Discovery and Optimization of AML Prognostic Biomarkers

AML 预后生物标志物的发现和优化

• 批准号: 8625275
• 负责人: DEREK L STIREWALT
• 金额: $ 47.04万
• 依托单位: FRED HUTCHINSON CANCER RESEARCH CENTER
• 依托单位国家: 美国
• 财政年份: 2012
• 资助国家: 美国
• 起止时间: 2012-05-01 至 2017-03-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8625275
• 关键词: Acute Myelocytic Leukemia; Adult; Age; Algorithms; Allogenic; Biological Assay; Biological Markers; Blast Cell; Caring; Cells; Characteristics; Child; Childhood; Clinical; Cytogenetics; Data; Diagnostic; Disease; Disease Resistance; EVI1 gene; Etiology; FLT3 gene; Genomics; Hematologic Neoplasms; Hematopoietic Neoplasms; In complete remission; Knowledge; Lead; Leukemic Cell; Measures; Methods; Modeling; Mononuclear; Multivariate Analysis; Mutation; Myeloproliferative disease; NPM1 gene; Nature; Outcome; Patients; Performance; Performance Status; Population; Predictive Value; Prognostic Factor; Prognostic Marker; Relapse; Research; Risk; Risk Assessment; Risk Factors; Sampling; Staging; Transcript; Transplantation; Triage; base; chemotherapy; comparative; conventional therapy; improved; novel; outcome forecast; patient population; predictive modeling; prognostic; response

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.

描述（由申请人提供）：AML是最常见和致命的造血系统恶性肿瘤之一。目前，需要改善可用的预后生物标志物的预测性质，并开发用于对AML患者进行风险分层的更精确的方法。为了满足这些需求，我们将确定是否可以通过检查AML原始细胞亚群中的这些生物标志物来显著改善生物标志物测定。此外，我们将开发风险评估模型，结合生物标志物结果和其他预后因素，更准确地预测临床结果。具体目标1。确定在AML原始细胞富集群体中测量生物标志物是否显著提高其预测准确性。诊断样本将从儿童（N = 250）和成人（N = 192）AML患者中获得。先前识别的基因组（例如，FLT3等）和转录本（例如，BAALC等）将在来自这些诊断样品的4个细胞群中检查生物标志物：单核细胞（MNC）、总AML母细胞、更分化的AML母细胞和更低分化的AML母细胞。多变量分析，包括其他预后因素的影响，将用于确定与临床结果的独立关联。将使用性能特征的比较分析来确定在AML原始细胞富集群体（包括分化程度较低的AML原始细胞）中测量生物标志物是否显著提高了生物标志物的预测准确性。具体目标2。开发和验证新型风险评估模型，用于预测AML患者的临床结局。使用特定目标1生成的数据，我们将开发以下临床结局的风险评估模型：完全缓解（CR）、耐药疾病（RD）、无复发生存期（RFS）和总生存期（OS）。这些模型将联合收割机结合最具预测性的生物标志物的结果，无论是否在AML原始细胞的富集群体中，与其他风险因素（年龄、细胞遗传学、体能状态等）。我们最初将为每个结局和两个患者群体（即，儿童和成人分开）。然而，我们还将研究是否可以开发一种综合风险评估模型，该模型可以预测儿科和成人人群的多种临床结局（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）患者人群中验证这些预测模型。