification of renal neoplasms solely by histopathology can often be challenging for a variety of reasons. The aim of this study was to develop and validate a genomic algorithm for molecular classification of renal cortical neoplasms that could be implemented in a routine clinical diagnostic setting. Using TCGA (The Cancer Genome Atlas) copy number profiles of over 600 RCC specimens, prior FISH studies and published literature, a classification algorithm was developed consisting of 15 genomic markers: loss of VHL, 3p21, 8p, and chromosomes 1, 2, 6, 10 and 17, and gain of 5qter, 16p, 17q, 20q, and chromosomes 3, 7, and 12. Criteria for scoring specimens for the presence of each genomic marker were established. As validation, 191 surgically resected formalin-fixed paraffin-embedded renal neoplasms were blindly submitted to targeted array-CGH and were classified according to the algorithm. Upon histologic re-review leading to exclusion of three specimens and using histology as the gold standard, the algorithm correctly classified 58 of 62 (93%) clear cell renal cell carcinoma, 51 of 56 (91%) papillary RCC, and 33 of 34 (97%) chromophobe RCC. Of the 36 OC specimens, 17 were classified as OC, two as a malignant subtype, 14 as benign, and three exhibited alterations not associated with a specific subtype. In ten of the latter two groups, CCND1-rearrangement was detected by fluorescence in situ hybridization, affording a classification as OC. Together, 33 of 36 (92%) OC were classified as OC or benign. For the entire validation cohort, an overall diagnostic sensitivity of 93% and above 97% specificity was achieved, suggesting that the implementation of genome-based molecular classification in a clinical diagnostic setting could impact the overall management and outcome of patients with renal tumors. A total of 191 RCC FFPE samples are analyzed including 63 clear cell RCC (ccRCC), 57 papillary RCC (pRCC), 35 chromophobe RCC (chrRCC) and 36 oncocytoma (OC). Two-color array-comparative genomic hybdrization on custom designed using RCC DNA as test and normal sex-matched DNA as reference.
2015-04-29 | E-GEOD-57162 | biostudies-arrayexpress