title: A data similarity-based strategy for meta-analysis of transcriptional profiles in cancer.
abstract: BACKGROUND
  Robust transcriptional signatures in cancer can be identified by data similarity-driven meta-analysis of gene expression profiles. An unbiased data integration and interrogation strategy has not previously been available.
  
  
  RESULTS
  We implemented and performed a large meta-analysis of breast cancer gene expression profiles from 223 datasets containing 10,581 human breast cancer samples using a novel data similarity-based approach (iterative EXALT). Cancer gene expression signatures extracted from individual datasets were clustered by data similarity and consolidated into a meta-signature with a recurrent and concordant gene expression pattern. A retrospective survival analysis was performed to evaluate the predictive power of a novel meta-signature deduced from transcriptional profiling studies of human breast cancer. Validation cohorts consisting of 6,011 breast cancer patients from 21 different breast cancer datasets and 1,110 patients with other malignancies (lung and prostate cancer) were used to test the robustness of our findings. During the iterative EXALT analysis, 633 signatures were grouped by their data similarity and formed 121 signature clusters. From the 121 signature clusters, we identified a unique meta-signature (BRmet50) based on a cluster of 11 signatures sharing a phenotype related to highly aggressive breast cancer. In patients with breast cancer, there was a significant association between BRmet50 and disease outcome, and the prognostic power of BRmet50 was independent of common clinical and pathologic covariates. Furthermore, the prognostic value of BRmet50 was not specific to breast cancer, as it also predicted survival in prostate and lung cancers.
  
  
  CONCLUSIONS
  We have established and implemented a novel data similarity-driven meta-analysis strategy. Using this approach, we identified a transcriptional meta-signature (BRmet50) in breast cancer, and the prognostic performance of BRmet50 was robust and applicable across a wide range of cancer-patient populations.
authors: Department of Medicine, Vanderbilt University, Nashville, TN, USA.
authors: Qingchao
authors: Q
authors: Qiu
authors: Pengcheng
authors: P
authors: Lu
authors: Yuzhu
authors: Y
authors: Xiang
authors: Yu
authors: Y
authors: Shyr
authors: Xi
authors: X
authors: Chen
authors: Brian David
authors: BD
authors: Lehmann
authors: Daniel Joseph
authors: DJ
authors: Viox
authors: Alfred L
authors: AL
authors: George
authors: Yajun
authors: Y
authors: Yi
mesh_terms: D000704:Analysis of Variance; D001943:Breast Neoplasms / Q000235:genetics* / Q000473:pathology; D016000:Cluster Analysis; D020869:Gene Expression Profiling / Q000379:methods*; D006801:Humans
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