DR. EPS: Drug Repurposing for Extended Patient Survival

项目摘要 人类癌症是一种复杂的疾病，存在巨大的异质性，导致生存率差异很大 成果。因此，对于癌症预后的可操作的和稳健的预测模型是非常需要的， 个性化治疗和疾病管理。然而，严重缺乏 治疗上可行的计算方法，其明确地将患者生存差异建模为 目的，同时整合多维高通量基因组学数据。为了解决这个问题，我们提出了一个 一种新的癌症药物再利用的可操作框架，称为DR. EPS（延长的药物再利用 患者存活率）。为了实现这一目标，我们将开发以下策略：（1）构建和验证一个 一类新的基于混合学习的多组学预后建模方法，使用七种不同的肝脏 癌症人群队列。(2)开发并实验验证一种可操作的计算药物- 重新利用框架，以提高高风险肝癌患者的生存率， 药物基因组学和药物遗传学数据。(3)构建一个用户友好的网络工具， 从实验室到病床的转变。我们预计，该项目将是开创性的许多 包括在多组学数据集上建立新的预后模型， 延长高危肝癌患者的寿命，为肝癌患者提供第一手的药物资源， 癌症治疗研究共同体。

Project Summary Human cancers are complex diseases that present vast heterogeneity, leading to widely different survival outcomes. Thus actionable and robust predictive models for cancer prognosis are much needed for more personalized treatment and disease management. However, There has been severely lacking of therapeutically actionable computational methods, which explicitly model patient survival difference as the objective while integrating multi-dimension high-throughput genomics data. To solve this issue, we propose a novel actionable framework for caner drug repurposing, called DR. EPS (Drug Repurposing for Extended Patient Survival). Towards this goal, we will develop the following strategies: (1) Constructing and validating a new class of hybrid-learning based, multi-omics prognosis modeling approach, using seven diverse liver cancer population cohorts. (2) Developing and experimentally validating an actionable computational drug- repurposing framework to improve the survival of high-risk liver cancer patients, using big sets of pharmacogenomics and pharmacogenetics data. (3) Building a user-friendly webtool that accelerates such bench-to-bed transition for liver cancer treatment. We expect that this project will be groundbreaking in many aspects, including building new prognostic models on multi-omics data sets, identifying new repurposed drugs to extend high-risk liver cancer patient life-spans, and providing a first-hand drug reposition resource for liver cancer therapeutics research community.