Predicting Relapse at the Time of Diagnosis in Acute Lymphoblastic Leukemia

项目摘要 复发是儿童白血病癌症相关死亡的主要原因。尽管有所改善 在600名B细胞祖细胞急性淋巴细胞白血病（ALL）患儿中， 每年复发的病人中有一半会死于这种疾病复发患者的高死亡率强调了 需要改进风险预测和治疗策略，以预防白血病复发。电流 复发预测的方法受到准确性不足、预测延迟和无法 基于预测信息进行可操作的治疗调整。为了克服这些局限性，我们 在诊断时对ALL患者样本应用单细胞、高参数蛋白质组学方法， 基于具有激活信号的前B细胞的存在准确预测未来复发。这 该方法比标准治疗复发预测方法准确38%。我们建议 在诊断时使用其区分蛋白质组学鉴定ALL中的复发预测细胞 和遗传特征将导致临床风险预测模型是准确的，即时的， 可起诉的这种复发预测方法将改变ALL复发风险的临床模式， 减少复发的发生率。 使用大型多机构，多模式队列的分子和临床注释的诊断 患者样本，我们将应用深层蛋白质组学方法来识别表面蛋白的独特表达， 复发预测性前B细胞能够在诊断样品中直接鉴定。我们将决定如何 基因组突变与复发预测细胞的存在相关， 使用单细胞外显子组测序的突变负荷。最后，基于我们的数据驱动的机器学习 方法，我们将构建一个比标准治疗更准确的诊断复发预测因子 模型，同时为有风险的患者提供白血病生物学和有针对性的治疗选择。这将 能够更精确地对患者进行分类和治疗， 复发和更接近精确的药物治疗儿童急性淋巴细胞白血病。

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.