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

项目摘要/摘要 准确的肿瘤分类是临床癌症治疗和精确肿瘤学的关键。治疗方案包括 通常由肿瘤组织的病理或诊断提供信息。对患有以下疾病的患者来说是一个重大挑战 转移性癌症是一种接触肿瘤组织的有限途径，因为手术活检不是常规的或重复的 在整个治疗过程中收集的数据。然而，肿瘤在治疗过程中会经历剧烈的分子变化 转移进展和对治疗的抵抗。肿瘤细胞释放的循环肿瘤DNA(CtDNA) 进入血液是一种非侵入性的解决方案，用于解决组织可获得性方面的挑战。当前的研究和 临床工作的重点是检测ctdna中的基因组变化，但它们并不总是能解释治疗。 失败了。耐药表型是由遗传和表观遗传调节的不同变化来定义的 景观，它们共同形成了肿瘤调节组。目前还不可能全面地描绘 患者在治疗过程中的肿瘤调节。 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 将被用于描述肿瘤调节组和调查耐药表型调节的方法 直接从ctDNA中提取。我们的方法将整合基因组改变的分析，染色质的可及性， 转录调控和DNA甲基化来自同一个ctDNA样本。这一具有成本效益的战略 通过监测肿瘤的表观遗传调节和其对患者疾病的影响 临床表型。该项目的创新方面包括深度神经网络的开发和 机器学习方法集成从单个ctdna分析中提取的多组数据。我们将聘用 独特的系统和资源来发展我们的方法和提高我们对肿瘤分子的理解 异质性和治疗反应。(1)从快速尸检研究中，我们将评估DNA的贡献 从多个转移灶中确定ctDNA的关键来源。(2)来自患者来源的异种移植(PDX) 小鼠模型，我们将建立一个从小鼠血浆中提取的人ctDNA储存库来支持发育 在PDX治疗条件下使用新疗法的活动和研究。 该框架可概括为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靶向治疗的有效性。成功 该项目的创新将建立癌症研究的范式转变，并加快翻译 推进精确肿瘤学的新临床应用。

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.