Gene Networks Essential to Colon Cancer Phenotype

描述（由申请人提供）：现在有一个广泛的共识，即如果不理解和特异性干扰支持癌细胞功能的网络，就不可能理解和合理干预癌症的发展和进展。在这里，我们制定了一种新的方法，合理和有效地发现癌细胞特异性药物靶点的基础上，我们的能力，通过整合基因实验与基因组，数学和计算/生物信息学工具，重建基因网络。我们方法的新奇在于特别考虑和利用了这样的概念，即癌细胞表型的许多特征仅作为多个协同致癌突变之间相互作用的结果而出现。通过分析致癌基因协同性的分子机制，我们已经确定了基因，两个致癌突变的协同改变，在他们的表达的联合作用。重要的是，我们已经发现，这种“合作反应基因”（CRG）的调节是关键的癌症表型在令人惊讶的高频率（14个测试的24个基因），表明致癌突变合作，通过协同调节下游基因网络。值得注意的是，所涉及的基因可以作为控制多种和不同细胞过程的介质，例如细胞信号传导、存活、运动性、侵袭性和自我更新，这表明协同致癌突变可以同时影响多种平行的癌细胞性状。值得注意的是，恶性细胞转化过程的复杂特征在鼠和人结肠细胞中是高度保守的。这些功能的分析，因此成为可行的，通过研究策略，利用遗传上易于处理的小鼠模型的恶性转化，来自结肠隐窝结合在人类结肠癌细胞中的数据验证。基于我们的观察，我们因此假设（I）CRG是一类对协同致癌突变下游的恶性细胞转化至关重要的基因。此外，我们有初步的证据表明，CRG表达的调控合作致癌突变不是独立的，而是基于强大的层次组织。因此，我们预测，（II）CRG网络结构的研究提供了一个合理的路径，以识别癌细胞的脆弱性，从而一类新的药物靶点。我们还发现组蛋白脱乙酰酶抑制剂（HDACi）的抗癌活性至少部分地通过CRG表达模式的逆转介导。因此，我们假设（III）CRG表达模式可以作为选择具有潜在癌症干预用途的有效药物的指标。我们测试这些假设的方法将直接导致通过合理的手段识别和验证真正的癌细胞特异性药物靶标和药物。 公共卫生相关性：癌症分子分析的主要动机之一是需要开发合理的方法来识别有效的癌症治疗方法。然而，靶点识别是众所周知的困难和不可预测的，至少部分是因为细胞调节本身是复杂的。在这里，我们制定了一种新的方法，合理和有效地发现癌细胞特异性药物靶点下游的致癌突变的基础上，我们的能力，重建基因网络，通过整合基因组，数学和计算/生物信息学工具的遗传实验。

DESCRIPTION (provided by applicant): 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. PUBLIC HEALTH RELEVANCE: Narrative 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.