Project description:Recent advancement in cancer research has shown that tumours are highly heterogeneous and multiple phenotypically different cell populations are found in single nodule. Cancer development and tumour growth is driven by specific type of cells - cancer stem cells or tumour initiating cells (CSCs/TICs), which are to be responsible for tumour growth, metastatic spread and drug resistance. This research was designed to verify the presence of tumour initiating cells in renal cancer cell lines. Subsequently, we aimed to characterize phenotype and cell biology of CD105+ cells, defined previously as renal cell carcinoma tumour initiating cells. Main goal of the project was to describe gene expression profile of tumour initiating cells originating from primary tumour and of metastatic origin. Metastatic RCC cell lines (ACHN and Caki-1) demonstrated higher colony forming ability comparing to primary RCC cell lines. We investigated presence of CD105+ cells in RCC cell lines. Furthermore, metastatic RCC cell lines have higher CD105+ cell population and higher expression for stemness genes (Oct-4 and Nanog). CD105+ cells adopt 3D grapes like structure under handing drop condition. Sorted CD105+ cells were found positive for human MSC makers such as CD90, CD73, CD44, CD146 and alkaline phosphatase activity. In addition, CD105+ cells were unable to expressed maker for CD24, CD34, CD11b, CD19, CD45 and HLA-DR. 1411 genes were commonly differentially expressed in CD105+ cells (both from primary (Caki-2) and metastatic RCC (ACHN)) cells in comparison to healthy kidney epithelial cell line (ASE-5063). TGF-β, Wnt/β-catenine, Epithelial-Mesenchymal Transition (EMT), Rap1 signaling, PI3K-Akt signaling and Hippo signaling pathway were altered in CD105+ cells after IPA and KEGG pathway analysis. TGFB1, ERBB2, and TNF are most significant transcriptional regulators activated in these cells. Altogether, our results indicate that RCC-CD105+ cells may represents a novel target for CSCs/TICs phenotype and their gene expression profile could be used as initial data for new functional studies and drug design.

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.

Project description:Purpose: To identify tissue microRNAs predictive of sunitinib activity in patients with metastatic renal-cell carcinoma (MRCC) and to validate them in a cellular model. Selected microRNAs were studied in serum from MRCC patients and healthy individuals. Methods: We screened 673 microRNAs using TaqMan Low-density Arrays (TLDAs) in tumors from MRCC patients with extreme phenotypes of marked efficacy and resistance to sunitinib, selected from an identification cohort (n=41). Differentially expressed microRNAs were selected using bioinformatics-based target prediction analysis and quantified by qRT-PCR in tumors from patients presenting similar phenoytpes selected from an independent cohort (n=117). Results were validated in a cellular model of sunitinib resistance and studied in serum from healthy individuals and MRCC patients. Results: TLDAs identified 64 microRNAs differentially expressed in the identification cohort. Seven candidates were quantified by qRT-PCR in the independent series. MiR-942 was the most accurate predictor of sunitinib efficacy (p=0.0074). High expression of miR-942, miR-133a, miR484, and miR-628-5p was significantly associated with decreased time-to-progression and overall survival. These microRNAs were overexpressed in the sunitinib resistant cell line Caki-2 in comparison with the sensitive parental cell line. Serum levels of miR-942, miR-133a, miR-484, miR-146a-5p, miR-374a and miR-486-5p were significantly reduced in MRCC patients compared to healthy controls. Conclusions: Our strategy identified differentially expressed microRNAs in MRCC patients presenting marked sensitivity and resistance to sunitinib. Mir-942 was the best predictor of efficacy. Results were confirmed in a cellular model of sunitinib resistance. We also identified exosome derived serum microRNAs differentially expressed in MRCC patients and healthy individuals. Taqman Low Density Array for 6 FFPE tissues obtained from extreme phenotype MRCC patients, (n=3 marked resistance to sunitinib treatment patients and n=3 marked sensitivity to sunitinib treatment patients), was performanced to screen 667 microRNAs.

Dataset Information

Gene set enrichment analysis and ingenuity pathway analysis of metastatic clear cell renal cell carcinoma cell line

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