A CANCER TAXONOMY BASED ON GENE EXPRESSION PATTERNS

基于基因表达模式的癌症分类

基本信息

批准号：
6514364
负责人：
PATRICK O. BROWN
金额：
$ 225.83万
依托单位：
STANFORD UNIVERSITY
依托单位国家：
美国
项目类别：
财政年份：
1999
资助国家：
美国
起止时间：
1999-09-30 至 2004-03-31
项目状态：
已结题

项目摘要

Current systems for classification of cancer group together tumors with important differences in clinical behavior. As might have been expected from the manifest diversity in clinical behavior, we have found that there is enormous variation in gene expression patterns in tumors that would classically be grouped together. The variation among tumors in global gene expression patterns is, however, orderly and systematic and it provides a distinctive and reproducible signature for each patient s tumor, and even paints a picture of their biological differences. Moreover, we have found that variation in expression profiles can highlight unrecognized similarities and differences among tumors, and can provide a basis for systematic clustering of subsets of tumors. We therefore believe that underlying the apparent heterogeneity among cancers that we currently call by the same name, there may be a systematic taxonomy that is not readily apparent from histology or the small set of markers usually used to define subgroups of tumors. We propose to characterize the molecular variations among cancers of the breast, prostate, brain, and liver, by systematically and quantitatively measuring variation in transcript abundance for at least 20,000 different genes, in several hundred independent tumor samples from each of these tumor types. We will use multivariate clustering methods to search for ways to group tumors into clusters that are internally coherent in their expression patterns and thus, we hope, in their clinical behavior. Most of the tumor samples that are now available for the large retrospective studies that will be required to test the clinical utility of the new taxonomic groups we define are not suitable for analysis of gene expression at the RNA level. They are, however, well suited to immunohistochemical characterization. To make the transition from exploration and discovery of the molecular variation in cancer, to testing its connection to clinical behavior, we therefore propose to identify a large set of genes whose expression pattern varies most, and most independently, among the tumors we study, and raise antibodies against the predicted protein products. These antibodies will be used for immunohistochemistry, to characterize the variation in expression of the corresponding proteins among a diverse set of normal tissues, tumor samples and cultured cell lines. These antibody reagents will then be used for retrospective studies aimed at classifying tumors for which the natural history and treatment response is already known, to determine whether a new cancer taxonomy based on gene expression patterns can successfully order these cancers into groups with distinctive and consistent natural histories and patterns of response to treatment. These antibodies will aid investigations of the molecular pathogenesis of cancer. Some of them may provide a basis for non-invasive screens for early detection of cancers, and others could eventually even be used therapeutically.

癌组分类的当前系统肿瘤将临床行为具有重要差异。正如临床行为的明显多样性所期望的那样，我们发现肿瘤中基因表达模式存在巨大变化，而基因表达模式将在经典中分组在一起。然而，全球基因表达模式中肿瘤之间的变化是有序的，系统的，它为每个患者的肿瘤提供了独特而可重复的特征，甚至还描绘了其生物学差异的图片。此外，我们发现表达曲线的变化可以突出肿瘤之间未知的相似性和差异，并且可以为肿瘤亚群的系统聚类提供基础。因此，我们认为，我们目前以同名称呼的癌症的明显异质性的根本存在，可能会有一种系统的分类法，而从组织学或通常用于定义肿瘤亚组的小型标记中不容易显而易见。我们建议通过系统地和定量测量至少20,000种不同基因的转录物丰度变化，在每种肿瘤类型中的几百个独立的肿瘤样品中，通过系统地和定量测量转录丰度的变化来表征乳腺，前列腺，脑和肝脏的分子变异。我们将使用多元聚类方法来寻找将肿瘤分组为内部表达模式相干的簇的方法，因此，我们希望他们的临床行为。现在，我们定义的新分类学组的临床实用性所需的大多数肿瘤样品现在可用于大型回顾性研究，不适合在RNA水平上分析基因表达。但是，它们非常适合免疫组织化学特征。因此，为了从探索和发现癌症分子变异，测试其与临床行为的联系，因此，我们建议确定一大种基因，其表达模式的变化最大，在我们研究的肿瘤中最独立，并提高针对预测蛋白质产物的抗体。这些抗体将用于免疫组织化学，以表征各种正常组织，肿瘤样品和培养细胞系中相应蛋白质表达的变化。然后，这些抗体试剂将用于回顾性研究，旨在对肿瘤进行分类，以确定基于基因表达模式的新的癌症分类学是否可以成功地将这些癌症成功命令分为具有独特且一致的自然史和治疗反应模式的群体。这些抗体将有助于研究癌症的分子发病机理。其中一些可能为早期发现癌症的非侵入性筛查提供基础，而另一些则最终甚至可以通过治疗使用。