Tracking Therapy-Resistant Alterations in Childhood Acute Lymphoblastic Leukemia

追踪儿童急性淋巴细胞白血病的治疗耐药性改变

• 批准号: 10504566
• 负责人: Xiaotu Ma
• 金额: $ 44.43万
• 依托单位: ST. JUDE CHILDREN'S RESEARCH HOSPITAL
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-08-01 至 2027-07-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10504566
• 关键词: Acute Lymphocytic Leukemia; Address; Affect; Algorithms; Biological Assay; Blood; Cancer Etiology; Cells; Cessation of life; Child; Childhood Acute Lymphocytic Leukemia; Clinical Research; Clinical Trials; Clinical Trials Cooperative Group; Clustered Regularly Interspaced Short Palindromic Repeats; Code; Collaborations; Coupled; Cytotoxic Chemotherapy; Data; Detection; Diagnosis; Disease remission; Dominant Genetic Conditions; Ensure; Event; Frequencies; Future; Genome; Genomics; Goals; Hand; Heart failure; Human Genome; Immunotherapy; Incidence; Infertility; Knowledge; Late Effects; Malignant Childhood Neoplasm; Measures; Methods; Modeling; Molecular; Newly Diagnosed; Open Reading Frames; Outcome; Patients; Pattern; Pediatric Oncology Group; Prevalence; Proteins; Relapse; Residual Cancers; Residual Neoplasm; Resistance; Resistance profile; Resources; Retrospective Studies; Risk; Saint Jude Children' s Research Hospital; Secure; Specimen; Survival Rate; Technology; Testing; Therapeutic Agents; Time; Untranslated RNA; Variant; base; cancer cell; chemotherapy; cohort; deep sequencing; design; efficacy testing; experience; genome sequencing; high risk; improved; improved outcome; innovation; insight; leukemia relapse; novel; novel therapeutics; prognostic; prospective; rare variant; relapse patients; relapse risk; risk minimization; risk stratification; single cell sequencing; therapy resistant; transcriptome sequencing; treatment response; tumor

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.

项目概要/摘要 复发性 ALL 与不良预后相关，并且仍然是癌症相关死亡的主要原因 所有儿童癌症。目前的疗法具有毒性，可能导致不孕等晚期效应的高发生率 和心力衰竭。因此，将新诊断的患者分配到基于治疗的治疗已成为标准做法 预测复发风险。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，很多患者并没有 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 提高准确性，使患者能够分配到适合风险的治疗方案，以减少 未来复发的可能性。我们之前对蛋白质编码区复发 ALL 的基因组学研究（约 2%） 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 开发新型分子风险模型的潜力，但仍然存在巨大的知识差距。第一，50%以上 的复发病例缺乏任何已知的耐药驱动因素。其次，已知的电阻驱动器源自 对复发标本进行回顾性研究，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颖的分子风险模型 在诊断时跟踪电阻驱动因素。我们的中心假设是，当检测到阻力驱动因素时 诊断将为患者分配风险适应疗法提供信息。我们将检验我们的假设 三个目标。在目标 1 中，我们将从蛋白质编码和非编码中识别全面的耐药驱动因素 通过利用来自最近完成的合作社的 669 例复发儿童 ALL 病例的大队列研究 具有可用基因组和转录组测序数据的临床试验。我们假设未经探索的非 编码区域将蕴藏新的耐药驱动因素，并且大的队列规模将有助于发现 罕见的阻力驱动因素。在目标 2 中，我们将通过使用超深度回溯诊断时的阻力驱动因素 测序与最先进的计算错误抑制相结合，将能够检测罕见的 频率低至 0.01% 的变体。在目标 3 中，我们将调查阻力驱动因素是否预先存在于 诊断时的独立患者队列。我们将通过比较开发新的分子风险模型 初次诊断时检测到的耐药驱动因素在复发和复发患者之间的流行情况 那些被治愈的人。成功完成我们的项目目标将提供 1) 的全面知识 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) 新颖 降低复发风险的分子风险模型。我们的成果将构成未来临床的基础 试验和开发旨在提高儿童 ALL 治愈率的新型治疗药物。