Clinical Implication of Concurrent Amplification of MET and FGFR2 in Metastatic Gastric Cancer
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PROVIDER: PRJEB69296 | ENA |
REPOSITORIES: ENA
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Project description:We found that RANKL, expressed by cancer cells or derived from exogenous sources, consistently induced human prostate, breast, kidney, lung and liver cancer cells to colonize or metastasize to bone in an animal model of cancer bone metastasis. RANK-mediated signaling established a premetastatic niche through a forward feedback loop by inducing RANKL and c-Met expression and downstream signaling via upregulation of master regulator transcription factors regulating EMT (Twist1, Slug, Zeb1, Zeb2), stem cells (Sox2, Myc, Oct3/4 and Nanog), neuroendocrine cells (Sox 9, HIF-1α and FoxA2) and osteomimicry (c-Myc/Max, Sox2, Sox9, HIF1α and Runx2). Abrogating RANK or its downstream signaling network, c-Myc/Max or c-Met, abolished PCa skeletal metastasis in mice. We observed that a small number of RANKL-expressing PCa cells can initiate bone and soft tissue metastases by recruiting non-tumorigenic or bystander PCa or host cells from the circulation or at metastatic sites to co-colonize bone. The recruited bystander PCa cells assume the phenotypes of RANKL-expressing PCa cells by expressing increased c-Met, phosphorylated c-Met and RANKL. RANKL expression at a single cell level in primary PCa tissues predicted disease-specific survival, reflecting the significant role of RANKL-RANK signaling in the development of lethal bone metastasis. Global gene expression analysis perturbed by RANKL in LNRANKL compared to LNNeo cells.
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Project description:MET amplification has been clinically credentialed as a therapeutic target in gastric cancer, but the molecular mechanisms underlying sensitivity and resistance to MET inhibitors are still not well understood. Using whole-genome mRNA expression profiling, we identified autophagy as a top molecular pathway that was activated by the MET inhibitor crizotinib in drug-sensitive human gastric cancer cells, and functional studies confirmed that crizotinib increased autophagy levels in the drug-sensitive cells in a concentration-dependent manner. We then used chemical and molecular approaches to inhibit autophagy in order to define its role in cell death. The clinically available inhibitor of autophagy, chloroquine, or RNAi-mediated knockdown of two obligate components of the autophagy pathway (ATG5 and ATG7) blocked cell death induced by crizotinib or RNAi-mediated knockdown of MET, and mechanistic studies localized the effects of autophagy to cytochrome c release from the mitochondria. Overall; the data reveal a novel relationship between autophagy and apoptosis in gastric cancer cells exposed to MET inhibitors. The observations suggest that autophagy inhibitors should not be used to enhance the effects of MET inhibitors in gastric cancer patients.
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Project description:We used microarrays to probe the global programme of gene expression under treatment with c-Met inhibitor and identified distinct classes of up- and down-regulated genes during this process
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Project description:Genome-wide association studies for breast cancer have identified over 80 different risk regions in the genome, with the FGFR2 locus consistently identified as the most strongly associated locus. However, we know little about the mechanisms by which the FGFR2 locus mediates risk or the pathways in which multiple risk loci may combine to cause disease. Here we use a systems biology approach to elucidate the regulatory networks operating in breast cancer and examine the role of FGFR2 in mediating risk. Using model systems we identify FGFR2-regulated genes and, combining variant set enrichment and eQTL analysis, show that these are preferentially linked to breast cancer risk loci. Our results support the concept that cancer-risk associated genes cluster in pathways The data consists of 71 microarray samples from MCF-7 cells treated under different conditions, at 3 time points (0, 6 and 24 h) in order to perturb FGFR2 signalling using the iF2 construct system. The data have been pre-processed in R using the beadarray package, and are presented in the form of log2 expression values. The experiment was carried out on 6 Humanv4 BeadChips using 12 samples per BeadChip. The original arrays contain 48324 features, with a mean of 22 beads per feature (Standard Deviation of 5)
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