Molecular Characterization and Personalized Approaches to Non-Hodgkin Lymphoma from Circulating Tumor DNA

从循环肿瘤 DNA 中鉴定非霍奇金淋巴瘤的分子特征和个性化方法

• 批准号: 10217051
• 负责人: David Matthew Kurtz
• 金额: $ 19.13万
• 依托单位: STANFORD UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-08-01 至 2024-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10217051
• 关键词: Adult; Area; B lymphoid malignancy; Biological Assay; Biological Markers; Biology; Blood specimen; CAR T cell therapy; CD19 gene; Cancer Detection; Cancer Patient; Cancer Personalized Profiling by Deep Sequencing; Cells; Clinical; Clinical Oncology; Computational Biology; DNA; DNA Sequence Alteration; Data; Detection; Diagnosis; Diagnostic radiologic examination; Disease; Disease Management; Disease Resistance; Doctor of Philosophy; Early treatment; Event; Genes; Genetic Fingerprintings; Genetic study; Genomics; Genotype; Goals; Haplotypes; Heterogeneity; Immunoglobulin Somatic Hypermutation; Lymphoma; Malignant Neoplasms; Mentorship; Methods; Modeling; Molecular; Mutation; Non-Hodgkin' s Lymphoma; Outcome; PET/CT scan; Patient-Focused Outcomes; Patients; Phase; Phenotype; Plasma; Precision therapeutics; Prediction of Response to Therapy; Public Health; Refractory; Relapse; Research Personnel; Resistance; Risk; Risk Factors; Single Nucleotide Polymorphism; Testing; Time; Translating; Treatment Failure; Treatment outcome; Tumor Biology; Tumor-Derived; Universities; Variant; Work; base; biological heterogeneity; biomedical data science; cancer therapy; career; chimeric antigen receptor; chimeric antigen receptor T cells; clinical heterogeneity; clinically significant; cohort; determinants of treatment resistance; disease heterogeneity; disorder risk; driver mutation; experience; genome-wide; high risk; improved; improved outcome; indexing; individual patient; large cell Diffuse non-Hodgkin' s lymphoma; medical schools; molecular subtypes; new technology; novel; novel strategies; outcome prediction; personalized approach; personalized medicine; personalized risk prediction; response; risk prediction model; risk stratification; targeted sequencing; targeted treatment; therapy outcome; tool; treatment response; tumor; tumor DNA

Molecular characterization and personalized approaches to non-Hodgkin lymphoma from circulating tumor DNA Biological and clinical heterogeneity between patients remain inherent barriers to improving cancer outcomes. This heterogeneity makes prediction of therapeutic outcomes and personalized treatment approaches a major challenge in clinical oncology. This is exemplified by diffuse large B-cell lymphoma (DLBCL), the most common non-Hodgkin lymphoma in adults. Over the last 30 years, significant strides have been made in unraveling the inter-patient heterogeneity of this disease, leading to identification of molecular subtypes based on the cell-of-origin and risk stratification tools based on clinical and radiographic factors. Recently, the field of genomics has made additional strides in identifying the specific genes that drive lymphomas in individual patients, which has in turn led to a revolution in personalized medicine. Nevertheless and despite these advances, current personalized approaches to therapy have failed to improve outcomes for patients with DLBCL. During the last decade, novel methods to detect cell-free tumor-derived DNA, or circulating tumor DNA (ctDNA), have emerged. We previously applied Cancer Personalized Profiling by Deep Sequencing (CAPP- Seq), a targeted sequencing approach for ctDNA detection, to patients with DLBCL. Additionally, we recently explored the response dynamics of ctDNA in DLBCL patients receiving standard therapy, defining robust molecular response criteria after as few as 21 days. Detection of ctDNA therefore provides an opportunity for both improved understanding of tumor genotype and phenotype, including response to therapy, opening the door to personalized approaches to diagnosis and disease management. In this proposal, I will further extend ctDNA detection by CAPP-Seq to develop methods to comprehensively molecularly characterize DLBCL directly from the blood plasma, including genome-wide copy-number profiling and detection of phased haplotypes, a unique entity in B-cell malignancies (Aim 1). I will then refine and validate a novel framework, called the Continuous Individualized Risk Index (CIRI), to integrate genomic information with ctDNA molecular response criteria to build a clinically useful personalized model of risk for DLBCL patients (Aim 2). Finally, I will apply these tools to study the genetics and molecular response dynamics of DLBCL patients receiving chimeric antigen receptor (CAR) T- cells, an emerging therapy for relapsed and refractory DLBCL, which remains an area of clinical need (Aim 3). This proposal will be carried out at the Stanford University School of Medicine, under the mentorship of Ash Alizadeh, MD/PhD. Through completion of this proposal, I will gain the relevant experience in computational biology and biomedical data science to successfully launch a career as an independent investigator focused on developing and translating new technologies for patients with lymphoma.

循环非霍奇金淋巴瘤的分子特征及个体化治疗 肿瘤DNA 患者之间的生物和临床异质性仍然是改善癌症的内在障碍 结果。这种异质性使得对治疗结果和个性化治疗方法的预测 这是临床肿瘤学的一大挑战。这一点以弥漫性大B细胞淋巴瘤(DLBCL)为例， 成人常见的非霍奇金淋巴瘤。在过去的30年里，在 解开这种疾病患者间的异质性，导致识别基于 关于基于临床和放射学因素的起源细胞和风险分层工具。最近，这一领域 基因组学在确定导致个体淋巴瘤的特定基因方面取得了更大进展 患者，这反过来又导致了个性化医疗的革命。然而，尽管如此， 目前的个体化治疗方法未能改善DLBCL患者的预后。 在过去的十年中，检测无细胞肿瘤衍生DNA或循环肿瘤DNA的新方法 (CtDNA)，已经出现了。我们之前应用了深度测序的癌症个性化分析(CAPP- SEQ)，一种检测ctDNA的靶向测序方法，用于DLBCL患者。另外，我们最近 探讨了接受标准治疗的DLBCL患者的ctDNA反应动力学，定义了Robust 分子反应标准在短短21天后。因此，ctDNA的检测提供了一个机会 两者都提高了对肿瘤基因和表型的理解，包括对治疗的反应，打开了大门 到个性化的诊断和疾病管理方法。在这项提议中，我将进一步延长ctDNA CAPP-SEQ检测建立直接从DLBCL中获得DLBCL的分子特征 血浆，包括全基因组拷贝数图谱和阶段性单倍型的检测，是一种独特的 B细胞恶性肿瘤的实体(目标1)。然后，我将改进和验证一个新的框架，称为连续 个体化风险指数(CIRI)，将基因组信息与ctDNA分子反应标准相结合，构建 DLBCL患者的临床有用的个性化风险模型(目标2)。最后，我将把这些工具应用到研究中 DLBCL患者接受嵌合抗原受体(CAR)T细胞的遗传学和分子反应动力学研究 细胞，一种治疗复发和难治性DLBCL的新兴疗法，这仍然是临床需要的一个领域(目标3)。 这项提议将在斯坦福大学医学院进行，由 Ash Alizadeh医学博士/博士通过完成这项提案，我将获得计算方面的相关经验 生物学和生物医学数据科学成功启动职业生涯，成为一名独立调查员，专注于 为淋巴瘤患者开发和翻译新技术。