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The Identification and Functional Characterization of Colorectal Cancer Genes

Thomas Ried

1 Collaborator(s)

Funding source

National Cancer Institute (NIH)
In order to characterize patterns of global transcriptional deregulation in primary colon carcinomas, we performed gene expression profiling of some 300 rectal and colon carcinomas, and, as expected, identified comprehensive lists of deregulated genes. Genes that are upregulated are potentially required for the viability of colorectal cancer cells, and can therefore be considered oncogenes. Our systematic comparison of colon and rectal carcinomas also revealed a significant overlap of genomic imbalances and transcriptional deregulation, including activation of the Wnt/beta-catenin signaling cascade, suggesting similar pathogenic pathways. The functional validation of novel targets was performed in cell lines established from colorectal carcinomas that recapitulate the genomic and gene expression changes that we have previously observed in primary colorectal cancers (CRC). We used a combined functional genomics and systems biology approach to identify such anti-CRC targets. Genes that are highly overexpressed in tumor cells can be required for tumor cell survival, and have the potential to be selective therapeutic targets. In an attempt to identify such targets, we assesd the consequences of RNAi-mediated silencing of overexpressed genes that were selected from 300 gene expression profiles from colorectal cancers (CRC) and matched normal mucosa. In order to identify credible models for in-depth functional analysis, we first confirmed the overexpression of these genes in 25 different CRC cell lines. We then identified five candidate genes based on how profoundly they reduced the viability of CRC cell lines when silenced with either siRNAs or shRNAs; the genes are HMGA1, TACSTD2, RRM2, RPS2, and NOL5A. These genes were further studied by systematic analysis of comprehensive gene expression profiles generated following siRNA-mediated silencing. Exploration of these RNAi-specific gene expression signatures allowed the identification of the functional space in which the five genes operate, and showed enrichment for cancer specific signaling pathways, some known to be involved in CRC. By comparing the expression of the RNAi signature genes with their respective expression levels in an independent set of primary rectal carcinomas we could recapitulate these defined RNAi signatures, therefore establishing the biological relevance of our observations. Our systematic and unbiased approach to map the functional space of CRC gene was accomplished by gene expression profiling after RNAi of genes of interest. This revealed specifically disturbed signaling pathways and correlated with the inverse deregulation of these modules when compared to primary cancer samples, in which these genes were upregulated. We were then interested in understanding the dynamics of these processes, i.e., the connectivity of deregulated gene expression as a function of perturbing a specific target, and the timeline in which these changes occur for the genes RRM2, PLK1, CASP8AP and POMP. In collaboration with Dr. Natasha Caplen, we performed these experiments as biological replicates and could show that the experimental conditions were extremely robust. We now propose injecting a systems biology approach into the analysis to answer the following questions: (i) can we identify patterns of interconnectivity among the experiments? (ii) to which extent are the gene expression signatures specific to the perturbation of candidate genes, what are the parallels? (iii) both PLK1 and RRM2 are prominent targets for cancer therapy. Can we identify consistent downstream targets that would open avenues for alternative targeting of these genes? (iv) all four candidates, when silenced, significantly reduced the viability of CRC cell lines. Can we identify non-redundant pathways that might rupture not only one, but both Achilles' heels of the tumor cell? In order to conduct a thorough, systematic analysis of these data, we engaged in collaborations with the group of Dr. Indika Rajapakse (University of Michigan), who will use his expertise in understanding the controllability of perturbed gene expression states. We are convinced that we have generated an extremely valuable dataset, whose in depth analysis will provide critical information on both the dynamics of RNAi experiments and on signaling pathways in cancer. Moreover, these data will be very useful to study structure/function relationships between transcriptional activity and nuclear organization.

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