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 dataforidentifyingnewcandidatedrugstotreatlungcancer.
Specific Aims :Inthisprojectwepropose to(1)applyadrugrepurposingmethodcalledIDEA(IntegrativeDrugExpressionAnalysis)developedby 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 toidentifycommonlyuseddrugsthatareassociatedwithmortalitydecreaseinlungcancer.
In Aim1, we willintegrate10lungcancergeneexpressiondatacontaining~2500tumorsamples,clinicalinformation 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 NationalHealthInsuranceResearchDatabasefromTaiwan.Significance:Thisprojectwillcombinetwo complementarydrug-repurposingstrategiestoanalyzethetwomostabundantbiomedicaldatatypesfor systematicdrugrepurposinganalysisinlungcancer.Candidatedrugsidentifiedbybothgenomic-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 bereadilyextendedtodrugrepurposinganalysisinothercancertypesandotherhumandiseases.
It is extremely expensive and time-consuming to develop a new drug. Drug repurposing provides an effective solution by identifying new indications of old drugs. In this project we propose to perform systematic drug repurposing analysis to identify new candidate drugs for treating lung cancer, which accounts for 25% of all cancer-associated mortalities. We will apply two complementary strategies that utilize two massive biomedical resources, the large-scale genomic data and the population-based longitudinalhealthcaredata,throughintegrativecomputationalapproachesandstatisticalanalyses.
