Translating the tumor regulome from cell-free DNA for precision oncology

将游离 DNA 转化为肿瘤调节组以实现精准肿瘤学

• 批准号: 10473384
• 负责人: Gavin Ha
• 金额: $ 154.21万
• 依托单位: FRED HUTCHINSON CANCER CENTER
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-09-13 至 2025-08-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10473384
• 关键词: Address; Autopsy; Biological Assay; Biopsy; Blood; Cancer Patient; Chromatin; Classification; Clinical; Computing Methodologies; DNA; DNA Methylation; Development; Disease; Disseminated Malignant Neoplasm; Effectiveness; Epigenetic Process; Genetic; Genome; Hematopoietic Neoplasms; Heterogeneity; Human; Malignant Neoplasms; Methods; Molecular; Monitor; Multiomic Data; Mus; Neoplasm Metastasis; Operative Surgical Procedures; Pathology; Pathway interactions; Patient-Focused Outcomes; Patients; Phenotype; Plasma; Regulation; Research; Resistance; Resources; Sampling; Source; Surveys; System; Time; Tissues; Transcriptional Regulation; Translating; Translations; Treatment Failure; Tumor Biology; Tumor Tissue; anticancer research; cancer care; cancer therapy; cell free DNA; clinical application; clinical diagnostics; clinical phenotype; cost effective; deep neural network; epigenetic regulation; genome analysis; improved; innovation; machine learning method; mouse model; neoplastic cell; novel therapeutics; patient derived xenograft model; precision oncology; repository; targeted treatment; therapy resistant; treatment response; tumor; tumor DNA; tumor behavior; tumor diagnostic

Project Summary/Abstract An accurate tumor classification is pivotal to clinical cancer care and precision oncology. Treatment options are often informed by the pathology or diagnostic from the tumor tissue. A major challenge for patients with metastatic cancer is the limited access to tumor tissue because surgical biopsies are not routinely nor repeatedly collected throughout the course of therapy. However, tumors can undergo drastic molecular changes during metastatic progression and resistance to therapies. Circulating tumor DNA (ctDNA) released from tumor cells into the blood is a non-invasive solution for addressing challenges in tissue accessibility. Current research and clinical efforts have focused on detecting genome alterations in ctDNA, but they do not always explain treatment failure. Treatment-resistant phenotypes are defined by distinct changes in the genetic and epigenetic regulatory landscape, which collectively form the tumor regulome. Currently, it is not possible to comprehensively portray the tumor regulome in patients during the course of therapy. We propose to overcome these limitations by developing innovative computational methods and epigenetic assays that will be employed to profile the tumor regulome and survey the regulation of resistant phenotypes directly from ctDNA. Our methods will integrate the analysis of genome alterations, chromatin accessibility, transcriptional regulation, and DNA methylation from the same ctDNA sample. This cost-effective strategy provides a temporal window into the patient’s disease by monitoring the tumor epigenetic regulation and its clinical phenotype. The innovative aspects of this project include the development of deep neural networks and machine learning methods to integrate the multi-omic data extracted from a single ctDNA assay. We will employ unique systems and resources to develop our methods and advance our understanding of tumor molecular heterogeneity and treatment response. (1) From rapid autopsy studies, we will assess the contribution of DNA from multiple metastatic lesions to determine the key source of ctDNA. (2) From patient-derived xenograft (PDX) mouse models, we will establish a repository of human ctDNA from mouse plasma to support development activities and studies under PDX treatment conditions using novel therapies. This framework is generalizable to address research questions related to tumor biology and treatment response, including monitoring cancer-associated pathways and the effectiveness of targeted therapies. Successful innovations made in this project will establish a paradigm shift in cancer research and accelerate translation of new clinical applications to advance precision oncology.

项目概要/摘要 准确的肿瘤分类对于临床癌症护理和精准肿瘤治疗选择至关重要。 通常通过肿瘤组织的病理学或诊断来了解，这是患者面临的主要挑战。 转移性癌症是对肿瘤组织的访问有限，因为手术活检既不常规也不重复 然而，肿瘤在治疗过程中可能会发生剧烈的分子变化。 肿瘤细胞释放的循环肿瘤 DNA (ctDNA) 的转移进展和耐药性。 进入血液是解决组织可及性挑战的非侵入性解决方案。 临床工作的重点是检测 ctDNA 中的基因组改变，但它们并不总能解释治疗 治疗抵抗表型是由遗传和表观遗传调控的明显变化定义的。 目前，还无法全面描述肿瘤的调控组。 治疗过程中患者的肿瘤调节组。 我们建议通过开发创新的计算方法和表观遗传学来克服这些限制 将用于分析肿瘤调节组并调查耐药表型调节的测定 我们的方法将直接从 ctDNA 整合基因组改变、染色质可及性的分析， 转录调控，以及来自同一 ctDNA 样本的 DNA 甲基化，这种具有成本效益的策略。 通过监测肿瘤表观遗传调控及其影响，提供了解患者疾病的时间窗口 该项目的创新方面包括深度神经网络的开发和 我们将采用机器学习方法来整合从单个 ctDNA 检测中提取的多组学数据。 独特的系统和资源来开发我们的方法并增进我们对肿瘤分子的理解 (1) 通过快速尸检研究，我们将评估 DNA 的贡献。 (2) 来自患者来源的异种移植物 (PDX) 小鼠模型，我们将建立一个来自小鼠血浆的人类 ctDNA 存储库以支持开发 使用新疗法在 PDX 治疗条件下进行的活动和研究。 该框架可推广到解决与肿瘤生物学和治疗反应相关的研究问题， 包括监测癌症相关途径和成功靶向治疗的有效性。 该项目的创新将改变癌症研究的范式并加速转化 推进精准肿瘤学的新临床应用。