Project description:The mouse renal cancer cell line RENCA was serially passaged in vivo using multiple implantation strategies (called Kidney, Tail and Lung) designed to replicate different aspects of primary tumour growth and metastasis. We have acquired methylomic data.
Project description:Papillary renal cell carcinoma (pRCC) is the second most frequent renal cell carcinomas (RCC) after clear cell RCC. In contrast to clear cell RCC, there is no consensual protocol using targeted therapy for metastatic pRCC. Moreover, diagnosis of some pRCC, especially pRCC of type 2 (pRCC2) may be challenging. Our aim was to identify molecular biomarkers that could be helpful for the diagnosis and treatment of pRCC.
Project description:Papillary renal cell carcinoma (pRCC) is the second most frequent renal cell carcinomas (RCC) after clear cell RCC. In contrast to clear cell RCC, there is no consensual protocol using targeted therapy for metastatic pRCC. Moreover, diagnosis of some pRCC, especially pRCC of type 2 (pRCC2) may be challenging. Our aim was to identify molecular biomarkers that could be helpful for the diagnosis and treatment of pRCC.
Project description:Papillary renal cell carcinoma (pRCC) is the second most frequent renal cell carcinomas (RCC) after clear cell RCC. In contrast to clear cell RCC, there is no consensual protocol using targeted therapy for metastatic pRCC. Moreover, diagnosis of some pRCC, especially pRCC of type 2 (pRCC2) may be challenging. Our aim was to identify molecular biomarkers that could be helpful for the diagnosis and treatment of pRCC.
Project description:Renal Cell Carcinoma (RCC) associated with Xp11.2 translocation (TFE3-RCC) has been recently defined as a distinct subset of RCC. The Xp11 translocations involve the TFE3 transcription factor and produce chimeric TFE3 proteins retaining the basic helix-loop-helix leucine zipper structure for dimerization. To facilitate the development of molecular-based diagnostic tools and targeted therapies for TFE3-RCC, we generated a translocation RCC mouse model and performed DNA microarray analysis.
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.
Project description:Papillary renal cell carcinoma (pRCC) is the second most frequent renal cell carcinomas (RCC) after clear cell RCC. In contrast to clear cell RCC, there is no consensual protocol using targeted therapy for metastatic pRCC. Moreover, diagnosis of some pRCC, especially pRCC of type 2 (pRCC2) may be challenging. Our aim was to identify molecular biomarkers that could be helpful for the diagnosis and treatment of pRCC. 9 samples was analysed by arrayCGH method using Agilen recommendations. Sample was labeled in Cy5 and co-hybridized with control DNA cy3 labeled.
Project description:Eleven TC-RCC cases were selected on the basis of the criteria defined by the 2012 Vancouver Classification of Renal Neoplasia and WHO 2016 classification criteria, and re-reviewed by a panel of expert renal pathologists. Two of these cases, TC-RCC#9 and #10, had a mixed tubulocystic and papillary histology and were macro-dissected into separate PRCC and TC-RCC components. The molecular basis of TC-RCC, and miRNA expression profile, is unknown. We therefore used Affymetrix miRNA v.2. arrays to elucidate the expression of these samples
Project description:Primary tumors of Renal Cell Carcinoma (RCC) were implanted orthotopically into mice and their gene expression profiling was compared to their corresponding tumors using Affymetrix Human Genome U133 Plus 2.0 arrays.
Project description:Primary tumors of Renal Cell Carcinoma (RCC) were implanted orthotopically into mice and their DNA copy number abnormalities were compared to their corresponding tumors using Affymetrix SNP arrays 6.0