Evaluation of public @entity5 datasets and signatures identifies XXXX mutant signatures with robust prognostic and predictive value.

@entity1 @entity0 @entity5 @entity5948 @entity3907 @entity876
BACKGROUND: Systematic analysis of @entity5 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 @entity5 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 @entity0 datasets. Survival analyses and pathologic responses were used to assess prediction of prognosis, chemoresponsiveness, and chemo-drug sensitivity. RESULTS: Among 31 @entity0 datasets and 351 public signatures, we identified 22 validation datasets, two robust prognostic signatures (BRmet50 and PMID18271932Sig33) in @entity0 and one signature (PMID20813035Sig137) specific for prognosis prediction in @entity1 with @entity5 . The 22 validation datasets demonstrated enhanced ability to distinguish @entity5 gene profiles from random gene profiles. Both prognostic signatures are composed of genes associated with @entity876 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 @entity0 @entity1 treated with commonly used chemotherapeutic regimens. Both BRmet50 and PMID18271932Sig33 retrospectively identified those @entity1 with an insensitive response to neoadjuvant chemotherapy (mean positive predictive values 85%-88%). Among those @entity1 predicted to be treatment sensitive, distant relapse-free survival (DRFS) was improved (negative predictive values 87%-88%). BRmet50 was further shown to prospectively predict @entity3907 - @entity5948 sensitivity in @entity1 with @entity0 . CONCLUSIONS: We have developed and applied a high-throughput screening method for public @entity5 signature validation. Using this method, we identified appropriate datasets for cross-validation and two robust signatures that differentiate @entity876 mutation status and have prognostic and predictive value for @entity0 @entity1 .
