A Comprehensive Protocol for Tissue of Origin Prediction in Circulating Cell-Free DNA

项目摘要 血液中的循环无细胞DNA（cfDNA）来源于垂死的细胞，是一种有前途的非细胞毒性DNA。 细胞死亡的侵入性生物标志物。最近的研究表明，cfDNA水平与 癌症，组织创伤，自身免疫性疾病和器官移植，表明潜在的临床效用 cfDNA。然而，CfDNA可以来源于整个身体的任何数量的组织，并且一些CfDNA可以来源于整个身体的任何数量的组织。 在特定时刻存在于个体中的可能源于健康的细胞更新。更好地了解 组织变性，特别是在疾病的情况下，cfDNA来源组织的可靠估计是 needed.鉴定cfDNA来源的组织将对疾病诊断和监测产生直接影响， 和定量生物标志物发现。 用于来源组织分解的现有方法不足以用于cfDNA。首先，cDNA仅 少量存在于血液中。当前的分解方法通常依赖于基于数组的平台 其需要大量的患者血液，这可能不适用于临床。另一种选择是完整的 基因组亚硫酸氢盐测序数据，需要较低的输入cfDNA，但有噪音。此数据未被处理 用以前的方法。最后，准确地分解cfDNA混合物需要对所有的 可能导致混合物的可能组织类型。然而，这一强大的参考几乎是 不可能组装，因为有数百种不同的组织类型，并且因为甲基化状态对于一个 组织中的CpG可以变化。这可能导致先前方法的分解结果中的偏差。 在本提案中，我们的目标是通过制定一个全面的 cfDNA来源组织预测的工作流程。我们将开发一种统计方法来预测组织的 cfDNA的起源，允许低读段计数数据和未知的组织类型（目的1）。我们假设这 将减少分解估计的偏差，并使我们的方法比以前的方法更广泛地使用。 此外，我们建议开发一种捕获方案，该方案将富集提供信息的cfDNA片段。 组织或疾病状态。该方案将被设计为仅与少量输入cfDNA（Aim 2）。我们假设捕获面板方法将大大降低测序成本，从而增加测序效率。 我们的方法的临床效用。最后，我们建议将我们的方法应用于一个大型的ALS患者队列， 匹配的对照（目标3）。如果成功，这种生物标志物将对治疗 以及ALS和其他退行性疾病的药物开发。

PROJECT ABSTRACT Circulating cell-free DNA (cfDNA) in the bloodstream originates from dying cells and is a promising non- invasive biomarker for cell death. Recent studies have demonstrated that cfDNA levels are correlated with cancer, tissue trauma, autoimmune disease, and organ transplants, indicating the potential clinical utility of cfDNA. CfDNA, however, can originate from any number of tissues throughout the body, and some of the cfDNA present in an individual at a given moment is likely originating from healthy cell turnover. To better understand tissue degeneration, especially in the context of disease, a reliable estimate of the tissue of origin of cfDNA is needed. Identifying the tissue of origin of cfDNA will have direct impact on disease diagnosis and monitoring, and in quantitative biomarker discovery. Pre-existing methods for tissue of origin decomposition are inadequate for cfDNA. Firstly, cDNA is only present in a small amount in the blood. Current decomposition methods generally rely on array-based platforms that require an onerous amount of patient blood, which may not be clinically applicable. An alternative is whole genome bisulfite sequencing data, which requires lower input cfDNA, but is noisy. This data is not addressed with previous methods. Finally, accurately decomposing cfDNA mixtures requires a robust understanding of all possible tissue types that could potentially contribute to the mixture. This robust reference, however, is nearly impossible to assemble, as there are hundreds of distinct tissue types, and because the methylation state for a CpG in a tissue can vary. This could lead to biases in the decomposition results of previous methods. In this proposal, we aim to address the limitations of previous methods by developing a comprehensive workflow for cfDNA tissue-of-origin prediction. We will develop a statistical method for predicting the tissue of origin of cfDNA, allowing for low read count data and unknown tissue types (Aim 1). We hypothesize that this will reduce bias in decomposition estimates and allow our method to be more widely used than previous methods. Additionally, we propose developing a capture protocol that will enrich for cfDNA fragments that are informative of tissue or disease status. This protocol will be designed to use with only small amounts of input cfDNA (Aim 2). We hypothesize that a capture panel approach will vastly reduce sequencing costs and, thus, increase the clinical utility of our approach. Lastly, we propose applying our method to a large cohort of ALS patients and age matched controls (Aim 3). If successful, this biomarker will have substantial impact on the treatment of and drug development for ALS and other degenerative diseases.