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A renal cell carcinoma tumorgraft platform to advance precision medicine

ABSTRACT: Renal cell carcinoma (RCC) encompasses a heterogenous group of diseases, creating challenges for drug development. Preclinical models which are both translatable and representative of disease heterogeneity are needed. Patient-derived xenograft (PDX) or tumorgraft (TG) models recapitulate the biology and treatment responsiveness of RCC. Through the orthotopic implantation into NOD/SCID mice of tumor samples from >900 ethnically-diverse patients at UT Southwestern Medical Center and the affiliated county hospital, we have generated a resource comprising 172 independently-derived stably-engrafted TG lines from 148 patients. The lines have been characterized histologically and genomically (whole exome sequencing [n = 97] and RNA sequencing [n =102]). They represent a variety of histological and on cogenotypes including representative TCGA clades further validated through orthogonal metabolomic analyses in a subset. We illustrate how this platform has enabled a deeper understanding of the biology and molecular genetics of RCC, the development of both tissue- and imaging-based biomarkers, as well as advances in drug development. This resource expands the diversity of models available to the scientific community to advance precision diagnosis and therapy in RCC.

PROVIDER: EGAS00001005516 | EGA |

REPOSITORIES: EGA

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Project description:Renal cell carcinoma (RCC) is one of the ten most common malignancies. Even though recent paradigm changes led to inclusion of immune checkpoint inhibitor combination therapy in treatment of naive metastatic RCC, insight in the biology and management of RCC would still favour the use of a combination of kinase inhibitors to target multiple pathways, presenting the possibility of enhanced efficacy and reduced development of resistance. We have used the streamlined-feedback system control (s-FSC) technique, a phenotypic approach, to identify optimized drug combinations (ODC) in 5 different human RCC lines. This approach requires no a priori mechanistic information on drug mechanism of action, and uses statistical modelling circumventing the need to experimentally screen large numbers of combinations. Different ODC with up to 90% effectivity were obtained for the 5 cell lines, importantly, by combining drug at doses that individually only inhibit 5-25% cell viability. Cell viability was reduced and apoptosis induced, accompanied by a general inhibition of downstream survival and growth promoting effector kinase RPS6. Global phosphoproteomics analysis of the cell lines revealed that in all cases where the ODC showed >50% efficacy, the most active kinases were targets of the selected drugs in the ODC. Interestingly, we observed considerable overlap in the top 20 most active kinases in all 5 cell lines, suggesting a universal ODC might be effective in all cells. Indeed, a combination of erlotinib and dasatinib, targeting EGFR and EPHA2/SRC signaling respectively, combined with AZD4547 or axitinib (targeting VEGFR and FGFR signaling), displayed excellent activity. The effect of the 786-O cell-line specific ODC was analysed in more detail using phosphoproteomics coupled to kinase activity analysis using the INKA pipeline.approaches. As expected, the activity of the main targets of erlotinib and dasatinib was reduced, but the activity of several untargeted kinases, including AXL, PTK2 and MET, remained high or increased, having potential implications for the development of resistance. Addition of crizotinib, targeting MET and PTK2, to the 3-drug ODC but not exchange with axitinib, further enhanced drug combination efficacy. In conclusion, we show that the phenotypic s-FSC method is highly effective in selecting optimized combinations of drugs, and that phosphoproteomics analysis can help further refine the selection of drugs included in the screen or final drug mixture.

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.

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A renal cell carcinoma tumorgraft platform to advance precision medicine

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