title: Evaluating the molecule-based prediction of clinical drug responses in cancer.
abstract: Molecule-based prediction of drug response is one major task of precision oncology. Recently, large-scale cancer genomic studies, such as The Cancer Genome Atlas (TCGA), provide the opportunity to evaluate the predictive utility of molecular data for clinical drug responses in multiple cancer types.
  
  
  
  Here, we first curated the drug treatment information from TCGA. Four chemotherapeutic drugs had more than 180 clinical response records. Then, we developed a computational framework to evaluate the molecule based predictions of clinical responses of the four drugs and to identify the corresponding molecular signatures. Results show that mRNA or miRNA expressions can predict drug responses significantly better than random classifiers in specific cancer types. A few signature genes are involved in drug response related pathways, such as DDB1 in DNA repair pathway and DLL4 in Notch signaling pathway. Finally, we applied the framework to predict responses across multiple cancer types and found that the prediction performances get improved for cisplatin based on miRNA expressions. Integrative analysis of clinical drug response data and molecular data offers opportunities for discovering predictive markers in cancer. This study provides a starting point to objectively evaluate the molecule-based predictions of clinical drug responses.
  
  
  
  jgu@tsinghua.edu.cn
  
  
  
  Supplementary data are available at Bioinformatics online.
authors.affiliation: MOE Key Laboratory of Bioinformatics, TNLIST Bioinformatics Division & Center for Synthetic and Systems Biology, Department of Automation, Tsinghua University, Beijing 100084, China.
authors.forename: Zijian
authors.initials: Z
authors.lastname: Ding
authors.affiliation: MOE Key Laboratory of Bioinformatics, TNLIST Bioinformatics Division & Center for Synthetic and Systems Biology, Department of Automation, Tsinghua University, Beijing 100084, China.
authors.forename: Songpeng
authors.initials: S
authors.lastname: Zu
authors.affiliation: MOE Key Laboratory of Bioinformatics, TNLIST Bioinformatics Division & Center for Synthetic and Systems Biology, Department of Automation, Tsinghua University, Beijing 100084, China.
authors.forename: Jin
authors.initials: J
authors.lastname: Gu
mesh_terms: D018906:Antineoplastic Agents, Alkylating / Q000494:pharmacology*; D019295:Computational Biology; D004305:Dose-Response Relationship, Drug; D023281:Genomics; D006801:Humans; D035683:MicroRNAs; D009369:Neoplasms / Q000188:drug therapy* / Q000235:genetics*; D011237:Predictive Value of Tests; D012333:RNA, Messenger; D016896:Treatment Outcome
chemical_list: D018906:Antineoplastic Agents, Alkylating; D035683:MicroRNAs; D012333:RNA, Messenger