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

项目摘要 血流中循环中的无细胞DNA(CfDNA)起源于死亡的细胞，是一种很有前途的非 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，表明潜在的临床应用 CfDNA。然而，cfDNA可以来自全身任何数量的组织，以及一些cfDNA 在特定时刻出现在个体身上，很可能源于健康的细胞周转。为了更好地理解 组织退化，特别是在疾病的情况下，对cfDNA来源组织的可靠估计是 需要的。识别cfDNA的起源组织将直接影响疾病的诊断和监测， 以及在定量生物标记物的发现方面。 对于cfDNA来说，现有的组织分解方法是不够的。首先，cDNAs只是 在血液中有少量存在。目前的分解方法一般依赖于基于数组的平台 这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，这需要较低的输入cfDNA，但噪声较大。此数据不会被寻址 与以前的方法相同。最后，准确分解cfdna混合物需要对所有 可能对混合物有影响的组织类型。然而，这一强有力的参考几乎是 不可能组装，因为有数百种不同的组织类型，而且因为一种 组织中的Cpg可能会有所不同。这可能会导致以前方法的分解结果出现偏差。 在这项提案中，我们的目标是通过开发一种全面的 CfDNA组织起源预测的工作流程。我们将开发一种统计方法来预测 CfDNA的来源，允许低读取计数数据和未知组织类型(目标1)。我们假设这是 将减少分解估计中的偏差，并使我们的方法比以前的方法得到更广泛的应用。 此外，我们建议开发一种捕获协议，以丰富信息丰富的cfdna片段。 指组织或疾病状态。该方案将设计为仅与少量输入cfDNA(AIM)一起使用 2)。我们假设捕获面板方法将极大地降低测序成本，从而增加 我们的方法的临床实用性。最后，我们建议将我们的方法应用于一大批ALS患者和年龄 匹配对照(目标3)。如果成功，这一生物标记物将对糖尿病的治疗产生重大影响。 以及治疗肌萎缩侧索硬化症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.