Psychiatric Illness: Multigene Expression Classification

描述（由申请人提供）：本提案旨在基于转录白细胞（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提供数据。这一建议背后的理由基于两个数据来源；1)文献报道了精神分裂症和双相情感障碍患者中免疫系统反应介质的差异表达，2)作为最近完成的NIMH B-start资助的一部分获得的初步结果。在初步实验中，PI利用外周白细胞的全局基因表达分析作为精神分裂症的分类介质。研究结果是惊人的；对来自8名精神分裂症患者和5名对照受试者的基因表达数据进行无监督分层聚类，结果将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，对来自精神分裂症患者和两名双相情感障碍患者的基因表达数据进行分层聚类，导致双相情感障碍患者从精神分裂症患者中分组为单个离散的子节点，这对当前的建议具有重要意义。基于这一令人振奋的结果，本建议的制定有以下具体目标：la)收集25名男性双相情感障碍患者和25名男性精神分裂症患者的外周血白细胞，在这个项目的两年期间，lb)测量白细胞样本中的整体基因表达，使用Affymetrix基因芯片微阵列技术，以及2)使用根据提案的具体目标1获得的白细胞基因表达数据集，在分层聚类和监督学习算法中，识别和验证多基因表达特征，通过诊断组区分受试者。目前提出的研究包括收集双相情感障碍或精神分裂症患者的白细胞样本，旨在扩展我们对精神分裂症患者的初步调查的积极发现。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特征，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群体，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个探索性的R21研究中得到证明，我们对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)将数据集中的受试者数量增加到可接受的统计能力水平；2)研究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，如重度抑郁症；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。这些生物特征可能反过来刺激靶向新型药物的开发。最后，有可能进行额外的后续研究，招募有成员患双相情感障碍或精神分裂症风险增加的家庭，这将使我们能够测试在发病前受试者中是否存在分类疾病的基因表达模式，以及是否有可能预测疾病的风险。精神疾病的生物学分类对公共卫生的好处可能是巨大的，特别是如果在发病前阶段进行预测性测试是可能的，这提高了有针对性的预防干预的可能性。

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.