Optimizing blood biopsy in cancers with low mutation burden and high structural complexity

项目总结 液体活组织检查是一种非侵入性技术，可用于帮助诊断和监测癌症。它是基于 肿瘤细胞释放小片段DNA和RNA进入循环的原理。在几种人类癌症中， FDA批准的液体活组织检查旨在寻找常见的疾病相关突变。这些液体 活组织检查最成功的是突变情况明确的肿瘤，如肺和乳房。 癌症。然而，在结构复杂的肿瘤中寻找共同的突变就不那么成功了，因为 突变的发生率，这是许多肉瘤的情况，如骨肉瘤(OS)和尤文氏肉瘤。 最近的数据表明，非突变液体活组织检查技术，包括循环评估 DNA片段大小模式和甲基化状态，可以提高检测的灵敏度和识别组织 人类癌症的起源和组织亚型。此外，现在有证据表明，独特的基因 通过液体活检测量的表达和甲基化特征有可能作为替代 对治疗的反应和/或确定治疗耐药的早期出现。因此，有可能 使用先进的液体活组织检查工具更有效地告知患者特定的治疗方法，特别是在 其中重复成像/肿瘤采样是具有挑战性的。因此，这一提议背后的假设是 从分离的RNA和DNA中可以鉴定出基因表达和表观遗传转移特征 血浆在犬OS中综合运用机器学习来提高液体活检的敏感性。 进一步预测，这种改进的液体活组织检查平台将能够识别治疗 反映对治疗的反应或抵抗的特定特征。我们将使用Canine OS，它有一个 结构混乱的肿瘤基因组，作为人类肉瘤的大型动物疾病模型。使用与患者匹配的 在治疗过程中多个时间点采集OS犬的血浆样本，我们将评估无细胞 DNA和RNA使用全面的不依赖突变的液体活组织检查。这将包括评估 多个参数，包括无细胞DNA片段大小、甲基化和基因表达变化以及 使用机器学习来优化参数集成。液体活组织检查工具将进一步得到验证 检测OS患者的早期疾病进展。最后，我们将开始剖析药物暴露如何改变。 流通中的疾病特异性签名。最终，从这项工作中开发的工具和技术将具有 广泛适用于犬类和人类肉瘤，有助于提高癌症检测和 临床决策。重要的是，本提案中概述的工作提供了一个独特的机会 在转化医学的背景下扩展基因组技能集，从而进一步支持我的 发展成为一名成功的独立临床医生科学家。

PROJECT SUMMARY Liquid biopsy is a non-invasive technique that can be used to help diagnose and monitor cancer. It is based on the principle that tumor cells release small pieces of DNA and RNA into circulation. In several human cancers, FDA-approved liquid biopsy tests are designed to look for common disease-associated mutations. These liquid biopsy tests are most successful in tumors with a well-defined mutation landscape, such as lung and breast cancer. However, looking for common mutations is less successful in structurally complex tumors with a lower incidence of mutations, as is the case with many sarcomas, such as osteosarcoma (OS) and Ewing’s sarcoma. Recent data indicate that mutation-independent liquid biopsy techniques, including assessment of circulating DNA fragment size patterns and methylation status, can increase sensitivity of the assay and identify the tissue of origin and histologic subtype of human cancers. Additionally, evidence now suggests that unique gene expression and methylation signatures measured by liquid biopsy have the potential to act as a surrogate for response to treatment and/or identify early emergence of treatment resistance. As such, there is potential for using an advance liquid biopsy tool to inform patient-specific therapies more effectively, particularly in instances where repeat imaging/tumor sampling is challenging. As such, the hypothesis underlying this proposal is that gene expression and epigenetic metastatic signatures can be identified in RNA and DNA isolated from plasma in canine OS and integrated using machine learning to improve the sensitivity of liquid biopsy. It is further predicted that this improved liquid biopsy platform will be capable of identifying treatment specific signatures reflective of response or resistance to therapy. We will use canine OS, which has a structurally chaotic tumor genome, as a large animal disease model of human sarcomas. Using patient-matched plasma samples from dogs with OS taken at multiple timepoints throughout treatment, we will evaluate cell-free DNA and RNA using a comprehensive mutation-independent liquid biopsy assay. This will incorporate evaluation multiple parameters, including cell-free DNA fragment sizes, methylation, and gene expression alterations and use machine learning to optimize parameter integration. The liquid biopsy tool will be further validated for detection of early disease progression in OS patients. Lastly, we will begin to dissect how drug exposure alters disease-specific signatures in circulation. Ultimately, the tools and techniques developed from this work will have broad applicability to both canine and human sarcomas, facilitating enhanced accuracy for cancer detection and clinical decision-making. Importantly, the work outlined in this proposal provides a unique opportunity for expansion of genomic skill sets in the context of translational medicine, thereby further supporting my development as a successful independent clinician scientist.