A Data Similarity-Based Strategy for Meta-analysis of Transcriptional Profiles in Cancer

• Type of Material: Article
• Authors: Qingchao Qiu, Vanderbilt University; Pengcheng Lu, Vanderbilt University; Yuzhu Xiang, Vanderbilt University; Yu Shyr, Vanderbilt University; Xi Chen, Vanderbilt University; Brian David Lehmann, Vanderbilt University; Daniel Viox, Emory University; Alfred L. George, Jr., Vanderbilt University; Yajun Yi, Vanderbilt University
• Language: English
• Date: 2013-01-29
• Publisher: Public Library of Science
• Publication Version: Final Published Version
• Copyright Statement: © 2013 Qiu et al.
• License: Creative Commons Attribution 4.0 International
• Final Published Version (URL): http://dx.doi.org/10.1371/journal.pone.0054979
• Title of Journal or Parent Work: PLOS ONE
• Volume: 8
• Issue: 1
• Start Page: e54979
• End Page: e54979
• Grant/Funding Information: This work was supported in part by a Howard Temin Award from the National Cancer Institute at the National Institutes of Health (CA114033 to YY), American Cancer Society-Institutional Research Grant (#IRG-58-009-51 to YY), and the Vanderbilt Clinical and Translational Science Awards (CTSA) UL1 RR024975 from National Center for Research Resources (NCRR), a part of the National Institutes of Health (NIH), (CRC1838 to YY).
• Keywords: 70-GENE PROGNOSIS-SIGNATURE; PREDICT; SAMPLE-SIZE CALCULATIONS; BREAST-CANCER; Science & Technology - Other Topics; GENE-EXPRESSION SIGNATURES; DNA MICROARRAYS; RISK STRATIFICATION; Multidisciplinary Sciences; VALIDATION; Science & Technology; HISTOLOGIC GRADE; DISTANT METASTASIS
• Research Categories: Biology, Genetics; Health Sciences, Oncology

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. Methods and Findings: 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.