Towards rational design of combination therapeutic targets

项目摘要 鉴于致癌途径和疾病之间的串扰普遍存在 异质性，越来越明显的是，联合疗法是 需要实现长期治愈并将耐药性突变的发展降至最低 和逃生通道。现有的大多数联合疗法都是在 一种特殊方式，即一次只有一个代理，而不需要系统地考虑 通过利用特定于疾病的基因靶点之间潜在的复杂相互作用 组学数据。此外，现有的联合疗法是基于 现有的药物，只代表了人类蛋白质组的一小部分。为此， 我们假设，系统地识别协同关键调节器代表着一种 有希望的方法来提名联合治疗的靶点。为了实现这一目标， 我们将向前工程一个平台，用于识别 肿瘤基因调控网络作为联合治疗的靶点。我们将生成 疾病特异性多组学数据构建整合的基因调控网络 了解癌细胞中去调控的基因网络和 开发有效和持久的治疗方法。我们将把研究重点放在费城 急性淋巴细胞性白血病作为一项原则证明。我们的团队提出了一部小说 通过利用调查人员的独特优势解决这个问题 系统生物学、基因组学、蛋白质组学和翻译研究，以及大型 我们机构提供的患者样本队列。如果成功，建议的 框架将是理解遗传学的巨大进步和范式转变 最终治疗干预的致癌途径之间的相互作用。

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.