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PROVIDER: PRJNA296120 | ENA |
REPOSITORIES: ENA
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Project description:Single-cell transcriptome profiling for metastatic renal cell carcinoma patient-derived cells
| PRJNA296087 | ENA
Project description:Single-cell transcriptome profiling for metastatic renal cell carcinoma patient-derived cells [RNA-seq]
| PRJNA296119 | ENA
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:microRNA Profiling in Metastatic Renal Cell Carcinoma
| PRJNA172767 | ENA
Project description:Clear cell renal cell carcinoma (ccRCC) initiated from the renal epithelium is the most prevalent histological type of adult kidney cancers. Dissecting intratumoral heterogeneity (ITH) of ccRCC has leveraged to extend our knowledge on how primary tumors harboring driver mutations evolve and spread to other sites. The cellular fractions within and across the primary (pRCC) and metastatic RCC (mRCC) are heterogeneous in both their genetic and biological features determining the variability in clinical aggressiveness and sensitivity to the therapy. To achieve sustainable therapeutic benefit with targeted agents in mRCC, the effective target should focus on signaling pathways that are related to driver mutations occurred early in the clonal evolution of the disease and thus should be common to primary tumor and metastatic sites. Considering that extensive genetic heterogeneity may result in drug response variability among patients and treatment resistance, the tailored strategies for metastatic RCC is urgently needed. Here, we analyze single-cell RNA-seq (scRNA-seq) data from a matched primary RCC (pRCC) and lung metastasis (mRCC) to dissect ITH at the highest resolution to date with the objective of discovering the better therapeutic regimen. In order to identify successful clonal propagation from patient to PDX samples and understand pathogenesis from primary to metastatic RCC, we performed whole-exome sequencing (WES, n=4) and matched aCGH (n=4) on bulk tumor samples. And we utilized single-cell RNA sequencing (scRNA-seq) to model and dissect functional heterogeneity acroass primary and metastatic RCC tumors. We checked whether of capturing live one cell, not more cells, in microfluidics by fluorescent microscopic observation. To construct RNA sequencing libraries, we performed further quality controls including adequate quantities and qualities of amplified transcriptomes respectively from single cells. Tumor cells from the parental mRCC (n=34), PDX-mRCC (n=36) and PDX-pRCC (n=46) were finally analyzed in this study after filtering out poor quality cells.
2015-09-18 | E-GEOD-73119 | biostudies-arrayexpress
Project description:Clear cell renal cell carcinoma (ccRCC) initiated from the renal epithelium is the most prevalent histological type of adult kidney cancers. Dissecting intratumoral heterogeneity (ITH) of ccRCC has leveraged to extend our knowledge on how primary tumors harboring driver mutations evolve and spread to other sites. The cellular fractions within and across the primary (pRCC) and metastatic RCC (mRCC) are heterogeneous in both their genetic and biological features determining the variability in clinical aggressiveness and sensitivity to the therapy. To achieve sustainable therapeutic benefit with targeted agents in mRCC, the effective target should focus on signaling pathways that are related to driver mutations occurred early in the clonal evolution of the disease and thus should be common to primary tumor and metastatic sites. Considering that extensive genetic heterogeneity may result in drug response variability among patients and treatment resistance, the tailored strategies for metastatic RCC is urgently needed. Here, we analyze single-cell RNA-seq (scRNA-seq) data from a matched primary RCC (pRCC) and lung metastasis (mRCC) to dissect ITH at the highest resolution to date with the objective of discovering the better therapeutic regimen. In order to identify successful clonal propagation from patient to PDX samples and understand pathogenesis from primary to metastatic RCC, we performed whole-exome sequencing (WES, n=4) and matched aCGH (n=4) on bulk tumor samples. And we utilized single-cell RNA sequencing (scRNA-seq) to model and dissect functional heterogeneity acroass primary and metastatic RCC tumors. We checked whether of capturing live one cell, not more cells, in microfluidics by fluorescent microscopic observation. To construct RNA sequencing libraries, we performed further quality controls including adequate quantities and qualities of amplified transcriptomes respectively from single cells. Tumor cells from the parental mRCC (n=34), PDX-mRCC (n=36) and PDX-pRCC (n=46) were finally analyzed in this study after filtering out poor quality cells.
2015-09-18 | E-GEOD-73121 | biostudies-arrayexpress
Project description:Profiling tumors at single-cell resolution provides an opportunity to understand complexities underpinning lymph-node metastases in head and neck squamous-cell carcinoma. Single-cell RNAseq (scRNAseq) analysis of cancer-cell trajectories identifies a sub-population of pre-metastatic cells, driven by actionable pathways including AXL and AURK. Blocking these two proteins blunts tumor invasion in patient-derived cultures. This study demonstrates the importance of tumor heterogeneity analyses in identifying key vulnerabilities during early metastasis.
2023-02-17 | GSE225331 | GEO
Project description:Transcriptome profiling of de novo-derived ccRCC cell cultures and their matching parental tumours. VHL-mutant and VHL wild-type cultures were established by isolating CA9+ and CA9- cells from tumor samples using FACS. RNASeq expression profiling of 18 renal cell carcinoma samples, including 6 patient tumours, 6 VHL mutant and 6 VHL WT derivative cell cultures
2016-06-01 | E-GEOD-74958 | biostudies-arrayexpress
Project description:arrayCGH profiling of 10 cell lines derived from clear cell Renal Cell Carcinoma The goal of this study was to make a detailed genomic profile of these cell lines, including aCGH, miRNA expression, and mRNA expression profiles. The miRNA and mRNA expressiion profiles are submitted to GEO separately. One paper is in preparation to link the miRNA expression data with the arayCGH data. For mRNA expression data see GEO Series accession number GSE20491.
2012-11-01 | GSE34979 | GEO
Project description:Large numbers of cells are generally required for quantitative global proteome profiling due to the significant surface adsorption losses associated with sample processing. Such bulk measurement obscures important cell-to-cell variability (cell heterogeneity) and makes proteomic profiling impossible for rare cell populations, such as circulating tumor cells (CTCs) and early metastatic cells. Herein we report a facile mass spectrometry (MS)-based single-cell proteomics method that capitalizes on a MS-compatible nonionic surfactant, n-Dodecyl-β-D-maltoside, for greatly reducing the surface adsorption losses by ~20-fold for effective single-tube processing of single cells, thus significantly improving detection sensitivity for single-cell proteomic analysis. With standard MS platforms, the method allows for the first time precise, label-free, reliable quantification of hundreds of proteins from single human cells in a simple, convenient manner. When applied to a patient CTC-derived xenograft (PCDX) model, the method can reveal distinct protein signatures between primary tumor cells and early metastases to the lungs at the single-cell resolution. The approach paves the way for routine, precise quantitative single-cell proteomic analysis.
2021-06-07 | PXD017075 | JPOST Repository