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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


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. 


Whole transcriptome signature for prognostic prediction (WTSPP): application of whole transcriptome signature for prognostic prediction in cancer.
Authors: Schaafsma E. , Zhao Y. , Wang Y. , Varn F.S. , Zhu K. , Yang H. , Cheng C. .
Source: Laboratory investigation; a journal of technical methods and pathology, 2020 Oct; 100(10), p. 1356-1366.
EPub date: 2020-03-06.
PMID: 32144347
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Authors: Xie F. , Zhang J. , Wang J. , Reuben A. , Xu W. , Yi X. , Varn F.S. , Ye Y. , Cheng J. , Yu M. , et al. .
Source: Clinical cancer research : an official journal of the American Association for Cancer Research, 2020-06-15; 26(12), p. 2908-2920.
EPub date: 2020-01-07.
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An EGFR signature predicts cell line and patient sensitivity to multiple tyrosine kinase inhibitors.
Authors: Cheng C. , Zhao Y. , Schaafsma E. , Weng Y.L. , Amos C. .
Source: International journal of cancer, 2020-05-14; , .
EPub date: 2020-05-14.
PMID: 32406930
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VISTA is a checkpoint regulator for naïve T cell quiescence and peripheral tolerance.
Authors: ElTanbouly M.A. , Zhao Y. , Nowak E. , Li J. , Schaafsma E. , Le Mercier I. , Ceeraz S. , Lines J.L. , Peng C. , Carriere C. , et al. .
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Systematic computational identification of prognostic cytogenetic markers in neuroblastoma.
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