GENE EXPRESSION PROFILE OF PROGRESSION & RESPONSE IN CML

慢性髓系白血病(CML)是一种具有不同生物学和临床特征的造血干细胞疾病，临床表现为相对良性的状态(慢性期)，不可避免地演变为不可治愈的侵袭性疾病(急变期)。治疗的范围从低强度化疗到骨髓移植(BMT)的治愈但潜在致命的治疗。不幸的是，人们对触发慢性期到爆发性危象演变的分子事件知之甚少。因此，针对个别患者S的风险量身定做治疗是不可能的。这项建议旨在确定在慢性期到危象演变过程中发生的基因表达变化，以及发现与传统干扰素治疗相关的基因表达模式。具体地说，我们将：1)优化和验证表达阵列技术，然后2)使用mRNA表达阵列来识别与慢性期到急变期CML进展相关的基因；以及3)识别与传统干扰素治疗效果好或差相关的基因。这些研究将使我们能够开始研究CML转化的生物学，并在基因水平上了解为什么一些患者对常规治疗有效，而另一些患者对治疗无效，并迅速转化为高度侵袭性疾病。低v高危患者的识别将使治疗能够针对每个S病的个体进行适当的量身定制。此外，大规模表达分析在这个模型系统中的应用将是解决不可预见的技术问题的理想选择，因此所获得的经验可能对未来研究其他更复杂的肿瘤系统非常有价值。

Chronic myeloid leukemia (CML) is a hematopoetic stem cell disease with distinct biological and clinical features, presenting as a relatively clinically benign state ( chronic phase ), which invariably evolves to an incurable aggressive disease ( blast crisis ). Treatment can range from low intensity chemotherapy to the curative yet potentially lethal therapy of bone marrow transplantation (BMT). Unfortunately little is known about the molecular events that trigger the evolution of chronic phase to blast crisis. Thus, tailoring therapy to individual patient s risk is impossible. This proposal aims to identify changes in gene expression that occur in the evolution of the chronic phase to blase crisis, as well as discovering gene expression patterns that are associated with good outcomes to conventional interferon-based therapy. Specifically, we will: 1) optimize and validate the expression array technology, then 2) use mRNA expression arrays to identify genes involved in the progression of chronic phase to blast phase CML; and 3) identify genes associated with good or poor outcome following conventional interferon-based therapy. These studies will allow us to begin to study the biology of CML transformation, and understand at a genetic level why some patients respond to conventional therapy, while other patients are refractory to therapy, and quickly transform to highly aggressive disease. The identification of low v high risk patients will allow theraphy to be appropriately tailored to each individual s disease. In addition, the application of large-scale expression analysis in this model system will be ideal to iron out unforeseen technical problems, and thus the experience gained may be very valuable in future investigations other more complex tumor systems.