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The role of blood-based biomarkers in advancing personalized therapy of schizophrenia

ABSTRACT Introduction: The discovery of potential biomarkers to improve the clinical management of schizophrenia has received considerable attention. While blood-based markers of this mental disorder have particular appeal, due to their accessibility as part of routine clinical sample collection, the discovery of such peripheral markers however faces specific challenges. It is becoming increasingly clear that the identification of clinically useful biomarker profiles will require integrative analysis across data modalities, to untangle systems-wide etiological mechanisms. Areas covered: In this review, the authors focus on multivariate integration of high-dimensional data sources, aimed at the characterization of schizophrenia patients at the individual subject level. The authors present current advances of multi-omics integration in schizophrenia research and discuss computational strategies towards this goal. Expert commentary: Integrative analysis approaches may have to initially focus on individual biological systems of high etiological plausibility. This may help to capture the likely complex structure of etiological mechanisms across data modalities and to take advantage of powerful machine learning methods. The rate at which such methods can learn the etiological complexity from multi-modal data will likely be a bottleneck for development of clinically useful biomarker signatures.