A CANCER TAXONOMY BASED ON GENE EXPRESSION PATTERNS

基于基因表达模式的癌症分类

• 批准号: 6377575
• 负责人: PATRICK O. BROWN
• 金额: $ 213.91万
• 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/6377575
• 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.

当前的癌症分类系统将临床行为具有重要差异的肿瘤归为一类。 正如临床行为的明显多样性所预期的那样，我们发现肿瘤中的基因表达模式存在巨大差异，而这些肿瘤通常被归为一类。 然而，肿瘤之间整体基因表达模式的变化是有序和系统的，它为每个患者的肿瘤提供了独特且可重复的特征，甚至描绘了其生物学差异的图景。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，并且可以为肿瘤子集的系统聚类提供基础。 因此，我们认为，在我们目前所称的同名癌症之间明显的异质性的基础上，可能存在一种系统的分类法，这种分类法从组织学或通常用于定义肿瘤亚组的一小组标记物中并不容易看出。 我们建议通过系统地、定量地测量每种肿瘤类型的数百个独立肿瘤样本中至少 20,000 个不同基因的转录本丰度变化，来表征乳腺癌、前列腺癌、脑癌和肝癌之间的分子变异。 我们将使用多变量聚类方法来寻找将肿瘤分组的方法，这些聚类在其表达模式上内部一致，因此我们希望在其临床行为上也一致。现在可用于大型回顾性研究的大多数肿瘤样本（这些样本需要测试我们定义的新分类组的临床效用）并不适合在 RNA 水平上分析基因表达。 然而，它们非常适合免疫组织化学表征。 因此，为了从探索和发现癌症分子变异过渡到测试其与临床行为的联系，我们建议在我们研究的肿瘤中鉴定出一大组表达模式变化最大且最独立的基因，并针对预测的蛋白质产物产生抗体。 这些抗体将用于免疫组织化学，以表征不同正常组织、肿瘤样本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，旨在对已知自然史和治疗反应的肿瘤进行分类，以确定基于基因表达模式的新癌症分类法是否可以成功地将这些癌症分类为具有独特且一致的自然史和治疗反应模式的组。 这些抗体将有助于研究癌症的分子发病机制。 其中一些可能为癌症早期检测的非侵入性筛查提供基础，而另一些甚至可能最终用于治疗。