APPLYING ERROR-CORRECTED SEQUENCING TO DETECT MINIMAL RESIDUAL IN THE AAML 1031 STUDY

应用纠错测序来检测 AAML 1031 研究中的最小残留

• 批准号: 9216965
• 负责人: Todd E Druley
• 金额: $ 38.86万
• 依托单位: WASHINGTON UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2016
• 资助国家: 美国
• 起止时间: 2016-12-09 至 2021-11-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9216965
• 关键词: Acute Myelocytic Leukemia; Adult; Adult Acute Myeloblastic Leukemia; Algorithms; Alleles; BAY 54-9085; Biological Markers; Bone Marrow; Bortezomib; Categories; Cessation of life; Childhood; Childhood Acute Myeloid Leukemia; Childhood Leukemia; Clinical; Clinical Data; Collaborations; Complex; Cytogenetics; DNA; DNA Sequence Alteration; DNA sequencing; Data; Detection; Diagnosis; Diagnostic; Disease; Disease Marker; Disease-Free Survival; Enrollment; Epigenetic Process; Exons; FLT3 gene; Family; Flow Cytometry; Frequencies; Future; Gene Targeting; Genes; Genetic; Goals; Gold; Immunophenotyping; Individual; Investigation; Machine Learning; Measures; Methodology; Methods; Monitor; Mutate; Mutation; Normal Cell; North America; Oncogenes; Outcome; Participant; Patients; Pattern; Pediatric Oncology Group; Phase; Point Mutation; Population; Probability; Prospective Studies; Protein Tyrosine Kinase; Protein phosphatase; Protocols documentation; Reagent; Recurrent disease; Refractory Disease; Relapse; Residual Neoplasm; Residual Tumors; Residual state; Resolution; Risk; Risk Assessment; Risk stratification; Role; Sampling; Site; Spliceosomes; Surface; Testing; Time; Treatment Efficacy; Tumor Suppressor Proteins; United States National Institutes of Health; Variant; analytical method; base; chemotherapy; cohesion; design; digital; falls; functional group; gene function; improved; improved outcome; insertion/deletion mutation; insight; leukemia; leukemia treatment; next generation; next generation sequencing; novel; nucleocytoplasmic transport; outcome prediction; pediatric patients; personalized cancer care; personalized medicine; prognostic; prospective; randomized trial; relapse risk; response; specific biomarkers; subclonal heterogeneity; transcription factor

Abstract Acute myeloid leukemia (AML) accounts for half of all pediatric leukemia deaths and is the leading cause of leukemia-related death in adulthood. One reason for worse outcomes is the inability to properly assess for minimal residual disease (MRD) following therapy. Unlike ALL, AML presents with multiple subclonal populations without a singular clonal surface marker, and surface markers can change during therapy. The current gold standard for AML MRD is multi-parameter flow cytometry (MPFC), which is predictive of outcomes to frequencies of 0.001, yet 30% of MPFC-MRD-negative patients still relapse. Alternatively, every AML case harbors leukemia-specific mutations that could be markers of disease, except that next-generation sequencing has high error rate of ~1%. In this proposal, we will implement a novel, validated error-corrected sequencing (ECS) strategy, developed by the Druley lab in collaboration with Illumina, to improve MRD assessment of AML subclonal heterogeneity in 990 pediatric de novo AML cases from the Children's Oncology Group AAML1031 study. We hypothesize that using a highly sensitive sequencing method will improve identification of residual AML, provide important insights on subclonal heterogeneity in pediatric AML, improve understanding of the role of germline variability and gene function on relapses or refractory disease and facilitate personalized medicine. To interrogate this hypothesis, we propose the following aims: 1. Define subclonal heterogeneity at diagnosis and end of Induction 1 (EOI1) in 990 pediatric de novo AML patients (n=1890). By using the largest prospective study of pediatric AML that has ever been performed, we will perform ECS on 94 genes that are the most frequently mutated genes in pediatric and adult AML at diagnosis and EOI1 to identify patterns of mutation associated with relapsed disease, FAB subtypes or other cytogenetic features. 2. Correlate ECS-MRD with existing EOI1 MPFC-MRD for all participants in the COG AAML1031 study. A major question is whether the “different from normal” cell population identified as residual disease by MPFC is actually the same population(s) identified by ECS. We will define residual disease by ECS and compare results to MPFC status (positive/negative), actual MPFC percentages (<0.001) and the clinical outcomes (relapse risk, disease-free survival and overall survival) of study participants. 3. Integrate germline variation and all subclonal mutations into mechanistic groups that are frequently mutated in pediatric AML and correlate with outcomes using unbiased machine learning algorithms. Preliminary data tells us that every patient will have multiple subclones at diagnosis and EOI1 as well as germline variants in AML-associated genes, which may be important for outcome. In this aim, we will take these mutations into account as well as MPFC, clinical features and cytogenetics for probabilistic risk assessment using unsupervised machine learning algorithms for improved outcome prognostication.

抽象的 急性髓系白血病 (AML) 占所有儿童白血病死亡人数的一半，是导致儿童白血病死亡的主要原因 成年期白血病相关死亡的原因。导致更糟糕结果的原因之一是无法正确地 评估治疗后的微小残留病 (MRD)。与 ALL 不同，AML 存在多种症状 没有单一克隆表面标记的亚克隆群体，并且表面标记可以在 治疗。目前 AML MRD 的黄金标准是多参数流式细胞术 (MPFC)，它具有预测性 结果频率为 0.001，但 30% 的 MPFC-MRD 阴性患者仍然复发。或者，每个 AML 病例含有白血病特异性突变，这些突变可能是疾病的标志，但下一代 测序错误率高达~1%。在本提案中，我们将实现一种新颖的、经过验证的纠错方法 测序 (ECS) 策略，由 Druley 实验室与 Illumina 合作开发，用于改善 MRD 儿童肿瘤科 990 例儿童新发 AML 病例的 AML 亚克隆异质性评估 AAML1031 小组研究。我们假设使用高度灵敏的测序方法将改善 识别残留的 AML，为儿科 AML 的亚克隆异质性提供重要见解，提高 了解种系变异和基因功能对复发或难治性疾病的作用，以及 促进个性化医疗。为了质疑这一假设，我们提出以下目标： 1. 定义 990 名儿科新生患者在诊断和诱导结束时的亚克隆异质性 1 (EOI1) AML 患者 (n=1890)。通过使用有史以来最大规模的儿科 AML 前瞻性研究 执行后，我们将对 94 个基因进行 ECS，这些基因是儿科和儿童中最常见的突变基因。 成人 AML 诊断和 EOI1 以确定与复发性疾病相关的突变模式，FAB 亚型或其他细胞遗传学特征。 2. 将 COG AAML1031 研究中所有参与者的 ECS-MRD 与现有 EOI1 MPFC-MRD 相关联。 一个主要问题是，“与正常不同”的细胞群是否被识别为残留疾病？ MPFC 实际上与 ECS 识别的人群相同。我们将通过 ECS 来定义残留病灶 将结果与 MPFC 状态（阳性/阴性）、实际 MPFC 百分比 (<0.001) 和临床结果进行比较 研究参与者的结果（复发风险、无病生存期和总体生存期）。 3. 将种系变异和所有亚克隆突变整合到经常出现的机制组中 在儿科 AML 中发生突变，并使用无偏见的机器学习与结果相关 算法。初步数据告诉我们，每个患者在诊断和 EOI1 时都会有多个亚克隆 以及 AML 相关基因的种系变异，这可能对结果很重要。为了这个目标，我们 将考虑这些突变以及 MPFC、临床特征和细胞遗传学的概率 使用无监督机器学习算法进行风险评估，以改善结果预测。