The polygenic risk score, already used in the personalized treatment of cancers, finds here a new application, in the prediction of the response to antipsychotic drugs, in patients with schizophrenia. According to this work by a team from the Feinstein Institute for Medical Research (New York) presented in the American Journal of Psychiatry, the future of psychosis treatment also involves precision medicine.
Schizophrenia is not a rare psychotic illness, it is one of the main causes of disability in rich countries. Characterized by delusions, hallucinations, and disorganized thoughts and behaviors, schizophrenia is treated with antipsychotic drugs, but it is not uncommon to have to "try" several different treatments before obtaining a satisfactory response from the patient. An uncertainty that weighs heavily not only on patients and their families, but also on professionals and health systems. The objective here is therefore to be able to predict the most appropriate treatment.
The interest of polygenic risk scores in psychiatry: here the team uses genetic tests to predict the response to drugs in patients suffering from a first episode of schizophrenia. And rather than looking for a single gene, researchers use the still recent approach of “polygenic risk scores”, based on the search for several thousand genetic variants across the entire genome. These scores will allow, underlines the main author, Dr. Jian-Ping Zhang, professor at the Feinstein Institute, “to better represent and detect the very complex genetic nature of schizophrenia”.
Towards precision psychiatry: Researchers show that patients with a high polygenic risk score or higher genetic load are indeed less likely to respond to conventional antipsychotic treatment. These results are further reproduced in 2 independent cohorts. These data open the way to “precision medicine” approaches in psychiatry, as we can know in oncology.
In short, this work is described as a major advance in the field of precision medicine for schizophrenia, “a major advance over current trial -and- error approaches ”.
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