title: Pareto Optimization Identifies Diverse Set of Phosphorylation Signatures Predicting Response to Treatment with Dasatinib.
abstract: Multivariate biomarkers that can predict the effectiveness of targeted therapy in individual patients 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 non-small cell lung cancer (NSCLC) cell lines from a previous biomarker study. The algorithm successfully identified a total of 77 candidate biomarker signatures predicting response to treatment with dasatinib. Through filtering and similarity clustering, this set was trimmed to four final biomarker signatures, which then were validated on an independent set of breast cancer 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 - integrin β4 (ITGB4) - was also present in all four Pareto signatures, confirming its pivotal role in predicting dasatinib response in NSCLC 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.
authors.affiliation: Evotec (München) GmbH, Dept. of Bioinformatics, Am Klopferspitz 19a, 82152 Martinsried, Germany.
authors.forename: Martin
authors.initials: M
authors.lastname: Klammer
authors.affiliation: Evotec (München) GmbH, Dept. of Bioinformatics, Am Klopferspitz 19a, 82152 Martinsried, Germany.
authors.forename: J Nikolaj
authors.initials: JN
authors.lastname: Dybowski
authors.affiliation: Center for Medical Biotechnology, University of Duisburg-Essen, Universitätsstrasse 1-4, 45141 Essen, Germany.
authors.forename: Daniel
authors.initials: D
authors.lastname: Hoffmann
authors.affiliation: Evotec (München) GmbH, Dept. of Bioinformatics, Am Klopferspitz 19a, 82152 Martinsried, Germany; Max-Plack Institute for Biochemistry, Am Klopferspitz 18, 82152 Martinsried, Germany.
authors.forename: Christoph
authors.initials: C
authors.lastname: Schaab
mesh_terms: D000465:Algorithms; D000970:Antineoplastic Agents / Q000627:therapeutic use / Q000633:toxicity*; D014408:Biomarkers, Tumor / Q000378:metabolism; D002289:Carcinoma, Non-Small-Cell Lung / Q000188:drug therapy / Q000378:metabolism / Q000473:pathology; D045744:Cell Line, Tumor; D016000:Cluster Analysis; D000069439:Dasatinib / Q000627:therapeutic use / Q000633:toxicity*; D006801:Humans; D039663:Integrin beta4 / Q000235:genetics / Q000378:metabolism; D008175:Lung Neoplasms / Q000188:drug therapy / Q000378:metabolism / Q000473:pathology; D010748:Phosphopeptides / Q000378:metabolism; D010766:Phosphorylation / Q000187:drug effects; D020543:Proteome / Q000187:drug effects* / Q000378:metabolism
chemical_list: D000970:Antineoplastic Agents; D014408:Biomarkers, Tumor; D039663:Integrin beta4; D010748:Phosphopeptides; D020543:Proteome; D000069439:Dasatinib