Search

Major depressive disorder (MDD) is the most common psychiatric disease worldwide with a huge socio-economic impact. Pharmacotherapy represents the most common option among 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 depression (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. Here we describe the methodology of the PROMPT project that aims at 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, 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 extremes phenotypes of response. A deep 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 sex-related 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, under real-world conditions, the capability of the algorithm to correctly predict the treatment outcomes. Moreover, in this phase we will investigate shared decision making (SDM) in the context of pharmacogenetic testing and evaluate various needs and perspectives of different stakeholders toward the use of predictive tools for MDD treatment to foster active participation and patients’ empowerment. 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.

PTSD is a prevalent mental disorder that results from exposure to extreme and stressful life events and comes at high costs for both the individual and society. Therapeutic treatment presents the best way to deal with PTSD-the mechanisms underlying change after treatment, however, remain poorly understood. While stress and immune associated gene expression changes have been associated with PTSD development, studies investigating treatment effects at the molecular level so far tended to focus on DNA methylation. Here we use gene-network analysis on whole-transcriptome RNA-Seq data isolated from CD14+ monocytes of female PTSD patients (N = 51) to study pre-treatment signatures of therapy response and therapy-related changes at the level of gene expression. Patients who exhibited significant symptom improvement after therapy showed higher baseline expression in two modules involved in inflammatory processes (including notable examples IL1R2 and FKBP5) and blood coagulation. After therapy, expression of an inflammatory module was increased, and expression of a wound healing module was decreased. This supports findings reporting an association between PTSD and dysregulations of the inflammatory and the hemostatic system and mark both as potentially treatment sensitive.