Psychiatric Illness: Multigene Expression Classification

描述（由申请人提供）：本提案旨在基于转录的白色血细胞（白细胞）mRNA的高密度微阵列测量，为精神分裂症和双相情感障碍的生物学分类的发展提供数据。这一建议背后的基本原理是基于两个数据来源：1）文献中的报告，记录了精神分裂症和双相情感障碍患者免疫系统反应介质的差异表达，以及2）作为最近完成的NIMH B启动资助的一部分获得的初步结果。在初步实验中，PI利用外周血白细胞的整体基因表达分析作为精神分裂症的分类媒介。研究结果是惊人的;来自8名精神分裂症患者和5名对照受试者的基因表达数据的无监督层次聚类，导致所有样本分类到正确的组（精神分裂症患者或健康对照）。此外，和意义，目前的建议，层次聚类的基因表达数据从精神分裂症患者和两个双相情感障碍患者，导致双相情感障碍患者分组到一个单一的，离散的亚节点从精神分裂症。 基于这一令人兴奋的结果，本提案的制定有以下具体目标：1a）在该项目的两年时间内，从25名双相情感障碍患者和25名精神分裂症患者收集外周血白细胞，1b）为了测量白细胞样品中的总体基因表达，使用Affyssin基因芯片微阵列技术，以及2）在分层聚类和监督学习算法中使用在该提议的具体目标1下获得的白细胞基因表达数据集，以识别和验证通过诊断组区分受试者的多基因表达签名。目前提出的研究，其中包括收集白细胞样本，从双相情感障碍或精神分裂症患者，已被设计为扩展我们的积极发现，从精神分裂症的初步调查。我们乐观地认为，这项探索性研究的完成将导致创建多基因表达签名，可以将白细胞样本分类为双相情感障碍或精神分裂症患者组，并可用于预测未知样本的类别。如果我们的方法的有效性在这项探索性R21研究中得到证明，我们这项研究的长期目标是通过以下方式扩大这项工作的范围：1）将我们数据集中的受试者数量增加到可接受的统计功效水平，2）调查用于诊断精神分裂症和双相情感障碍的生物特征的可行性，并将该研究扩展到包括其他精神疾病，如重性抑郁症，以及3）研究患者对治疗方案的反应与生物表达特征之间的相关性。这些生物学特征可能反过来刺激靶向新药的开发。最后，我们可能会进行更多的后续研究，招募那些有双相情感障碍或精神分裂症风险增加的家庭成员，这将使我们能够测试是否存在于患病前的受试者中的基因表达模式，以及是否有可能预测疾病的风险。对精神疾病进行生物学分类的公共卫生益处可能很大，特别是如果可以在发病前阶段进行预测性检测，则有可能采取有针对性的预防性干预措施。

DESCRIPTION (provided by applicant): This proposal is designed to produce data for the development of a biological classification of schizophrenia and bipolar disorder, based on high density microarray measurements of transcribed white blood cell (leukocyte) mRNA. The rationale behind this proposal is based on two sources of data; 1) reports in the literature that document differential expression of immune system response mediators among patients with schizophrenia and bipolar disorder, and 2) preliminary results obtained as part of a recently completed NIMH B-start grant. In the preliminary experiments, the PI utilized global gene expression analysis of peripheral leukocytes as a classification medium for schizophrenia. The study results were striking; unsupervised hierarchical clustering of the gene expression data from eight schizophrenic patients and five control subjects, resulted in classification of all the samples into their correct group (schizophrenic patients or healthy controls). In addition, and of significance to this current proposal, hierarchical clustering of the gene expression data from the schizophrenic patients and from two bipolar disorder patients, resulted in the bipolar disorder patients grouping into a single, discrete subnode from the schizophrenics. Based on this exciting result, the present proposal has been developed with the following specific aims: la) To collect peripheral blood leukocytes from 25 men with bipolar disorder and 25 men with schizophrenia, over the two year period of this project, lb) To measure global gene expression in the leukocyte samples, using Affymetrix GeneChip microarray technology, and 2) To employ the leukocyte gene expression dataset obtained under Specific Aim 1 of the proposal, in hierarchical clustering and supervised learning algorithms to identify and validate multi-gene expression signatures that distinguish between the subjects by diagnostic group. The current proposed study, which involves the collection of leukocyte samples from patients with bipolar disorder or schizophrenia, has been designed to extend our positive findings from the preliminary investigation of schizophrenics. We are optimistic that the completion of this proposed exploratory study will lead to the creation of multigene expression signatures that can classify leukocyte samples into bipolar disorder or schizophrenic patient groups, and which can be employed to predict the classes of unknown samples. If the validity of our approach is demonstrated in this exploratory R21 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 datasets to the level of acceptable statistical power, 2) investigating the feasibility of a biological signature for diagnosis of schizophrenia and bipolar disorder, and to extend this investigation to include other psychiatric disorders such as major depression, and 3) investigating correlations between patient responses to treatment regimes and biological expression signatures. These biological signatures may, in turn, stimulate the development of targeted novel medications. Finally, it may be possible to perform additional follow-up studies, recruiting families with members at increased risk of developing bipolar disorder or schizophrenia, which would allow us to test whether gene expression patterns that classify the disorders are present in premorbid subjects and whether it is possible to predict risk of illness. The public health benefits of a biological classification of psychiatric disorders are potentially large, especially if predictive testing in the premorbid stage is possible, raising the possibility of targeted preventative interventions.