title: Evaluation of public cancer datasets and signatures identifies TP53 mutant signatures with robust prognostic and predictive value.
abstract: BACKGROUND
  Systematic analysis of cancer gene-expression patterns using high-throughput transcriptional profiling technologies has led to the discovery and publication of hundreds of gene-expression signatures. However, few public signature values have been cross-validated over multiple studies for the prediction of cancer prognosis and chemosensitivity in the neoadjuvant setting.
  
  
  METHODS
  To analyze the prognostic and predictive values of publicly available signatures, we have implemented a systematic method for high-throughput and efficient validation of a large number of datasets and gene-expression signatures. Using this method, we performed a meta-analysis including 351 publicly available signatures, 37,000 random signatures, and 31 breast cancer datasets. Survival analyses and pathologic responses were used to assess prediction of prognosis, chemoresponsiveness, and chemo-drug sensitivity.
  
  
  RESULTS
  Among 31 breast cancer datasets and 351 public signatures, we identified 22 validation datasets, two robust prognostic signatures (BRmet50 and PMID18271932Sig33) in breast cancer and one signature (PMID20813035Sig137) specific for prognosis prediction in patients with ER-negative tumors. The 22 validation datasets demonstrated enhanced ability to distinguish cancer gene profiles from random gene profiles. Both prognostic signatures are composed of genes associated with TP53 mutations and were able to stratify the good and poor prognostic groups successfully in 82%and 68% of the 22 validation datasets, respectively. We then assessed the abilities of the two signatures to predict treatment responses of breast cancer patients treated with commonly used chemotherapeutic regimens. Both BRmet50 and PMID18271932Sig33 retrospectively identified those patients with an insensitive response to neoadjuvant chemotherapy (mean positive predictive values 85%-88%). Among those patients predicted to be treatment sensitive, distant relapse-free survival (DRFS) was improved (negative predictive values 87%-88%). BRmet50 was further shown to prospectively predict taxane-anthracycline sensitivity in patients with HER2-negative (HER2-) breast cancer.
  
  
  CONCLUSIONS
  We have developed and applied a high-throughput screening method for public cancer signature validation. Using this method, we identified appropriate datasets for cross-validation and two robust signatures that differentiate TP53 mutation status and have prognostic and predictive value for breast cancer patients.
authors.affiliation: Department of Biochemistry, Vanderbilt University, Nashville, TN, USA. brian.d.lehmann@vanderbilt.edu.|Vanderbilt Ingram Cancer Center, Vanderbilt University, Nashville, TN, USA. brian.d.lehmann@vanderbilt.edu.
authors.forename: Brian David
authors.initials: BD
authors.lastname: Lehmann
authors.affiliation: Department of Dermatology, Hainan General Hospital, Haikou, Hainan, China. annymilk@126.com.
authors.forename: Yan
authors.initials: Y
authors.lastname: Ding
authors.affiliation: Feinberg School of Medicine, Northwestern University, Chicago, IL, USA. daniel-viox@fsm.northwestern.edu.
authors.forename: Daniel Joseph
authors.initials: DJ
authors.lastname: Viox
authors.affiliation: Division of Epidemiology, Vanderbilt University, Nashville, TN, USA. ming.jiang.1@ntu.edu.cn.|Vanderbilt Ingram Cancer Center, Vanderbilt University, Nashville, TN, USA. ming.jiang.1@ntu.edu.cn.|Laboratory of Nuclear Receptors and Cancer Research, Center for Basic Medical Research, Nantong University School of Medicine, Nantong, Jiangsu, China. ming.jiang.1@ntu.edu.cn.
authors.forename: Ming
authors.initials: M
authors.lastname: Jiang
authors.affiliation: Pediatric Surgery Department, Qilu Hospital of Shandong University, Jinan, Shangdong, China. milozhengyi@gmail.com.
authors.forename: Yi
authors.initials: Y
authors.lastname: Zheng
authors.affiliation: Department of Cardiovascular Disease, Hainan General Hospital, Haikou, Hainan, China. crain_lw@163.com.
authors.forename: Wang
authors.initials: W
authors.lastname: Liao
authors.affiliation: Department of Biostatistics, Vanderbilt University, Nashville, TN, USA. steven.chen@vanderbilt.edu.
authors.forename: Xi
authors.initials: X
authors.lastname: Chen
authors.affiliation: Department of Pediatrics, Maternal and Child Health Care Hospital of Hainan Province, Haikou, China. xiangwei8@163.com.
authors.forename: Wei
authors.initials: W
authors.lastname: Xiang
authors.affiliation: Department of Medicine, Vanderbilt University, Nashville, TN, USA. yajun.yi@vanderbilt.edu.|Division of Genetic Medicine, 536A Light Hall, Vanderbilt University, 2215 Garland Avenue, Nashville, TN, 37232-0275, USA. yajun.yi@vanderbilt.edu.
authors.forename: Yajun
authors.initials: Y
authors.lastname: Yi
mesh_terms: D001943:Breast Neoplasms / Q000188:drug therapy / Q000235:genetics* / Q000473:pathology; D001952:Bridged-Ring Compounds / Q000627:therapeutic use; D030541:Databases, Genetic; D005260:Female; D020869:Gene Expression Profiling; D015972:Gene Expression Regulation, Neoplastic; D006801:Humans; D053208:Kaplan-Meier Estimate; D020360:Neoadjuvant Therapy; D009363:Neoplasm Proteins / Q000096:biosynthesis* / Q000235:genetics; D011379:Prognosis; D011960:Receptors, Estrogen / Q000096:biosynthesis / Q000235:genetics; D043823:Taxoids / Q000627:therapeutic use; D016159:Tumor Suppressor Protein p53 / Q000235:genetics*
chemical_list: D001952:Bridged-Ring Compounds; D009363:Neoplasm Proteins; D011960:Receptors, Estrogen; C495901:TP53 protein, human; D043823:Taxoids; D016159:Tumor Suppressor Protein p53; C080625:taxane