Functional genomics of cancer

癌症生物学中的主要问题之一是确定肿瘤细胞中基因表达的异常模式，并将这种模式与肿瘤发生过程中发生的特定基因组变化联系起来。为了解决这个问题，一种新的技术-DNA微阵列杂交被应用于在基因表达水平上分析染色体异常的后果。使用机器人设备，可以在一张显微镜载玻片上打印代表完整基因组的数千个DNA探针。从任何感兴趣的细胞或组织来源制备的荧光探针然后与这些阵列杂交，提供大规模的基因表达视图。这个项目的最终目标是全基因组表达分析。以这种方式，证明有可能描述单个疾病，并确定给定的基因改变对基因表达的影响。这项技术目前正被应用于携带受移位、激活、突变、扩增或缺失影响的肿瘤特异性基因变化的模型系统，以及具有不同生物学特性的模型，如转移或对激素的反应。然后，将从模型系统获得的信息与从组织样本的表达数据的统计分析中获得的基因表达谱相结合。我们最近的努力已经将这项技术应用于儿童癌症、成人肉瘤、黑色素瘤和乳腺癌。我们已经能够建立芯片在儿童癌症准确诊断和区分雌激素受体阳性乳腺癌和受体阴性肿瘤方面的潜力。利用实验室模型的数据，我们发现了与癌症的重要临床特征相关的基因表达模式，如乳腺癌的雌激素敏感性以及黑色素瘤和骨肉瘤的转移。

One of the major problems in cancer biology is to define the aberrant pattern of gene expression in tumor cells and to relate this pattern to specific genomic alterations which occur during tumorigenesis. To address this issue, a novel technology, DNA microarray hybridization is being applied to analyze the consequences of chromosome anomalies at the level of gene expression. Using a robotic device, it is possible to print thousands of DNA probes representing the complete genome on a single microscope slide. Fluorescent probes prepared from any cell or tissue source of interest are then hybridized to these arrays providing a large scale view of gene expression. The ultimate goal of this project is genome wide expression analysis. In this fashion, it is proving possible to profile individual diseases, and to determine the consequences of a given genetic alteration on gene expression. This technology is now being applied in model systems carrying alterations in tumor specific genes affected by translocation, activation mutation, amplification or deletion, and in models which have distinct biological properties such as metastasis or responsiveness to hormones. Information obtained from model systems is then integrated with gene expression profiles derived from the statistical analysis of expression data from tissue specimens. Our recent efforts have applied this technology to pediatric cancers, adult sarcomas, melanoma and breast cancers. We have been able to establish the potential of microarrays for the accurate diagnosis of pediatric cancers and for distinguishing estrogen receptor positive breast cancers from receptor negative tumors. Using data from laboratory models we have uncovered patterns of gene expressionrelated to important clinical properties of cancers such as estrogen sensitivity in breast cancer and metastasis in melanoma and osteosarcoma.