SynthesisPloS one2013
A data similarity-based strategy for meta-analysis of transcriptional profiles in cancer.
Synthesis in PloS one, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers, 1 of them a synthesis that pooled it.
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Who cites it
4 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Evaluation of public cancer datasets and signatures identifies TP53 mutant signatures with robust prognostic and predictive value.BMC cancer · 2015Pooled it
- Identification of a gene-expression predictor for diagnosis and personalized stratification of lupus patients.PloS one · 2018Article
- A Murine Model of K-RAS and β-Catenin Induced Renal Tumors Expresses High Levels of E2F1 and Resembles Human Wilms Tumor.The Journal of urology · 2015Article
- SPARCL1 suppresses metastasis in prostate cancer.Molecular oncology · 2013Article
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Authors and funding
9 authors.
Funding
Abstract
backgroundRobust 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
findingsWe 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.
conclusionsWe 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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