A Genomic Framework for Molecular Risk Prediction & Individualized Lymphoma Therapy

分子风险预测的基因组框架

• 批准号: 10454960
• 负责人: Ash Arash Alizadeh
• 金额: $ 55.07万
• 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/10454960
• 关键词: Address; Antibody-drug conjugates; Assessment tool; BCL2 gene; Biological Assay; Blood; Blood specimen; CD19 gene; Cancer Center; Cancer Patient; Clinical; Clinical Trials; DNA Sequence Alteration; Data; Detection; Development; Diagnosis; Disease; Doctor of Medicine; Doctor of Philosophy; Drops; Early Diagnosis; Early treatment; Europe; Evaluation; FDA approved; Failure; Foundations; Functional Imaging; Future; Genomic approach; Genomics; Genotype; Goals; Hematologic Neoplasms; Image; Immuno-Chemotherapy; Individual; International; Lymphoma; Measures; Metabolic; Methods; Modeling; Molecular; Monitor; Newly Diagnosed; North America; Outcome; Patients; Prediction of Response to Therapy; Prognostic Factor; Public Health; Randomized; Recurrence; Refractory; Relapse; Residual Tumors; Retrospective cohort; Risk; Risk Assessment; Risk Factors; Specimen; Surrogate Endpoint; Testing; Training; Treatment Failure; Treatment outcome; Tumor Volume; Validation; Work; base; burden of illness; cancer risk; cancer type; chimeric antigen receptor T cells; cohort; conventional therapy; detection method; genetic profiling; high risk; improved; indexing; innovation; large cell Diffuse non-Hodgkin' s lymphoma; lenalidomide; novel; novel marker; novel strategies; novel therapeutics; outcome prediction; personalized medicine; personalized predictions; personalized risk prediction; predictive modeling; prospective; radiological imaging; response; risk prediction; risk stratification; surrogacy; survival prediction; targeted agent; tool; treatment effect; treatment response; treatment risk; trial design; tumor; tumor DNA

PROJECT SUMMARY/ABSTRACT PIs: Ash Alizadeh, M.D./Ph.D. & Maximilian Diehn, M.D./Ph.D. For patients with Diffuse large B-cell lymphoma (DLBCL), the most common lymphoma subtype, curative outcomes are common. Unfortunately, despite many large clinical trials, survival has not significantly improved over the last 15 years and nearly a third of patients continue to succumb to this disease. For these patients, effective strategies to predict early treatment failures have been elusive. Our long-term goal is to study the ability of baseline and dynamic risk factors, including genetic mutations and circulating tumor DNA (ctDNA), to accurately predict treatment outcomes in DLBCL patients. Our central hypothesis is that novel biomarkers of cancer risk, such as detection of ctDNA and detailed genetic profiling, can be used for early detection of residual disease, to identify dynamic changes that anticipate treatment failure, and to provide early surrogate endpoints for future clinical trials. We will test our hypothesis via three specific aims: (1) To build an accurate and dynamic predictor of survival for patients newly diagnosed with DLBCL, (2) To test the validity and utility of this predictor in a large multi-institutional cohort of patients from around the globe, and (3) To assess the ability of this dynamic risk assessment tool to serve as an early surrogate endpoint in prospective clinical trials. We will apply our novel approach in both the frontline and relapse/refractory setting and to a variety of treatment types including immunochemotherapy, an antibody-drug conjugate and Chimeric Antigen Receptor (CAR) T cells. If successful, our project will lead to novel ways to select better therapies for patients at highest risk of failure. Our innovative approach, in which we will employ novel, blood-based methods for tumor genotyping and disease monitoring that were developed by our group, will lay the foundation for studies aimed at reducing risk of treatment failure in DLBCL patients. Demonstrating that this approach can serve as a robust, early surrogate endpoint for patients with aggressive lymphomas would be transformative for future trial design and for rapid evaluation of novel, personalized treatment approaches in patients at highest risk for recurrence. Our work will serve as proof-of-principle for an approach that could also be applied to other cancer types.

项目摘要/摘要 PIS：Ash Alizadeh医学博士和马克西米利安·迪恩医学博士/博士 弥漫性大B细胞淋巴瘤(DLBCL)是最常见的淋巴瘤亚型， 治愈的结果是常见的。不幸的是，尽管进行了许多大型临床试验，但存活率却没有。 在过去的15年中显著改善，近三分之一的患者继续死于 这种病。对于这些患者，预测早期治疗失败的有效策略是 难以捉摸。 我们的长期目标是研究基线和动态风险因素的能力，包括遗传因素 突变和循环肿瘤DNA(CtDNA)，以准确预测DLBCL的治疗结果 病人。我们的中心假设是，新的癌症风险生物标志物，如检测 CtDNA和详细的遗传图谱，可用于早期检测残留病、鉴定 预测治疗失败的动态变化，并提供早期替代终结点 未来的临床试验。我们将通过三个具体目标来检验我们的假设：(1)建立一个准确的 和新诊断的DLBCL患者生存的动态预测因子，(2)检验其有效性 这一预测指标在来自全球的大型多机构患者队列中的实用性， 以及(3)评估该动态风险评估工具作为早期替代工具的能力 前瞻性临床试验的终点。我们将把我们的新方法应用于前线和 复发/难治环境和包括免疫化疗在内的各种治疗类型，以及 抗体-药物结合物和嵌合抗原受体(CAR)T细胞。 如果成功，我们的项目将带来新的方法，为患者选择更好的治疗方案 失败的风险。我们的创新方法，其中我们将使用新颖的、基于血液的方法来 我们团队开发的肿瘤基因分型和疾病监测将为 为旨在降低DLBCL患者治疗失败风险的研究奠定基础。 证明这种方法可以作为强健的、早期的替代终点 侵袭性淋巴瘤将改变未来的试验设计和快速评估 新的个性化治疗方法适用于复发风险最高的患者。我们的工作将 作为一种方法的原则证明，该方法也可以应用于其他癌症类型。