Tracking Therapy-Resistant Alterations in Childhood Acute Lymphoblastic Leukemia

项目摘要/摘要 复发的ALL与不良预后有关，仍然是与癌症相关的死亡的主要原因 都是儿童期癌症。目前的治疗方法是有毒的，并可能导致不孕不育等后遗症的高发生率。 和心力衰竭。因此，将新诊断的患者分配到基于 关于复发风险的预测。诱导化疗后残留癌细胞的存在，称为 微小残留病(MRD)是一个非常重要的预后变量。然而，许多患者并不是 被认为是“高风险”的患者仍会复发。开发新的风险模型的需求尚未得到满足 提高了准确性，使患者能够分配到风险适应的治疗，以减少 未来复发的可能性。我们之前的基因组学研究表明，复发的ALL位于蛋白质编码区(~2%的 人类基因组)揭示了对治疗耐药的驱动因素的新见解。虽然这些发现已经 尽管存在开发新的分子风险模型的潜力，但仍然存在重大的知识差距。第一，超过50% 的复发病例缺乏任何已知的耐药性驱动因素。第二，已知的电阻驱动器是从 对复发标本的回溯性研究，目前尚不清楚如何从 初步诊断，以降低复发的可能性。我们的目标是通过以下方式开发新的分子风险模型 在诊断时跟踪电阻驱动器。我们的中心假设是，当检测到抗药性时 诊断，将为分配患者接受风险适应治疗提供信息。我们将在下面测试我们的假设 三个目标。在目标1中，我们将从蛋白质编码和非编码两个方面识别全面的抗性驱动因素 通过利用最近完成的一个合作社的669例儿童复发病例的大队列，对各地区进行了分析 可获得基因组和转录组测序数据的临床试验。我们假设，未开发的非 编码区将包含新的抗性驱动因素，并且大的队列大小将使发现 罕见的阻力驱动因素。在目标2中，我们将在诊断时使用超深度回溯阻力驱动器 测序与最先进的计算误差抑制相结合，将能够检测罕见的 变异频率低至0.01%。在目标3中，我们将调查阻力驱动因素是否预先存在于 诊断时患者的独立队列。我们将通过比较来开发新的分子风险模型 初诊时发现的耐药驱动因素在复发患者和 那些被治愈的人。成功完成我们的项目目标将提供1)全面的知识 治疗耐药的驱动因素，2)诊断时预先存在的耐药驱动因素的全谱，以及3)新的 降低复发风险的分子风险模型。我们的交付成果将成为未来临床工作的基础 旨在提高儿童急性淋巴细胞白血病治愈率的试验和开发新的治疗药物。

PROJECT SUMMARY/ABSTRACT Relapsed ALL is associated with poor outcome and remains the leading cause of cancer-related death among all childhood cancer. Current therapies are toxic and can result in high incidence of late effects such as infertility and heart failure. Thus, it has been a standard practice to allocate newly diagnosed patients to therapies based on predicted risk of relapse. The presence of residual cancer cells after induction chemotherapy, known as minimal residual disease (MRD), is a highly significant prognostic variable. However, many patients not considered to be “high risk” still experience relapse. There is an unmet need to develop novel risk models with enhanced accuracy to enable allocation of patients to risk-adapted therapies to reduce the likelihood of future relapse. Our prior genomics studies on relapsed ALL in protein-coding regions (~2% of the human genome) have revealed novel insights on the drivers of resistance to therapy. While these findings have potential for developing novel molecular risk models, significant knowledge gaps remain. First, more than 50% of relapsed cases lack any known resistance drivers. Second, the known resistance drivers are derived from retrospective studies of relapsed specimens and it is unclear how to apply such information prospectively from initial diagnosis to decrease the likelihood of relapse. Our goal is to develop novel molecular risk models by tracking resistance drivers at diagnosis. Our central hypothesis is that resistance drivers, when detected at diagnosis, will be informative for allocation of patients to risk-adapted therapies. We will test our hypothesis in three aims. In Aim 1, we will identify comprehensive resistance drivers from both protein-coding and non-coding regions by leveraging a large cohort of 669 relapsed childhood ALL cases from a recently completed cooperative clinical trial with genome and transcriptome sequencing data available. We hypothesize that unexplored non- coding regions will harbor novel resistance drivers and that the large cohort size will empower the discovery of rare resistance drivers. In Aim 2, we will backtrack the resistance drivers at diagnosis by using ultra-deep sequencing coupled with state-of-the-art computational error suppression that will enable detection of rare variants with frequency as low as 0.01%. In Aim 3, we will investigate if resistance drivers pre-exist in an independent cohort of patients at diagnosis. We will develop novel molecular risk models by comparing prevalence profiles of resistance drivers detected at initial diagnosis between patients who have relapsed and those who are cured. Successful completion of our project aims will deliver 1) comprehensive knowledge of drivers of resistance to therapy, 2) the full spectrum of pre-existing resistance drivers at diagnosis, and 3) novel molecular risk models for decreasing the risk of relapse. Our deliverables will form the basis for future clinical trials and for developing novel therapeutic agents aimed at improving the cure rate of childhood ALL.