(PDQ5)Integrated Genetic and Epigenetic Prognostication for Acute Myeloid Leukemi

描述（由申请人提供）：目前急性髓性白血病（AML）的风险分层是基于临床变量，如年龄和传统细胞遗传学。最近，分子遗传学分析已被证明在AML患者的风险分层和治疗管理中是有用的。例如，发现FLT3、NPM1和CEBPA突变分析可以改善无核型异常患者的风险分层。然而，目前所有的预后方案都不能将化疗治愈的患者与复发的患者强有力地区分开来。为了解决这个问题，我们进行了研究，利用遗传和表观遗传标记来增强分子预测，重要的是，使这些检测在临床上可行。Levine实验室最近完成了对一大群新发AML患者中16个AML突变基因的突变分析，并确定了新的预后预测因子，包括TET2、PHF6、DNMT3A和IDH2 R140Q突变。重要的是，这些遗传变量的组合为许多AML患者提供了增强的预后能力，导致这些亚组的生存率预测超过80%。在其他情况下，遗传变量的组合只能提供适度的预后区分。此外，Melnick实验室最近通过全基因组DNA甲基化分析证明，AML患者对治疗的反应可以通过15个基因DNA甲基化小组来预测。该小组是使用全基因组甲基化分析平台确定的，目前不适用于临床环境。综上所述，这些研究导致了AML是一种以遗传和表观遗传变化为特征的疾病的假设，并且最佳的预测模式需要结合这两种类型的标记物。为了使DNA甲基化分析在临床实践中的预后应用可行，我们开发了一种基于微球的甲基化分析（MELP）测定方法，该方法使用了分子病理学实验室广泛使用的技术（详见下文）。初步评估表明，该检测可靠地再现了原始HELP（通过连接介导的PCR富集HpaII微小片段）研究甲基化平台产生的结果，并预测了先前描述的AML队列（HOVON）的结果。此外，我们已经实施了下一代基于DNA测序的方法来确定AML中的基因突变，并将该方法应用于最近注释的UPENN AML组（如下所述）。在本项目中，我们将进一步开发一种结合遗传、表观遗传和临床特征的强大的、临床可行的AML预测系统。我们将首先专注于确认一个强大的基于DNA甲基化的临床分析，然后将该分析整合到正在开发的多变量风险评估系统中。后一种系统应作为一个平台，为全面整合的预后系统的持续发展，我们朝着实现个性化癌症诊断的目标迈进。

DESCRIPTION (provided by applicant): Current risk stratification in acute myeloid leukemia (AML) is based on clinical variables such as age and traditional cytogenetics. Recently, molecular genetic analysis has proven to be useful in risk stratification and therapeutic management of AML patients. For example, FLT3, NPM1, and CEBPA mutational analysis was found to improve risk stratification in patients without karyotypic abnormalities. However, all current prognostication schema fail to robustly separate patients who will be cured by chemotherapy from those who will relapse. To address this problem, we have performed studies to enhance molecular prognostication using both genetic and epigenetic markers and, importantly, to make these assays clinically feasible. The Levine Laboratory recently completed mutational analysis of 16 genes mutated in AML in a large cohort of patients with de novo AML and identified novel predictors of outcome, including TET2, PHF6, DNMT3A, and IDH2 R140Q mutations. Importantly, combinations of these genetic variables provide enhanced prognostic ability for many AML patients, leading to predictions of survival of greater than 80% in these sub-groups. In other cases, combinations of genetic variables provide only modest discrimination of prognosis. Additionally, the Melnick laboratory has recently demonstrated through genome-wide DNA methylation analysis that response to therapy for AML patients can be predicted with a fifteen-gene DNA methylation panel. This panel was identified using a genome-wide methylation profiling platform not currently amenable to use in the clinical setting. Taken together, these studies lead to the hypothesis that AML is a disease characterized by both genetic and epigenetic changes and that optimal prognostication schema will need to incorporate both types of markers. To make the prognostic use of DNA methylation profiling feasible in clinical practice, we have developed a microsphere-based methylation profiling (MELP) assay which uses technology widely available in molecular pathology laboratories (see below for details). Initial evaluation demonstrates that the assay robustly reproduces results generated with the original HELP (HpaII tiny fragment enrichment by ligation-mediated PCR) research-based methylation platforms and predicts outcome in a previously described AML cohort (HOVON). In addition, we have implemented a next generation based DNA sequencing methodology to determine genetic mutations in AML and applied this methodology to a recently annotated UPENN AML group (described below). In this proposal, we will further develop a robust, clinically feasible, AML prognostication system combining genetic, epigenetic and clinical features. We will initially focus on confirming a robust DNA methylation based clinical assay and then integrate this assay into a multivariate risk assessment system being developed. The latter system should serve as a platform for ongoing development of a fully integrated prognostic system as we move towards realization of the goal of personalized cancer diagnostics.