Discovery and Optimization of AML Prognostic Biomarkers

AML 预后生物标志物的发现和优化

• 批准号: 9024460
• 负责人: DEREK L STIREWALT
• 金额: $ 45.88万
• 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 2018-03-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9024460
• 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 biomarkers; patient population; predict clinical outcome; predicting response; predictive marker; predictive modeling; prognostic significance; 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，最致命的造血恶性肿瘤之一。当前，有必要提高可用预后生物标志物的预测性，并开发更精确的方法来进行风险分层AML患者。为了满足这些需求，我们将通过检查AML爆炸亚群中的这些生物标志物来确定生物标志物测定是否可以显着改善。此外，我们将使用生物标志物结果和其他预后因素的组合开发风险评估模型，这些因素将更准确地预测临床结果。具体目标1。确定测量富集爆炸群中的生物标志物是否会显着提高其预测精度。诊断样品将从小儿（n = 250）和成人（n = 192）患者的患者中获得。将在这些诊断样品的4个细胞群中检查先前认可的基因组（例如，FLT3等）和转录本（例如，BAALC等）生物标志物：单核细胞（MNC），总AML BLAST，总AML BLAST，更具分化的AML爆炸和较少分化的AML AML Blasts。多元分析（结合了其他预后因素的影响）将用于识别与临床结果的独立关联。对性能特征的比较分析将用于确定在富集的AML爆炸群中测量生物标志物（包括较少分化的AML爆炸）是否会显着提高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，以预测AML患者的临床结果。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：完全反应（CR），抗性疾病（RD），无复发生存（RFS）和整体生存（OS）。这些模型将结合最预测性生物标志物的结果，无论是否在富集的AML爆炸群中，以及其他危险因素（年龄，细胞遗传学，性能状态等）。我们最初将针对每个结果独立开发这些模型，并将两个患者种群（即儿童和成人分别）开发。但是，我们还将检查是否可以开发全面的风险评估模型，该模型可以预测儿科和成人人群中多种临床结果（CR，RD，RFS和OS）。然后，我们将在类似治疗的小儿（n = 250）和成人（n = 191）患者种群中验证这些预测模型。