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Ena-DATASET-MUG-23-01-2020-15:19:23:857-102 - samples

ABSTRACT: Paired end shallow whole genome sequencing (sWGS) data for the identification of somatic copy number alterations (SCNA) and the estimation of tumor fractions in plasma DNA of renal cell carcinoma (RCC) patients (MonRec Cohort)

PROVIDER: EGAD00001005804 | EGA |

REPOSITORIES: EGA

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ena-DATASET-MUG-02-12-2021-20:16:00:427-345 - samples

Project description:Paired end shallow whole genome sequencing (sWGS) data for the identification of genomewide somatic copy number alterations (SCNA) and the estimation of tumor fractions.

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ena-DATASET-MUG-01-09-2020-10:55:42:997-687 - samples

Project description:Shallow whole-genome sequencing (sWGS) data for the identification of somatic copy number alterations (SCNA) and the estimation of tumor fractions in plasma DNA of metastatic colorectal cancer patients (mCRC).

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ena-DATASET-MUG-04-05-2020-16:04:36:243-580 - samples

Project description:Paired end (47/98) and single end (51/98) shallow whole genome sequencing (sWGS) data for the identification of somatic copy number alterations (SCNA) and the estimation of tumor fractions in plasma DNA of colorectal cancer (CRC) patients.

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e50a5f0d-c339-4494-9d1d-f2b7961701dd - samples

Project description:Paired end shallow whole genome sequencing (sWGS) data (FASTQ) for the identification of genomewide somatic copy number alterations (SCNA) and the estimation of tumor fractions.

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ena-DATASET-MUG-25-01-2021-19:08:23:000-259 - samples

Project description:Paired end shallow whole genome sequencing (sWGS) data for the identification of somatic copy number alterations (SCNA) and the estimation of tumor fraction and ploidy sorted malignant CD3+/Vb+ T-cells and corresponding CD19+ non-malignant B-cells

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ena-DATASET-MUG-25-01-2020-09:58:43:375-169 - samples

Project description:Paired end shallow whole genome sequencing (sWGS) data of tumor tissue from RCC patients (DAIMOND cohort)

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ena-DATASET-MUG-25-01-2020-09:55:51:618-168 - samples

Project description:Paired end shallow whole genome sequencing (sWGS) data of cell-free DNA from plasma and urine from RCC patients (DAIMOND cohort)

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ena-DATASET-MUG-23-01-2020-15:27:37:770-103 - samples

Project description:Paired end shallow whole genome sequencing (sWGS) data of cell-free DNA from plasma from self-reporting healthy individuals (MonRec Cohort)

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Transcriptome analysis of normal kidney, primary and metastasis ccRCC

Project description:Understanding gene expression changes during transformation from normal tissue to primary RCC and then to metastasis is important. Such analysis is pivotal for undertanding biology in renal cancer and also to unearth novel gene targets. Hence, we examined global transcriptome profile of normal renal tissue, primary RCC and metastasis RCC tumors, identifying important RCC biomarkers

2018-10-16 | GSE105261 | 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.

2024-09-02 | BIOMD0000000908 | BioModels

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