There is now broad consensus that it will be impossible to understand and rationally intervene with the development and progression of cancer without understanding and specifically perturbing the networks that support the functioning of cancer cells. Here we formulate a novel approach for rational and efficient discovery of cancer cell-specific drug targets based on our capability to reconstruct gene networks through the integration of genetic experimentation with genomic, mathematical and computational/bioinformatics tools. The novelty of our approach lies in specifically considering and exploiting the notion that many features of the cancer cell phenotype only emerge as a result of the interplay between multiple co- operating oncogenic mutations. Through analysis of the molecular mechanisms underlying oncogene cooperativity we have identified genes that respond to the combined effect of two oncogenic mutations with synergistic alterations in their expression. Importantly, we have discovered that the regulation of such `cooperation response genes'(CRG) is critical for the cancer phenotype at surprisingly high frequency (14 out of 24 genes tested), indicating that oncogenic mutations cooperate through synergistic regulation of downstream gene networks. Remarkably, the genes involved can act as mediators in the control of multiple and diverse cellular processes, such as cell signaling, survival, motility, invasiveness and self-renewal indicating that cooperating oncogenic mutations simultaneously can affect multiple parallel cancer cell traits. Notably, the complex features underlying the malignant cell transformation process are strongly conserved in murine and human colon cells. Analysis of these features thus becomes feasible through a research strategy utilizing both a genetically tractable murine model of malignant transformation derived from colonic crypts combined with data validation in human colon cancer cells. Based on our observations we thus hypothesize (I) that CRGs are a class of genes essential for malignant cell transformation downstream of cooperating oncogenic mutations. In addition, we have preliminary evidence to show that regulation of CRG expression by cooperating oncogenic mutations is not independent but rather underlies strong hierarchical organization. We thus predict that (II) investigation of CRG network architecture provides a rational path to identification of cancer cell vulnerabilities and thus a novel class of drug targets. We also have discovered that the anti-cancer activity of histone deacetylase inhibitors (HDACi) is at least in part mediated through reversion of CRG expression patterns. We thus hypothesize (III) that CRG expression patterns can serve as indicators for selection of efficacious drugs with potential use in cancer intervention. Our approaches to test these hypotheses will lead directly to identification and validation of bona fide cancer cell-specific drug targets and drugs by rational means.
One of the main motives for the molecular analysis of cancer is the need to develop rational approaches to the identification of effective cancer treatments. Target identification, however, is notoriously difficult and unpredictable at least in part, because cell regulation is inherently complex. Here we formulate a novel approach for rational and efficient discovery of cancer cell-specific drug targets downstream of oncogenic mutations based on our capability to reconstruct gene networks through the integration of genetic experimentation with genomic, mathematical and computational/bioinformatics tools.
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