Predicting Relapse at the Time of Diagnosis in Acute Lymphoblastic Leukemia

急性淋巴细胞白血病诊断时预测复发

• 批准号: 10210902
• 负责人: Kara Lynn Davis
• 金额: $ 61.56万
• 依托单位: STANFORD UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-04-01 至 2026-03-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10210902
• 关键词: Acute Lymphocytic Leukemia; Address; Adoption; Automobile Driving; B-Cell Acute Lymphoblastic Leukemia; B-Lymphocytes; Biology; CREB1 gene; Cancer Etiology; Cause of Death; Cell Adhesion; Cells; Child; Childhood Leukemia; Classification; Clinical; Clinical Trials; Complex; DNA Sequence Alteration; Data; Diagnosis; Diagnostic; Disease; Future; Gene Mutation; Genetic; Genomics; Goals; Immunotherapeutic agent; Immunotherapy; Incidence; Individual; Leukemic Cell; Machine Learning; Measurement; Measures; Medicine; Membrane Proteins; Methods; Modeling; Molecular; Mutation; Nature; Oncogenic; Pathway interactions; Patients; Performance; Phenotype; Population; Proteins; Proteomics; Publishing; Recurrence; Recurrent disease; Relapse; Risk; Risk Assessment; SYK gene; Sampling; Signal Transduction; Surface; Therapeutic; Time; Work; base; chemotherapy; clinical implementation; clinical risk; cohort; differential expression; exome sequencing; improved; individual patient; innovation; leukemia; mortality; multimodality; mutational status; novel; precision medicine; predictive modeling; prevent; prospective; prospective test; prototype; relapse prediction; relapse risk; response; risk prediction; risk prediction model; standard of care; targeted treatment; therapeutic target; treatment strategy

PROJECT SUMMARY Relapse is the major cause of cancer related mortality in children with leukemia. Despite improvements in overall survival for children with B-cell progenitor acute lymphoblastic leukemia (ALL), for the 600 patients who will relapse each year, half will die of their disease. The high mortality of patients who relapse underscores the need for improved risk prediction and treatment strategies to prevent recurrent leukemia. Current approaches to relapse prediction are limited by insufficient accuracy, delayed prediction and the inability to make actionable treatment adjustments based on prediction information. To address these limitations, we applied a single-cell, high-parameter proteomic approach to ALL patient samples at the time of diagnosis, accurately predicting future relapse based on the presence of pre-B cells with activated signaling. This approach was 38% more accurate than standard of care relapse prediction methods. We propose that identifying relapse-predictive cells in ALL at the time of diagnosis using their distinguishing proteomic and genetic features will result in a clinical risk prediction model that is accurate, immediate, and actionable. This approach to relapse prediction will change the clinical paradigm of relapse risk in ALL to reduce the incidence of relapse itself. Using large multi-institutional, multimodal cohorts of molecularly and clinically annotated diagnostic patient samples, we will apply deep proteomic approaches to identify surface proteins uniquely expressed on relapse predictive pre-B cells enabling direct identification in a diagnosis sample. We will determine how genomic mutations associate with the presence of relapse predictive cells and examine their genomic mutational burden using single-cell exome sequencing. Finally, building on our data-driven, machine learning approaches, we will construct a diagnostic relapse predictor that is more accurate than standard of care models while informing on leukemia biology and targeted therapeutic options for patients at risk. This will enable a more precise approach to patient classification and treatment, reducing the number of children facing relapse and moving closer to precision medicine for children with ALL.

项目概要 复发是白血病儿童癌症相关死亡的主要原因。尽管有所改进 600 名 B 细胞祖细胞急性淋巴细胞白血病 (ALL) 儿童的总生存率 每年复发的人中，有一半会死于该病。复发患者的高死亡率凸显了 需要改进风险预测和治疗策略以预防复发性白血病。当前的 复发预测方法受到准确性不足、预测延迟和无法 根据预测信息做出可行的治疗调整。为了解决这些限制，我们 在诊断时对所有患者样本应用单细胞、高参数蛋白质组学方法， 根据具有激活信号传导的前 B 细胞的存在，准确预测未来的复发。这 该方法比标准护理复发预测方法准确 38%。我们建议 在诊断时使用其独特的蛋白质组学识别 ALL 中的复发预测细胞 和遗传特征将产生准确、即时和可靠的临床风险预测模型 可行的。这种复发预测方法将改变 ALL 复发风险的临床范式 减少复发本身的发生率。 使用大型多机构、多模式的分子和临床注释诊断队列 患者样本，我们将应用深层蛋白质组学方法来识别独特表达的表面蛋白 复发预测前 B 细胞能够在诊断样本中直接识别。我们将决定如何 基因组突变与复发预测细胞的存在相关并检查其基因组 使用单细胞外显子组测序测定突变负荷。最后，以我们的数据驱动的机器学习为基础 方法，我们将构建一个比标准护理更准确的诊断复发预测器 模型，同时提供有关白血病生物学和针对高危患者的针对性治疗方案的信息。这将 实现更精确的患者分类和治疗方法，减少面临的儿童数量 复发并更接近针对 ALL 儿童的精准医疗。