Tracking Therapy-Resistant Alterations in Childhood Acute Lymphoblastic Leukemia

项目总结/摘要 复发性ALL与不良结局相关，仍然是癌症相关死亡的主要原因， 都是儿童癌症目前的治疗是有毒的，并可能导致高发病率的后期影响，如不孕症 和心力衰竭因此，将新诊断的患者分配到基于 预测复发的风险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，许多患者不 被认为是“高风险”的人仍然会复发。开发新型风险模型的需求尚未得到满足 提高了准确性，能够将患者分配到风险适应性治疗， 未来复发的可能性我们先前对复发性ALL蛋白编码区的基因组学研究（约2%的 人类基因组）已经揭示了对治疗抗性的驱动因素的新见解。虽然这些发现 尽管开发新型分子风险模型的潜力巨大，但仍然存在重大的知识差距。一是超过50% 的复发病例缺乏任何已知的耐药驱动因素。第二，已知的电阻驱动器由下式导出： 复发标本的回顾性研究，目前尚不清楚如何前瞻性地应用这些信息， 初步诊断，以减少复发的可能性。我们的目标是开发新的分子风险模型， 诊断时跟踪电阻驱动器。我们的中心假设是，当检测到阻力驱动因素时， 诊断，将为患者分配到风险适应性治疗提供信息。我们将测试我们的假设， 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确定全面的抗性驱动因素 利用最近完成的一项合作研究中的669例复发性儿童ALL病例的大型队列， 基因组和转录组测序数据可用的临床试验。我们假设未探测的非- 编码区将隐藏新的耐药驱动因素，大的队列规模将使发现耐药基因成为可能。 罕见的阻力驾驶员。在目标2中，我们将通过使用超深的 测序加上最先进的计算误差抑制，这将使检测罕见的 变异频率低至0.01%。在目标3中，我们将研究阻力驱动因素是否预先存在于 诊断时的独立患者队列。我们将开发新的分子风险模型， 复发和复发患者之间初次诊断时检测到的耐药驱动因素的患病率特征 治好的人。成功完成我们的项目目标将提供1）全面的知识， 对治疗耐药的驱动因素，2）诊断时预先存在的全部耐药驱动因素，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 试验和开发新的治疗药物，旨在提高儿童ALL的治愈率。

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.