Project description:Renal cell carcinomas with unclassified histology (uRCC) constitute a significant portion of aggressive non-clear cell RCC (nccRCC) that have no standard therapy. The oncogenic drivers in these tumors are unknown. We performed a molecular analysis of 62 high-grade primary uRCC, incorporating targeted cancer gene sequencing, RNA sequencing, Single Nucleotide Polymorphism array, fluorescence in-situ hybridization, immunohistochemistry, and cell-based assays. We identified recurrent somatic mutations in 29 genes, including NF2 (18%), SETD2 (18%), BAP1 (13%), KMT2C (10%), and MTOR (8%). Integrated analysis revealed distinct molecular subsets, including a subset of 26% uRCC characterized by NF2-loss, dysregulated Hippo-YAP pathway and worse survival.
Project description:Renal cell carcinomas with unclassified histology (uRCC) constitute a significant portion of aggressive non-clear cell RCC (nccRCC) that have no standard therapy. The oncogenic drivers in these tumors are unknown. We performed a molecular analysis of 62 high-grade primary uRCC, incorporating targeted cancer gene sequencing, RNA sequencing, Single Nucleotide Polymorphism array, fluorescence in-situ hybridization, immunohistochemistry, and cell-based assays. We identified recurrent somatic mutations in 29 genes, including NF2 (18%), SETD2 (18%), BAP1 (13%), KMT2C (10%), and MTOR (8%). Integrated analysis revealed distinct molecular subsets, including a subset of 26% uRCC characterized by NF2-loss, dysregulated Hippo-YAP pathway and worse survival.
Project description:In order to clarify the molecular mechanism involved in renal carcinogenesis, and identify molecular targets for diagnosis and treatment, we analyzed genome-wide gene expression profiles of 15 surgical specimens of clear cell renal cell carcinoma (RCC), compared to normal renal cortex, using a combination of laser microbeam microdissection (LMM) with a cDNA microarray representing 27,648 genes.
Project description:In order to clarify the molecular mechanism involved in renal carcinogenesis, and identify molecular targets for diagnosis and treatment, we analyzed genome-wide gene expression profiles of 15 surgical specimens of clear cell renal cell carcinoma (RCC), compared to normal renal cortex, using a combination of laser microbeam microdissection (LMM) with a cDNA microarray representing 27,648 genes. Tissue samples of surgically-resected clear cell renal cell carcinoma (ccRCC) and their corresponding clinical information were obtained from patients with written informed consent. The total of 15 cancer patients (6 women and 9 men; median age, 66; range, 36-75 years) that had been confirmed histologically as ccRCC were selected for this study. Two to three pieces of cancer tissue had been taken from each patient at the time of radical nephrectomy. Normal tissue had been obtained from the distant region from cancer area in the resected kidney tissue. These samples were immediately embedded in TissueTek OCT compound (Sakura, Tokyo, Japan), frozen, and stored at -80°C. The frozen tissues were sliced into 8-μm sections using a cryostat (Sakura) and then stained with H&E for histological examination. We used LMM technology to collect pure populations of ccRCC cells as well as non-cancerous renal cortex. A mixture of normal renal cortex cells in kidney tissues from 11 patients was prepared as a universal control. Experiments were performed using 6 sets of slides (slide set 1-6 corresponding to ID_REF 1-27648).
Project description:The proteome of clinical tissue samples diagnosed with clear cell renal cell carcinoma (ccRCC) and papillary renal cell carcinoma (pRCC) were evaluated analyzed along with the dataset identifier PXD022018 to establish a potential discriminative biomarker panel of proteins for these tumors subtypes.
Project description:Both cigarette smoking and obesity have been implicated in increased risk of clear cell renal cell carcinoma (ccRCC); however, there are limited data regarding the molecular mechanisms that underlie these associations. We used a multi-stage design to identify and validate specific molecular targets that are associated with smoking or obesity-related ccRCC.