Combining differential gene expression analysis and genetic data predict treatment response in first-episode psychosis

• Published in: European neuropsychopharmacology 2019, Vol.29, p.1318-1318
• Main Authors: Jamain, Stephane, Troudet, Réjane, Ali, Wafa Bel Haj, Barau, Caroline, Boland-Auge, Anne, Deleuze, Jean-François, Leboyer, Marion, Consortium, O. Ptimise
• Format: Article
• Language: English
• Subjects: Biochemistry, Molecular Biology; Genetics; Genomics; Life Sciences
• ISSN: 0924-977X; 1873-7862
• DOI: 10.1016/j.euroneuro.2018.08.463

Background: Despite nearly fifty years of pharmacological research, the treatment of schizophrenia remains a challenge and clinical outcomes are still far from optimal. One of the major shortcomings in the current treatment of schizophrenia is that we have no valid criteria in clinical practice to predict who will respond to antipsychotic treatment and how long the treatment should be maintained before changing therapeutic strategy. The identification of blood-based biological markers of drug response with a good sensitivity and specificity would enable physicians to use these tests prior to choosing the antipsychotic treatment and therefore help the practitioner in his daily clinical practice. Methods: Through a European consortium on Optimization of Treatment and Management of Schizophrenia in Europe (OPTiMiSE), we investigated treatment response in 188 individuals with first episode psychoses. Using RNA sequencing, we characterized changes in gene expression after 4-week treatment with amisulpride according to treatment outcome. In addition, we genotyped subjects with DNA array to identify eQTLs, and used this eQTLs to propose a polygenic score to predict treatment outcome. Results: Out of the 16,264 genes expressed in peripheral blood mononuclear cells, we showed an enrichment in differentially expressed genes in subjects who will be in remission after 4-week amilsupride treatment, when compared with non-remitted patients. We thus demonstrated that 10% of differentially expressed genes had a change in the expression level, which was correlated with clinical outcome. We identified many eQTLs that may explain transcriptional variations between responders and non-responders to treatment. The combination of these eQTLs in a polygenic score allowed the prediction of clinical improvement with an accuracy of 0.7 on the discovery sample of 135 individuals and 0.6 on an independent sample of 129 subjects. Discussion: We demonstrated here that amisulpride treatment affects gene expression in peripheral blood mononuclear cells, mainly in patients who will be in remission after four-week treatment, and that gene expression was associated with symptom improvement. We also showed that combining transcription and genetic data might help in the identification of biological signature to predict treatment response in first episode psychosis.