Computational Identification of new candidate drugs for lung cancer treatment

肺癌治疗新候选药物的计算鉴定

基本信息

批准号：
9888344
负责人：
CHAO CHENG
金额：
$ 17.38万
依托单位：
BAYLOR COLLEGE OF MEDICINE
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：
2019-04-01 至 2021-09-30
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/9888344
关键词：
Accounting Benign Big Data Cancer Patient Cessation of life Cholesterol Classification Clinical Computational Technique Computational algorithm Computing Methodologies Consumption Data Data Set Data Sources Databases Development Diabetes Mellitus Disease Drug Prescriptions Drug usage Epidemiology Exhibits Exposure to FDA approved Genes Genomics Goals Healthcare Malignant Neoplasms Malignant neoplasm of lung Medicare Metformin Methods Modality Molecular Operative Surgical Procedures Outcome Patients Pharmaceutical Preparations Pharmacotherapy Population Analysis Process Property Reporting Research Sampling Statistical Data Interpretation Statistical Methods Survival Rate Systems Biology Taiwan Therapeutic Agents Therapeutic Effect Time Translating United States United States National Health Insurance anti-cancer base biomedical resource cancer diagnosis cancer gene expression cancer therapy cancer type chemotherapy clinical investigation cohort comorbidity computational platform computer framework cost effective cost efficient data resource drug candidate drug development drug standard epidemiology study genomic data human disease improved innovation mortality mortality risk novel novel anticancer drug novel therapeutics outcome forecast population based pre-clinical screening targeted treatment tool transcriptomics tumor

项目摘要

Project Summary/Abstract Lung cancer is the leading cause and accounts for a quarter of all cancer-associated deaths in the United States. There is a constant and critical need for new therapeutic agents to improve treatment of patients with this disease. However, developing an innovative drug is extremely expensive and time- consuming, taking on average 1.1 billion dollars and 11 years. Drug repurposing analysis, which identifies new diseases or indications of existing drugs, provides an effective solution to this problem. Particularly, in the era of big data, a vast amount of biomedical data have been generated, including different types of genomic data and population-based longitudinal healthcare data. These data provide an excellent opportunity for systematic drug repurposing analysis. The primary goal of this project is to apply computational techniques and statistical methods to utilize large-scale genomic and healthcare data for identifying new candidate drugs to treat lung cancer. Specific Aims: In this project we propose to (1) apply a drug repurposing method called IDEA (Integrative Drug Expression Analysis) developed by our group to systematically predict new candidate drugs for lung cancer by integrating diverse genomic data resources, and (2) apply epidemiological analysis to population-based longitudinal healthcare data to identify commonly used drugs that are associated with mortality decrease in lung cancer. In Aim 1, we will integrate 10 lung cancer gene expression data containing ~2500 tumor samples, clinical information of samples, drug treatment profiles for 20,000 compounds including >1300 FDA-approved drugs, and other genomic data sources. In Aim 2, we will systematically analyze the healthcare data from two nationwide population-based databases: the SEER-Medicare database from the United States and the National Health Insurance Research Database from Taiwan. Significance: This project will combine two complementary drug-repurposing strategies to analyze the two most abundant biomedical data types for systematic drug repurposing analysis in lung cancer. Candidate drugs identified by both genomic-based and healthcare-based analyses are supported by both molecular and epidemiological evidences, and deserve more detailed preclinical and clinical investigation. The resulting frameworks and pipelines can be readily extended to drug repurposing analysis in other cancer types and other human diseases.

项目概要/摘要肺癌是主要原因，占美国所有癌症相关死亡的四分之一美国一直迫切需要新的治疗药物来改善治疗。然而，开发一种创新药物非常昂贵且耗时。平均花费 11 亿美元和 11 年的药物再利用分析。识别新的疾病或现有药物的适应症，为这一问题提供了有效的解决方案。特别是在大数据时代，产生了大量的生物医学数据，包括这些数据提供了不同类型的基因组数据和基于人群的纵向医疗数据。系统药物再利用分析的绝佳机会该项目的主要目标是应用计算技术和统计方法来利用大规模基因组和医疗保健确定治疗肺癌的新候选药物的数据具体目标：在这个项目中，我们提出。 (1) 应用由开发者开发的称为 IDEA（综合药物表达分析）的药物再利用方法我们的团队通过整合不同的基因组来系统地预测肺癌的新候选药物数据资源，(2) 将流行病学分析应用于基于人群的纵向医疗保健数据确定与肺癌死亡率降低相关的常用药物。在目标 1 中，我们将整合10个肺癌基因表达数据，包含约2500个肿瘤样本、临床信息样本数量、20,000 种化合物的药物治疗概况，其中包括超过 1300 种 FDA 批准的药物，以及在目标 2 中，我们将系统地分析来自两个基因组数据源的医疗数据。全国人口数据库：美国和美国的 SEER-Medicare 数据库台湾国家健康保险研究数据库意义：该项目将结合两个项目。互补的药物再利用策略来分析两种最丰富的生物医学数据类型基于基因组的候选药物在肺癌中的系统药物再利用分析。基于医疗保健的分析得到分子和流行病学证据的支持，并且值得进行更详细的临床前和临床研究。由此产生的框架和管道可以。很容易扩展到其他癌症类型和其他人类疾病的药物再利用分析。

项目成果

期刊论文数量（10）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

Immune infiltration difference between tumor and adjacent normal regions is prognostic for gastric cancer patients.

肿瘤与邻近正常区域之间的免疫浸润差异是胃癌患者的预后指标。

DOI：
10.1002/ctd2.8
发表时间：
2022
期刊：
Clinical and translational discovery
影响因子：
0
作者：
Zhang,Baoyi;Yao,Kevin;Cheng,Chao
通讯作者：
Cheng,Chao

A B-Raf V600E gene signature for melanoma predicts prognosis and reveals sensitivity to targeted therapies.

DOI：
10.1002/cam4.4491
发表时间：
2022-03
期刊：
Cancer medicine
影响因子：
4
作者：
Yao K;Zhou E;Cheng C
通讯作者：
Cheng C

A framework to predict the applicability of Oncotype DX, MammaPrint, and E2F4 gene signatures for improving breast cancer prognostic prediction.

DOI：
10.1038/s41598-022-06230-7
发表时间：
2022-02-09
期刊：
Scientific reports
影响因子：
4.6
作者：
Yao K;Tong CY;Cheng C
通讯作者：
Cheng C

Whole transcriptome signature for prognostic prediction (WTSPP): application of whole transcriptome signature for prognostic prediction in cancer.

用于预后预测的全转录组特征（WTSPP）：全转录组特征在癌症预后预测中的应用。

DOI：
10.1038/s41374-020-0413-8
发表时间：
2020
期刊：
Laboratory investigation; a journal of technical methods and pathology
影响因子：
0
作者：
Schaafsma,Evelien;Zhao,Yanding;Wang,Yue;Varn,FrederickS;Zhu,Kenneth;Yang,Huan;Cheng,Chao
通讯作者：
Cheng,Chao

B cell infiltration is highly associated with prognosis and an immune-infiltrated tumor microenvironment in neuroblastoma.