Liotta L, Petricoin E
National Cancer Institute, NIH and CBER, FDA, Bethesda, Maryland 20892, USA.
Nat Rev Genet. 2000 Oct;1(1):48-56. doi: 10.1038/35049567.
Traditionally, tumours have been categorized on the basis of histology. However, the staining pattern of cancer cells viewed under the microscope is insufficient to reflect the complicated underlying molecular events that drive the neoplastic process. By surveying thousands of genes at once, using DNA arrays, it is now possible to read the molecular signature of an individual patient's tumour. When the signature is analysed with clustering algorithms, new classes of cancer emerge that transcend distinctions based on histological appearance alone. Using DNA arrays, protein arrays and appropriate experimental models, the ultimate goal is to move beyond correlation and classification to achieve new insights into disease mechanisms and treatment targets.
传统上,肿瘤是根据组织学进行分类的。然而,在显微镜下观察到的癌细胞染色模式不足以反映驱动肿瘤形成过程的复杂潜在分子事件。通过使用DNA阵列一次性检测数千个基因,现在有可能读取个体患者肿瘤的分子特征。当用聚类算法分析这种特征时,就会出现超越仅基于组织学外观差异的新型癌症分类。利用DNA阵列、蛋白质阵列和适当的实验模型,最终目标是超越相关性和分类,以获得对疾病机制和治疗靶点的新见解。