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

Qingchao Qiu, Pengcheng Lu, Yuzhu Xiang, Yu Shyr, Xi Chen, Brian David Lehmann, Daniel Joseph Viox, Alfred L. George, Yajun Yi

Research output: Contribution to journalArticle

4 Citations (Scopus)

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.

Original languageEnglish (US)
Article numbere54979
JournalPLoS One
Volume8
Issue number1
DOIs
StatePublished - Jan 29 2013
Externally publishedYes

Fingerprint

meta-analysis
Gene expression
breast neoplasms
Meta-Analysis
Breast Neoplasms
neoplasms
Neoplasms
Transcriptome
Data integration
gene expression
Neoplasm Genes
prostatic neoplasms
lung neoplasms
Lung Neoplasms
Prostatic Neoplasms
Survival Analysis
Phenotype
Gene Expression
phenotype
Survival

ASJC Scopus subject areas

  • Biochemistry, Genetics and Molecular Biology(all)
  • Agricultural and Biological Sciences(all)

Cite this

A Data Similarity-Based Strategy for Meta-analysis of Transcriptional Profiles in Cancer. / Qiu, Qingchao; Lu, Pengcheng; Xiang, Yuzhu; Shyr, Yu; Chen, Xi; Lehmann, Brian David; Viox, Daniel Joseph; George, Alfred L.; Yi, Yajun.

In: PLoS One, Vol. 8, No. 1, e54979, 29.01.2013.

Research output: Contribution to journalArticle

Qiu, Q, Lu, P, Xiang, Y, Shyr, Y, Chen, X, Lehmann, BD, Viox, DJ, George, AL & Yi, Y 2013, 'A Data Similarity-Based Strategy for Meta-analysis of Transcriptional Profiles in Cancer', PLoS One, vol. 8, no. 1, e54979. https://doi.org/10.1371/journal.pone.0054979
Qiu, Qingchao ; Lu, Pengcheng ; Xiang, Yuzhu ; Shyr, Yu ; Chen, Xi ; Lehmann, Brian David ; Viox, Daniel Joseph ; George, Alfred L. ; Yi, Yajun. / A Data Similarity-Based Strategy for Meta-analysis of Transcriptional Profiles in Cancer. In: PLoS One. 2013 ; Vol. 8, No. 1.
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AB - 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.

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