Classifying Schizophrenia:Leukocyte Multigene Signatures

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DESCRIPTION (provided by applicant): This proposal is designed to produce data for development of a biological classification of schizophrenic patients, based on high-density microarray measurement of transcribed white blood cell (leukocyte) RNA. In a recently completed NIMH B-start grant, the PI has utilized global gene expression analysis of peripheral leukocytes as a classification medium for schizophrenia. The preliminary study results were striking; hierarchical clustering of the gene expression data from seven schizophrenic patients and five controls, resulted in classification of all the samples into their correct group (schizophrenic patients or healthy controls). This exciting result has led to the present proposal with the following Specific Aims: (1) a. To collect peripheral blood leukocytes from twenty neuroleptic-naive schizophrenics, twelve neuroleptic-treated schizophrenics, and fourteen healthy control subjects over the two-year period of the project, and b. To employ Affymetrix GeneChip mieroarray technology to measure global gene expression in the leukocyte samples. (2) The leukocyte gene expression datasets collected during the preliminary study and the proposal, resulting in a final analysis of 58 subjects, will be combined and analyzed by hierarchical clustering and discriminate analyses to identify and validate multi-gene fingerprints that differentiate schizophrenic subjects from healthy controls. The current proposed study, which incorporates the collection of samples from neurolepfic-naive schizophrenic patients, is designed to avoid the potential confounding factor of neuroleptic-medication induced gene expression changes. We are optimistic that completion of this proposed exploratory study will lead to the creation of a multigene expression signature that can classify leukocyte samples into schizophrenic patient or control groups and that can be used to predict the class of unknown samples. If the validity of our approach is demonstrated in this exploratory study, our longer-term aims for this research are to increase the scope of this work by: 1. Increasing the number of subjects in our schizophrenic dataset to the level of acceptable statistical power and also duplicating this dataset for other psychiatric disorders including bipolar disorder, schizoaffective disorder and major depression. This will allow us to test these classification signatures and thus attempt biological diagnosis of psychiatric disorders and possible biological subtypes of those disorders. These biological signatures may, in turn, stimulate the development of targeted novel medications. 2. Perform a follow-up study recruiting families with members at increased risk of developing schizophrenia. These families will allow us to test whether gene expression patterns that classify schizophrenia are present in premorbid subjects and whether it is possible to predict risk of illness, and thus provide early treatment that might mitigate the course of the disorder. The public health benefits of a biological classification of schizophrenia are potentially large, especially if predictive testing in the premorbid stage is possible, raising the possibility of targeted preventative treatment.