Towards rational design of combination therapeutic targets

项目摘要 考虑到致癌途径和疾病之间的串扰的普遍性， 随着异质性的增加，越来越明显的是， 实现长期治愈和最大限度地减少耐药突变的发展所需的 和逃生通道大多数现有的联合疗法是在 一种特别的方式，即一次一个代理人，没有系统地考虑 基因靶点之间潜在的复杂相互作用， 组学数据。此外，现有的组合疗法基于以下靶点： 现有的药物，这只代表了人类蛋白质组的一小部分。为此， 我们假设，系统识别协同作用的关键调节器代表了一种 有前途的方法提名的联合治疗的目标。为了实现这一目标， 我们将向前设计一个平台，用于识别协同监管节点， 癌症基因调控网络作为联合治疗的靶点。我们将产生 疾病特异性多组学数据，以构建一个整合的基因调控网络， 这是了解癌细胞中失调基因网络和 开发有效和持久的治疗方法。我们将把我们的研究重点放在费城， 急性淋巴细胞白血病作为一个原理证明。我们的团队提出了一部小说 解决这个问题的方法，利用调查人员的独特优势， 系统生物学、基因组学、蛋白质组学和转化研究，以及大型 我们的研究机构提供了一组患者样本。如果成功，建议 这个框架将是理解遗传学的一个巨大进步和范式转变 致癌途径之间的相互作用，以进行最终的治疗干预。

Project Summary Given the prevalence of crosstalk among oncogenic pathways and disease heterogeneity, it has become increasingly apparent that combination therapies are required to achieve long-term cure and to minimize development of resistance mutations and escape pathways. The majority of existing combination therapies are developed in an ad hoc fashion, namely one agent at a time, without systematic consideration of potential complex interactions among the gene targets by leveraging disease-specific omics data. Moreover, the existing combination therapies are based on targets of existing drugs, which only represent a small portion of the human proteome. To this end, we hypothesize that systematic identification of synergistic key regulators represents a promising approach for nominating targets of combination therapy. Towards this goal, we will forward engineer a platform for identifying synergistic regulatory nodes in a cancer gene regulatory network as the targets for combination therapy. We will generate disease-specific multi-omics data to construct an integrative gene regulatory network, a pre-requisite for understanding the deregulated gene network in the cancer cells and for developing effective and lasting therapy. We will focus our study on Philadelphia-like acute lymphoblastic leukemia as a proof-of-principle. Our team proposes a novel approach to this problem by leveraging the unique strengths of the investigators in systems biology, genomics, proteomics, and translational research, as well as the large cohort of patient samples available at our institutions. If successful, the proposed framework would be a tremendous advance and paradigm shift to understand genetic interactions among oncogenic pathways for eventual therapeutic intervention.