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，即如果不理解和具体地干扰支持癌细胞功能的网络，就不可能理解和合理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来合理和有效地发现癌细胞特异性药物靶点，基于我们的能力重构基因网络，通过整合基因实验与基因组，数学和计算/生物信息学工具。我们方法的新颖之处在于特别考虑和利用了这样一个概念，即癌细胞表型的许多特征仅作为多个合作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作用的分子机制，我们已经确定了对两种致癌突变的联合作用作出反应的基因，并在其表达上产生协同改变。重要的是，我们已经发现，这种“合作反应基因”（CRG）的调控对癌症表型至关重要，其频率之高令人惊讶（在所测试的24个基因中有14个），表明致癌突变通过下游基因网络的协同调控而合作。值得注意的是，相关基因可以作为介质控制多种不同的细胞过程，如细胞信号传导、存活、运动、侵袭性和自我更新，这表明协同致癌突变同时可以影响多种平行的癌细胞特性。值得注意的是，恶性细胞转化过程的复杂特征在小鼠和人类结肠细胞中是高度保守的。因此，通过一种研究策略，利用来自结肠隐窝的遗传可处理的小鼠恶性转化模型，结合人类结肠癌细胞的数据验证，分析这些特征变得可行。根据我们的观察，我们假设(1)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，通过合作致癌突变对CRG表达的调节不是独立的，而是基于强大的分层组织。因此，我们预测（II）对CRG网络架构的研究为识别癌细胞脆弱性提供了一条合理的途径，从而发现了一类新的药物靶点。我们还发现，组蛋白去乙酰化酶抑制剂（HDACi）的抗癌活性至少部分是通过逆转CRG表达模式介导的。因此，我们假设（III） CRG表达模式可以作为选择有效药物的指标，在癌症干预中具有潜在的用途。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点和药物。

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.