Discovery and Optimization of AML Prognostic Biomarkers

描述(申请人提供)：AML是最常见和致命的血液系统恶性肿瘤之一。目前，有必要改善现有预后生物标志物的预测特性，并开发更精确的方法来对AML患者进行风险分层。为了与这些需求保持一致，我们将确定是否可以通过检测AML原始细胞亚群中的这些生物标记物来显著改进生物标记物分析。此外，我们将使用生物标记物结果和其他预后因素的组合来开发风险评估模型，以更准确地预测临床结果。具体目标1.确定测量AML原始细胞丰富群体中的生物标记物是否显著提高了它们的预测准确性。诊断样本将来自儿童(N=250)和成人(N=192)急性髓系白血病患者。先前识别的基因组(例如，Flt3等)和成绩单(例如，BAALC等)生物标志物将在这些诊断样本的4个细胞群中进行检测：单个核细胞(MNC)、总AML母细胞、分化程度较高的AML母细胞和分化程度较低的AML母细胞。多变量分析，结合其他预后因素的影响，将被用于确定与临床结果的独立关联。性能特征的比较分析将被用来确定在丰富的AML原始细胞群中测量生物标记物是否显著提高了生物标记物的预测准确性，包括分化较少的AML原始细胞。具体目标2.开发和验证用于预测AML患者临床结果的新的风险评估模型。使用特定目标1产生的数据，我们将开发以下临床结果的风险评估模型：完全缓解(CR)、耐药疾病(RD)、无复发生存率(RFS)和总生存率(OS)。这些模型将把最具预测性的生物标志物的结果与其他风险因素(年龄、细胞遗传学、表现状态等)结合起来，无论是在AML原始细胞丰富的人群中还是在非AML原始细胞群体中。我们最初将为每个结局和两个患者群体(即分别为儿童和成人)独立开发这些模型。然而，我们还将检查是否可以开发一个全面的风险评估模型，该模型可以预测儿童和成人人群的多种临床结果(CR、RD、RFS和OS)。然后，我们将在接受类似治疗的儿童(N=250)和成人(N=191)患者群体中验证这些预测模型。 公共卫生相关性：急性髓细胞白血病患者在治疗后经常复发。拟议的研究可能会确定显著提高我们预测治疗反应的能力的方法，并导致治疗AML的更有针对性的方法。

DESCRIPTION (provided by applicant): AML is one of the most common and lethal hematopoietic malignancies. Currently, there is a need to improve the predictive nature of available prognostic biomarkers and to develop more precise methods for risk- stratifying AML patients. In keeping with these needs, we will determine whether biomarker assays can be significantly improved by examining these biomarkers in subpopulations of AML blasts. Furthermore, we will develop risk-assessment models using a combination of biomarker results and other prognostic factors that will more accurately predict clinical outcomes. Specific Aim 1. Determine if measuring biomarkers in enriched populations of AML blasts significantly improves their predictive accuracy. Diagnostic samples will be obtained from pediatric (N = 250) and adult (N = 192) patients with AML. Previously recognized genomic (e.g., FLT3, etc.) and transcript(e.g., BAALC, etc.) biomarkers will be examined in 4 cell populations from these diagnostic samples: mononuclear cells (MNCs), total AML blasts, more differentiated AML blasts, and less differentiated AML blasts. Multivariate analyses, incorporating the effects of other prognostic factors, will be used to identify independent associations with clinical outcomes. Comparative analyses of performance characteristics will be used to determine if measuring the biomarker in enriched populations of AML blasts, including the less differentiated AML blasts, significantly improves the predictive accuracy of the biomarker. Specific Aim 2. Develop and validate novel risk-assessment models for predicting clinical outcomes for AML patients. Using the data generated from Specific Aim 1, we will develop risk-assessment models for the following clinical outcomes: complete response (CR), resistant disease (RD), relapse-free survival (RFS), and overall survival (OS). These models will combine the results from the most predictive biomarkers, whether in enriched populations of AML blasts or not, with other risk factors (age, cytogenetics, performance status, etc.). We will initially develop these models independently for each outcome and the two populations of patients (i.e., children and adult, separately). However, we will also examine whether a comprehensive risk- assessment model can be developed, which predicts for multiple clinical outcomes (CR, RD, RFS, and OS) across both pediatric and adult populations. We will then validate these predictive models in similarly treated populations of pediatric (N = 250) and adult (N = 191) patients. PUBLIC HEALTH RELEVANCE: Patients with AML frequently relapse after therapy. The proposed studies may identify methods to dramatically improve our ability to predict responses to therapy and lead to more targeted approaches for treating AML.