Simple and Accessible Microfluidic Platform for Single Molecule Sequence Profiling of Tumor-derived DNA within Liquid Biopsies

项目总结/摘要 今年美国将有超过60万人死于癌症。据估计，其中25%的死亡病例可能 在早期阶段就被发现了。实施微创常规筛查， 例如子宫颈抹片检查，已证明是降低癌症死亡率有效方法。 然而，一些挑战阻碍了在大多数癌症中成功实施筛查，特别是在癌症中。 其不容易用于成像或组织活检。癌症诊断的一个有前途的途径是 通过使用来自所谓的“液体活组织检查”的循环DNA或其它可获得的循环样品介质 在整个身体中从组织中收集遗传物质，包括肿瘤。肿瘤特异性分子 生物标志物，如DNA突变和甲基化，可以在微创样品介质中找到，如 如血液、粪便和尿液，但通常仅以非常低的拷贝数（<10拷贝/mL）和低拷贝数（<10拷贝/mL）存在。 在健康DNA的高背景中的部分（<0.1%）。技术局限性和实用性 目前可用工具的不可及性已经排除了发现早期癌症的研究努力- 特定的DNA生物标志物面板和随后的临床实施，可以改善患者的结果。 为了解决这个问题，我们以前开发了一个原型数字微流体平台，以促进高度敏感， 通过高度平行化的单分子低成本检测癌症特异性DNA甲基化模式 热力学排序在第1阶段SBIR提案中，我们将大大扩展 一个平台，以提高可及性和改善早期疾病检测的分析性能 通过显著提高其数字化能力。我们将开发一个高度多路复用的范例， 使用条形码技术检测和甲基化分析生物标志物组。然后我们将 将该测定结合到微流控平台中，该微流控平台可以询问数百至数千个单个DNA 通过将模板分子数字化为液滴进行高通量单分子分析，的 该平台将通过将成本降低至<25美元/次， 样品和周转时间为4小时。该平台将使单拷贝检测，即使在高 背景人群（<0.001%灵敏度），这可能是早期疾病所必需的。拟议 在这个第一阶段的工作项目将开发一个多路复用，多维分析的测定基础， 临床相关的生物标志物组（目标1），将该测定法并入高度平行化的微滴微流体 平台（目标2），并评估其临床可行性与血浆样本从一个队列的肺癌患者 控制（目标3）。这将为随后的第二阶段项目设计墨盒奠定基础 和仪器仪表，用于可扩展的制造和用户友好性，并实现自动化，机器- 学习图像和数据分析管道。

Project Summary/ Abstract Over 600,000 people in the US will die from cancer this year. It is estimated that 25% of these deaths could have been prevented by detection in earlier stages. Implementation of minimally-invasive routine screening, such as pap smears for cervical cancer, has proven to be an effective approach for reducing cancer mortality. However, several challenges prevent successful implementation of screening in most cancers, especially ones which are not readily accessible for imaging or tissue biopsy. One promising avenue for cancer diagnostics is through use of circulating DNA from so-called “liquid biopsies” or other accessible sample media circulating throughout the body collecting genetic material from tissues, including tumors. Tumor-specific molecular biomarkers, such as DNA mutations and methylation, can be found in minimally-invasive sample media, such as blood, stool, and urine, but are typically only present in very low copy numbers (<10 copies/mL) and low fractions (<0.1%) among a high background of healthy DNA. Technical limitations as well as practical inaccessibility of currently available tools have precluded research efforts to discover early-stage cancer- specific DNA biomarker panels and subsequent clinical implementation that could improve patient outcomes. To address this, we previously developed a prototype digital microfluidic platform to facilitate highly sensitive, low-cost detection of cancer-specific DNA methylation patterns by highly parallelized single-molecule thermodynamic sequencing. In this Phase 1 SBIR proposal, we will greatly expand upon the capabilities of this platform to increase accessibility and improve analytical performance towards detection of early-stage disease by significantly increasing its digitization power. We will develop a high-degree multiplexing paradigm for detection and methylation profiling of biomarker panels using a multicolor barcoding technique. We will then incorporate this assay into a microfluidic platform that can interrogate hundreds to thousands of single DNA copies by digitizing template molecules into droplets for high-throughput single molecules analysis. The platform will increase accessibility for biomarker research from liquid biopsies by reducing costs to <$25 per sample and turnaround time to 4 hours. The platform will enable single-copy detection even among high background populations (<0.001% sensitivity), which may be necessary for early-stage disease. The proposed work in this Phase 1 project will develop the assay fundamentals for a multiplex, multidimensional analysis of a clinically relevant biomarker panel (Aim 1), incorporate this assay into a highly-parallelized droplet microfluidic platform (Aim 2), and assess its clinical feasibility with plasma samples from a cohort of lung cancer patients and controls (Aim 3). This will lay the groundwork for a subsequent Phase 2 project to design the cartridge and instrumentation for scalable manufacturing and user-friendliness and implement automated, machine- learning image and data analysis pipelines.