Major depressive disorder (MDD) is the most common psychiatric disease worldwide, with a huge socio-economic impact. Pharmacotherapy represents the first-line treatment choice; however, only about one third of patients respond to the first trial and about 30% are classified as treatment-resistant (TRD). TRD is associated with specific clinical features and genetic/gene expression signatures. To date, single sets of markers have shown limited power in response prediction. The aim of this project is the development of a precision medicine algorithm that would help early detection of non-responder patients, who might be more prone to later develop TRD. To address this, aim the project will be organized in 2 phases. Phase 1 will involve 300 patients with MDD already recruited, comprising 150 TRD and 150 responders, considered as extreme phenotypes of response. An accurate clinical stratification will be performed for all patients; moreover, a genomic, transcriptomic and miRNomic profiling will be conducted. The data generated will be exploited to develop an innovative algorithm integrating clinical, omics and gender data, in order to predict treatment response and TRD development. In phase 2, a new naturalistic cohort of 300 MDD patients will be recruited to assess, in real-world conditions, the capability of the algorithm to correctly predict the treatment outcomes. Moreover, in this phase an active participation and advocacy of patients will be established as a critical component for successful consideration of patients' perspective and needs on the use of predictive tools for MDD treatment. This project represents a proof-of-concept study. The obtained results will provide information about the feasibility and usefulness of the proposed approach, with the perspective of designing future clinical trials in which algorithms could be tested as a predictive tool to drive decision making by clinicians, enabling a better prevention and management of MDD resistance.