Predicting transcriptional signatures and tumor subtypes from circulating tumor DNA

从循环肿瘤 DNA 预测转录特征和肿瘤亚型

• 批准号: 10601439
• 负责人: Gavin Ha
• 金额: $ 20.19万
• 依托单位: FRED HUTCHINSON CANCER CENTER
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-09-10 至 2023-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10601439
• 关键词: Predicting; transcriptional; signatures; tumor; subtypes

Project Summary/Abstract Tumor phenotype changes, such as trans-differentiation in lethal prostate cancers and hormone receptor conversions in breast cancer, are increasingly frequent observations as resistance mechanisms to targeted therapies. Therefore, characterizing the transcriptional regulation that drives treatment-induced tumor phenotype changes during therapy in “real-time” has critical implications for studying mechanisms of resistance to therapies and informing clinical treatment decisions. Surveillance of molecular changes in tumors is especially challenging because the location and number of metastatic sites make it intractable to perform repeated biopsies. As a result, it is difficult to characterize tumor evolution and cellular plasticity during therapy, exemplifying a major limitation of current treatment strategies and precision medicine for patients with metastatic cancer. Circulating tumor DNA (ctDNA) released from tumor cells into the blood is a non-invasive “liquid biopsy” solution for addressing challenges in tissue accessibility. Current research and clinical efforts have focused on detecting genomic alterations in ctDNA. However, studying the tumor phenotype from ctDNA remains challenging and is still a nascent area of research. The objective of this proposal is to develop an innovative computational method to profile and integrate genomic alterations, chromatin accessibility, and transcriptional regulation directly from standard ctDNA sequencing data. Recent advances and our preliminary studies now demonstrate the intriguing possibility to profile these “multi- omic” patterns solely from computational analysis of standard ctDNA whole genome sequencing data. However, there is still a lack of tools to predict transcriptional profiles from ctDNA. In Aim 1, we will develop a generalized framework to predict transcriptional regulation from ctDNA. We will optimize ctDNA data normalization and develop an unsupervised probabilistic generative model for predicting chromatin accessibility and transcriptional regulation in ctDNA. To evaluate the method, we will perform benchmarking using plasma ctDNA from patient- derived xenograft models. In Aim 2, we will test the hypothesis that the multi-omic signatures profiled from ctDNA will provide a non-invasive approach to classify tumor subtypes and to survey molecular phenotype changes during therapy. We will develop classifiers for predicting tumor subtypes and phenotype changes in adult and pediatric cancers. To test the utility for characterizing multi-omic signature and predicting treatment-induced phenotype changes, we will analyze serial ctDNA samples from patients receiving targeted therapies. The method will be implemented as an open-source R package, and a workflow that can be deployed on local and cloud environments, facilitating its adoption in the cancer research community. This proposal addresses the urgent unmet clinical need for better analytical approaches to study cancer treatment resistance in “real-time” and to advance cancer precision medicine.

项目概要/摘要 肿瘤表型变化，例如致命性前列腺癌和激素受体的转分化 作为针对靶向药物的耐药机制，乳腺癌中的转化越来越频繁地被观察到。 因此，表征驱动治疗诱导的肿瘤表型的转录调控。 治疗过程中“实时”的变化对于研究治疗耐药机制具有重要意义 监测肿瘤的分子变化尤其具有挑战性。 因为转移部位的位置和数量使得重复活检变得困难。 在治疗过程中很难表征肿瘤的进化和细胞可塑性，这是一个主要的限制 循环肿瘤 DNA 患者的当前治疗策略和精准医学。 （ctDNA）从肿瘤细胞释放到血液中是一种非侵入性“液体活检”解决方案，用于解决 当前的研究和临床工作主要集中在检测基因组方面。 然而，从 ctDNA 研究肿瘤表型仍然具有挑战性，并且仍然是一个难题。 新兴的研究领域。 该提案的目标是开发一种创新的计算方法来分析和整合基因组 直接从标准 ctDNA 测序数据中获取改变、染色质可及性和转录调控。 最近的进展和我们的初步研究现在证明了描述这些“多 组学”模式仅来自标准 ctDNA 全基因组测序数据的计算分析。 仍然缺乏从 ctDNA 预测转录谱的工具。在目标 1 中，我们将开发一个通用的方法。 我们将优化 ctDNA 数据标准化和预测转录调控的框架。 开发一个无监督的概率生成模型来预测染色质可及性和转录 为了评估该方法，我们将使用来自患者的血浆 ctDNA 进行基准测试。 在目标 2 中，我们将测试从 ctDNA 分析多组学特征的假设。 将提供一种非侵入性方法来对肿瘤亚型进行分类并调查分子表型变化 我们将开发用于预测成人和儿童肿瘤亚型和表型变化的分类器。 测试表征多组学特征和预测治疗诱导的实用性。 表型变化，我们将分析接受靶向治疗的患者的连续 ctDNA 样本。 该方法将作为开源 R 包实现，并且可以部署在本地的工作流程 和云环境，促进其在癌症研究界的采用。 对更好的分析方法来“实时”研究癌症治疗耐药性的迫切未满足的临床需求 并推进癌症精准医学。