A CANCER TAXONOMY BASED ON GENE EXPRESSION PATTERNS

基于基因表达模式的癌症分类

• 批准号: 6175291
• 负责人: PATRICK O. BROWN
• 金额: $ 207.28万
• 依托单位: STANFORD UNIVERSITY
• 依托单位国家: 美国
• 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
• 来源: https://reporter.nih.gov/project-details/6175291
• 关键词: brain neoplasms; breast neoplasms; clinical research; cooperative study; gene expression; genetic registry /resource /referral center; human data; human genetic material tag; human subject; human tissue; immunocytochemistry; liver neoplasms; molecular biology information system; neoplasm /cancer classification /staging; neoplasm /cancer genetics; nucleic acid hybridization; nucleic acid quantitation /detection; prostate neoplasms; statistics /biometry

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.

现行的癌症分类系统将肿瘤分组与临床行为的重要差异结合起来。正如从临床行为的明显多样性中可能预期的那样，我们发现，在肿瘤中，基因表达模式存在巨大的差异，这些基因表达模式通常被归类在一起。然而，不同肿瘤之间的全球基因表达模式的变化是有序和系统的，它为每个患者S肿瘤提供了一个独特和可重现的签名，甚至描绘了它们的生物学差异。此外，我们发现，表达谱的变化可以突出肿瘤之间未被识别的相似性和差异性，并可以为肿瘤亚群的系统聚类提供基础。因此，我们认为，在目前我们称之为相同名称的癌症之间的明显异质性之下，可能存在一种从组织学或通常用于定义肿瘤亚组的小标记集中不太明显的系统分类。我们建议通过系统和定量地测量至少20,000个不同基因在数百个独立的肿瘤样本中的转录丰度变化来表征乳腺癌、前列腺癌、脑癌和肝癌之间的分子差异。我们将使用多变量聚类方法来寻找将肿瘤分组的方法，使其在表达模式上内部一致，从而希望在临床行为方面也是如此。目前可用于大型回溯性研究的大多数肿瘤样本不适合在RNA水平上分析基因表达，这将需要测试我们定义的新分类组的临床实用性。然而，它们非常适合免疫组织化学表征。为了从探索和发现癌症的分子变异过渡到测试其与临床行为的联系，因此，我们建议在我们研究的肿瘤中识别其表达模式变化最大和最独立的一大组基因，并针对预测的蛋白产物产生抗体。这些抗体将用于免疫组织化学，以表征相应蛋白在一组不同的正常组织、肿瘤样本和培养细胞系中的表达变化。然后，这些抗体试剂将被用于旨在对已知自然病史和治疗反应的肿瘤进行分类的回溯性研究，以确定基于基因表达模式的新癌症分类是否能够成功地将这些癌症分成具有独特和一致的自然病史和治疗反应模式的组。这些抗体将有助于癌症分子发病机制的研究。其中一些可能为早期发现癌症的非侵入性筛查提供基础，另一些甚至最终可能被用于治疗。