Grant Details
| Grant Number: | 5R21CA227996-02 Interpret this number |
| Primary Investigator: | Cheng, Chao |
| Organization: | Baylor College Of Medicine |
| Project Title: | Computational Identification of New Candidate Drugs for Lung Cancer Treatment |
| Fiscal Year: | 2020 |
Abstract
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.
Publications
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