Ontology highlight
ABSTRACT:
PROVIDER: PRJNA838259 | ENA |
REPOSITORIES: ENA
Dataset's files
| Action | DRS | |||
|---|---|---|---|---|
| SRR19214442_1.fastq.gz | Fastqsanger.gz | |||
| SRR19214442_2.fastq.gz | Fastqsanger.gz | |||
| SRR19214443_1.fastq.gz | Fastqsanger.gz | |||
| SRR19214443_2.fastq.gz | Fastqsanger.gz | |||
| SRR19214444_1.fastq.gz | Fastqsanger.gz |
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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:We aim to identify profiling of circRNAs in renal tissue from renal cell carcinoma patients. In this study, seven paired frozen carcinoma tissues as well as normal tissues from patients with renal cell carcinoma were used for circRNA profiling by second generation of RNA sequencing.
2019-05-01 | GSE108735 | GEO
Project description:Renal papillary cell carcinoma
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Project description:Aberrant DNA methylation is common in cancer. To associate DNA methylation with gene function, we performed RNAseq upon tumor tissue and matched normal tissues of two ccRCC (clear cell renal cell carcinoma) patients. To quantify 5mC and 5hmC level in each CG site at genome-wide level, we performed BS-seq and TAB-seq upon tumor tissue and matched normal tissues of two ccRCC (clear cell renal cell carcinoma) patients, respectively. mRNA profiles of tumor and matched normal tissues from two ccRCC patients were generated by deep sequencing, using Hiseq 2000. Single-nucleotide-resolution, whole-genome, 5mC and 5hmC profiles of tumor and matched normal tissues from two ccRCC (clear cell renal cell carcinoma) patients were generated by deep sequencing, using Hiseq 2000.
2016-02-29 | E-GEOD-63183 | biostudies-arrayexpress
Project description:Background: Renal cell carcinoma, which presents no significant clinical manifestations at early stages, is one of the few tumors with an increasing worldwide incidence. This malignancy cannot be directly detected by tumor markers in body fluid without information from imaging examinations. Approximately 30–40% of patients with kidney cancer exhibit distant metastasis at diagnosis, and their 5-year survival rate is much lower than that of patients with early-stage renal cell carcinoma. Thus, the early diagnosis of renal cell carcinoma is extremely important. The aim of this study was to investigate the utility of urinary exosomal long noncoding RNA (lncRNA) as a new potential diagnostic marker for renal cell carcinoma. Methods: Exosomes were isolated by ultracentrifugation from 50-ml urine samples from 10 patients with clear cell renal cell carcinoma and 10 matched healthy donors. Differentially expressed lncRNAs were analyzed by next-generation sequencing and further validated in kidney cancer cell lines, tissues and urinary exosomes. Then, we evaluated the sensitivity, specificity, clinical diagnostic value and stability of the selected lncRNAs. Results: The levels of lncRNAs NR_040448 and NR_033390 in urinary exosomes can likely indicate the presence of renal cell carcinoma. In addition, RNA protected by exosomes was stable enough to serve as a biomarker. Conclusion: Urinary exosomal lncRNA is a promising marker for the early diagnosis of renal cell carcinoma.
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Project description:This is a Phase 1, open-label, dose-escalation trial of avelumab [antibody targeting programmed death ligand 1 (anti PD-L1)] with consecutive parallel group expansion in participants with selected tumor indications. New recruitment is open for all active cohorts.
Active cohorts: Escalation revised dosing regimen cohort.
Closed cohorts: Non-small cell lung cancer (NSCLC, first line), NSCLC (post-platinum), metastatic breast cancer (MBC), colorectal cancer (CRC), urothelial carcinoma (secondary), mesothelioma, gastric/GEJ cancer (first line switch maintenance and second line), and ovarian cancer (secondary and platinum refractory + liposomal doxorubicin), renal cell carcinoma (second line) melanoma and head, neck squamous cell carcinoma (HNSCC), castrate-resistant prostate cancer (CRPC), adrenocortical carcinoma (ACC) urothelial carcinoma (efficacy), gastric/gastroesophageal junction (GEJ) cancer (third line), renal cell carcinoma (RCC, first line) and escalation phase .
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