Pareto Optimization Identifies Diverse Set of Phosphorylation Signatures Predicting Response to Treatment with XXXX .

@entity1 @entity8947 @entity0 @entity23289 @entity1149
UNASSIGNED: Multivariate biomarkers that can predict the effectiveness of targeted therapy in individual @entity1 are highly desired. Previous biomarker discovery studies have largely focused on the identification of single biomarker signatures, aimed at maximizing prediction accuracy. Here, we present a different approach that identifies multiple biomarkers by simultaneously optimizing their predictive power, number of features, and proximity to the drug target in a protein-protein interaction network. To this end, we incorporated NSGA-II, a fast and elitist multi-objective optimization algorithm that is based on the principle of Pareto optimality, into the biomarker discovery workflow. The method was applied to quantitative phosphoproteome data of 19 @entity1149 ( @entity1149 ) cell lines from a previous biomarker study. The algorithm successfully identified a total of 77 candidate biomarker signatures predicting response to treatment with @entity8947 . Through filtering and similarity clustering, this set was trimmed to four final biomarker signatures, which then were validated on an independent set of @entity0 cell lines. All four candidates reached the same good prediction accuracy (83%) as the originally published biomarker. Although the newly discovered signatures were diverse in their composition and in their size, the central protein of the originally published signature - @entity23289 ( @entity23289 ) - was also present in all four Pareto signatures, confirming its pivotal role in predicting @entity8947 response in @entity1149 cell lines. In summary, the method presented here allows for a robust and simultaneous identification of multiple multivariate biomarkers that are optimized for prediction performance, size, and relevance.
