Predicting transcriptional signatures and tumor subtypes from circulating tumor DNA

项目总结/摘要 肿瘤表型变化，如致死性前列腺癌中的转分化和激素受体 在乳腺癌中，转化越来越频繁的观察，作为耐药机制，以靶向 治疗因此，表征驱动治疗诱导的肿瘤表型的转录调控 治疗过程中的“实时”变化对研究治疗抵抗机制具有重要意义 并告知临床治疗决策。肿瘤分子变化的监测尤其具有挑战性 因为转移部位的位置和数量使得难以进行重复的活组织检查。因此，在本发明中， 在治疗过程中很难描述肿瘤的演变和细胞的可塑性， 目前的治疗策略和精准医疗的转移性癌症患者。循环肿瘤DNA 从肿瘤细胞释放到血液中的ctDNA（ctDNA）是一种非侵入性的“液体活检”解决方案，用于解决肿瘤细胞的细胞毒性。 组织可及性方面的挑战。目前的研究和临床工作集中在检测基因组 ctDNA的改变然而，从ctDNA研究肿瘤表型仍然具有挑战性，仍然是一个挑战。 新兴的研究领域。 该提案的目的是开发一种创新的计算方法来分析和整合基因组 改变、染色质可及性和转录调控直接来自标准ctDNA测序数据。 最近的进展和我们的初步研究现在表明，有趣的可能性，以概况这些“多- 仅来自标准ctDNA全基因组测序数据的计算分析的“组学”模式。然而，在这方面， 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 框架来预测ctDNA的转录调控。我们将优化ctDNA数据标准化， 开发一个无监督的概率生成模型，用于预测染色质可及性和转录 ctDNA的调控。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- 衍生的异种移植模型。在目标2中，我们将检验从ctDNA中分析的多组学特征的假设， 将提供一种非侵入性的方法来分类肿瘤亚型和调查分子表型变化 在治疗期间。我们将开发分类器，用于预测成人和成人中的肿瘤亚型和表型变化， 儿科癌症测试用于表征多组学特征和预测治疗诱导的 表型变化，我们将分析来自接受靶向治疗的患者的连续ctDNA样品。 该方法将被实现为一个开源的R包，以及一个可以部署在本地的工作流。 和云环境，促进其在癌症研究界的采用。该提案针对 迫切需要更好的分析方法来“实时”研究癌症治疗抗性 并推进癌症精准医疗。

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.