DR. EPS: Drug Repurposing for Extended Patient Survival

博士。

基本信息

批准号：
10186808
负责人：
Lana X Garmire
金额：
--
依托单位：
UNIVERSITY OF MICHIGAN AT ANN ARBOR
依托单位国家：
美国
项目类别：
财政年份：
2019
资助国家：
美国
起止时间：
2019-09-21 至 2023-05-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/10186808
关键词：
Algorithms Animal Model Beds Bioinformatics Cancer Center Cancer Etiology Cancer Patient Cancer Prognosis Cancer cell line Canes Cessation of life Clinic Communities Complex Computer Models Computing Methodologies Contracts DNA DNA Methylation Data Data Set Development Diagnosis Disease Disease Management Gender Gene Expression Profile Genomics Geography Goals Heterogeneity Human Hybrids In Vitro Incidence Internet Investigational Therapies Learning Libraries Longevity Malignant Neoplasms Malignant neoplasm of liver Methodology Methods Michigan MicroRNAs Modeling Multiomic Data Network-based Neurons Pathology Patient-Focused Outcomes Patients Pharmaceutical Preparations Pharmacogenetics Pharmacogenomics Pharmacotherapy Population Predictive Cancer Model Primary carcinoma of the liver cells Prognosis Prognostic Marker Research Research Personnel Resources Risk San Francisco Small RNA Survival Rate Technology The Cancer Genome Atlas Therapeutic Therapeutic Human Experimentation Time Universities Work autoencoder base bench to bedside cancer therapy cancer type chemotherapy clinically actionable clinically significant cohort computer framework curative treatments deep learning drug candidate drug repurposing drug sensitivity drug testing efficacious treatment exome sequencing experimental study genomic data genomic profiles genomic signature high dimensionality high risk improved innovation interest learning strategy mRNA sequencing methylome molecular marker mortality multiple omics novel outcome prediction personalized management personalized medicine precision medicine prediction algorithm prognostic prognostic model response sound survival outcome survival prediction tool transcriptome sequencing user-friendly

项目摘要

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.

项目概要人类癌症是复杂的疾病，具有巨大的异质性，导致生存率差异很大结果。因此，更多的研究非常需要可操作且稳健的癌症预后预测模型。个性化治疗和疾病管理。然而，却严重缺乏治疗上可行的计算方法，明确地将患者生存差异建模为目标，同时整合多维高通量基因组数据。为了解决这个问题，我们提出一个癌症药物再利用的新型可行框架，称为 DR。 EPS（延长药物再利用患者生存）。为实现这一目标，我们将制定以下策略：（1）构建并验证基于混合学习的新型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) 开发并实验验证可操作的计算药物- 重新调整框架以提高高危肝癌患者的生存率，使用大量的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来加速这种过程肝癌治疗从实验室到床位的过渡。我们预计该项目将在许多方面具有开创性方面，包括在多组学数据集上建立新的预后模型，识别新的再利用药物延长高危肝癌患者生命，为肝脏提供第一手药物重新定位资源癌症治疗研究团体。