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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

基本信息

批准号：
10664892
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
项目类别：
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/10664892
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 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&apos 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 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&apos s lymphoma medical schools molecular subtypes new technology novel novel strategies outcome prediction personalized approach personalized medicine personalized risk prediction radiological imaging response risk prediction model risk stratification targeted sequencing targeted treatment therapy outcome tool treatment response tumor tumor DNA

项目摘要

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和个性化方法肿瘤DNA 患者之间的生物学和临床异质性仍然是改善癌症的固有障碍结果。这种异质性可以预测治疗结果和个性化治疗方法临床肿瘤学的一个重大挑战。弥漫性大 B 细胞淋巴瘤 (DLBCL) 就是一个例子，它是最常见的淋巴瘤。成人常见非霍奇金淋巴瘤。过去 30 年来，我们在以下方面取得了重大进展：揭示这种疾病的患者间异质性，从而识别基于分子亚型基于临床和放射学因素的细胞起源和风险分层工具。近来，该领域基因组学在识别导致个体淋巴瘤的特定基因方面取得了进一步的进展患者，这反过来又引发了个性化医疗的革命。尽管如此，尽管如此尽管进展缓慢，但目前的个性化治疗方法未能改善 DLBCL 患者的预后。在过去的十年中，检测无细胞肿瘤来源的 DNA 或循环肿瘤 DNA 的新方法出现了（ctDNA），已经出现。我们之前应用了深度测序癌症个性化分析 (CAPP- Seq），一种针对 DLBCL 患者的 ctDNA 检测的靶向测序方法。另外，我们最近研究了接受标准治疗的 DLBCL 患者中 ctDNA 的反应动态，定义了稳健的短短 21 天后的分子反应标准。因此，ctDNA 的检测提供了一个机会两者都提高了对肿瘤基因型和表型的理解，包括对治疗的反应，打开了大门个性化的诊断和疾病管理方法。在这个提案中，我将进一步扩展ctDNA 通过 CAPP-Seq 进行检测，开发直接从 DLBCL 中全面分子表征 DLBCL 的方法血浆，包括全基因组拷贝数分析和定相单倍型检测，这是一种独特的方法 B 细胞恶性肿瘤中的实体（目标 1）。然后我将完善并验证一个新颖的框架，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，建立临床上有用的 DLBCL 患者风险个性化模型（目标 2）。最后我会应用这些工具来学习接受嵌合抗原受体（CAR）T-的DLBCL患者的遗传学和分子反应动力学细胞，这是一种针对复发性和难治性 DLBCL 的新兴疗法，仍然是临床需求的领域（目标 3）。该提案将在斯坦福大学医学院的指导下进行阿什·阿里扎德，医学博士/博士。通过完成这个提案，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，成功开展作为独立研究者的职业生涯，专注于为淋巴瘤患者开发和转化新技术。