Gene Networks Essential to Colon Cancer Phenotype

描述(申请人提供)：现在有一个广泛的共识，即如果不了解并特别干扰支持癌细胞功能的网络，就不可能理解和理性地干预癌症的发展和进展。在这里，我们制定了一种新的方法，基于我们通过整合基因组、数学和计算/生物信息学工具重建基因网络的能力，合理而有效地发现癌细胞特异性药物靶点。我们方法的新颖性在于具体考虑和利用了这样一个概念，即癌细胞表型的许多特征只是由于多个合作的癌基因突变之间的相互作用而出现的。通过分析癌基因协同作用的分子机制，我们已经确定了对两个癌基因突变及其表达协同变化的联合作用做出反应的基因。重要的是，我们已经发现，这种“合作反应基因”(CRG)的调节对于癌症表型至关重要，频率高得惊人(24个基因中有14个)，这表明致癌突变是通过下游基因网络的协同调节来协同作用的。值得注意的是，所涉及的基因可以作为中介来控制多种不同的细胞过程，如细胞信号、存活、运动、侵袭和自我更新，这表明同时协同致癌基因突变可以影响多个平行的癌细胞特征。值得注意的是，恶性细胞转化过程中的复杂特征在小鼠和人类结肠细胞中高度保守。因此，通过一种研究策略来分析这些特征是可行的，该研究策略既利用了源于结肠隐窝的遗传易处理的小鼠恶性转化模型，又结合了对人类结肠癌细胞的数据验证。基于我们的观察，我们因此假设(I)CRGs是协同致癌突变下游恶性细胞转化所必需的一类基因。此外，我们有初步证据表明，协同致癌基因突变对CRG表达的调节不是独立的，而是强大的等级组织的基础。因此，我们预测(Ii)CRG网络结构的研究为识别癌细胞的脆弱性提供了一条合理的途径，从而提供了一类新的药物靶点。我们还发现，组蛋白去乙酰酶抑制剂(HDACi)的抗癌活性至少部分是通过逆转CRG的表达模式来实现的。因此，我们假设(Iii)CRG的表达模式可以作为选择有潜力用于癌症干预的有效药物的指标。我们检验这些假说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点和药物。 公共卫生相关性：叙述癌症分子分析的主要动机之一是需要开发合理的方法来确定有效的癌症治疗方法。然而，至少在一定程度上，识别目标是出了名的困难和不可预测，因为细胞调节本身就很复杂。在这里，我们制定了一种新的方法，用于合理和有效地发现癌基因突变下游的癌细胞特异性药物靶点，该方法基于我们通过将遗传实验与基因组、数学和计算/生物信息学工具相结合来重建基因网络的能力。

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.