GENE EXPRESSION PROFILE OF PROGRESSION & RESPONSE IN CML

慢性粒细胞白血病（CML）是一种造血干细胞疾病，具有独特的生物学和临床特征，表现为相对临床良性状态（慢性期），其不可避免地演变为不可治愈的侵袭性疾病（急变）。 治疗的范围可以从低强度化疗到治愈性但可能致命的骨髓移植（BMT）治疗。 不幸的是，人们对引发慢性期演变为爆发性危机的分子事件知之甚少。 因此，根据个体患者的风险定制治疗是不可能的。 该提案旨在确定在慢性期到blase危机的演变中发生的基因表达变化，以及发现与传统干扰素治疗的良好结果相关的基因表达模式。 具体而言，我们将：1）优化和验证表达阵列技术，然后2）使用mRNA表达阵列来鉴定参与慢性期至急变期CML进展的基因;和3）鉴定与常规基于干扰素的治疗后的良好或不良结果相关的基因。 这些研究将使我们开始研究CML转化的生物学，并在遗传水平上理解为什么有些患者对常规治疗有反应，而其他患者对治疗无效，并迅速转化为高度侵袭性疾病。 低风险和高风险患者的识别将使治疗适合每个人的疾病。 此外，在该模型系统中应用大规模表达分析将是解决不可预见的技术问题的理想方法，因此所获得的经验在未来研究其他更复杂的肿瘤系统中可能非常有价值。

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.