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

优化突变负荷低、结构复杂性高的癌症的血液活检

• 批准号: 10789700
• 负责人: Heather Lynn Gardner
• 金额: $ 12.38万
• 依托单位: TUFTS UNIVERSITY BOSTON
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-09-01 至 2025-08-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10789700
• 关键词: Aftercare; Algorithms; Animal Disease Models; Animal Model; Biological Assay; Biological Models; Biopsy; Blood; Cancer Detection; Cancer Model; Canis familiaris; Cells; Characteristics; Circulation; Clinical; Combined Modality Therapy; Complex; Copy Number Polymorphism; DNA; DNA Methylation; DNA methylation profiling; Data; Data Set; Detection; Development; Diagnosis; Disease; Disease Progression; Drug Exposure; Drug resistance; Early Diagnosis; Early Intervention; Early identification; Enrollment; Epigenetic Process; Evaluation; Ewings sarcoma; Excision; FDA approved; Gene Expression; Gene Expression Alteration; Genes; Genome; Genomics; Goals; Histologic; Human; Image; Incidence; Individual; MPP2 gene; Machine Learning; Malignant Neoplasms; Malignant neoplasm of lung; Measures; Methodology; Methods; Methylation; Monitor; Mutation; Nature; Neoadjuvant Therapy; Neoplasm Metastasis; Outcome; Patient Care; Patients; Pattern; Plasma; Primary Neoplasm; Prior Therapy; Prospective Studies; RNA; Recurrence; Regimen; Relapse; Resistance; Rhabdomyosarcoma; Sampling; Scientist; Sensitivity and Specificity; Somatic Mutation; Techniques; Testing; Therapeutic; Time; Tissues; Transcript; Treatment Protocols; Tumor Burden; Tumor Tissue; Variant; Work; biobank; cancer cell; cancer diagnosis; cancer genome; cell free DNA; circulating DNA; clinical decision-making; clinical diagnostics; data integration; design; differential expression; experience; genome sequencing; human model; immunoregulation; implementation facilitation; improved; individualized medicine; liquid biopsy; lung metastatic; machine learning algorithm; malignant breast neoplasm; methylation pattern; molecular marker; neoplastic cell; novel; osteosarcoma; prospective; rapid detection; response; sarcoma; skills; therapy resistant; tool; transcriptome sequencing; transcriptomics; translational medicine; treatment optimization; treatment response; tumor; tumor DNA; tumor microenvironment; whole genome

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.

项目摘要 液体活检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批准的液体活检测试旨在寻找常见的疾病相关突变。这些液体 活检测试在具有明确突变景观的肿瘤中最成功，例如肺和乳腺 癌然而，寻找常见突变在结构复杂的肿瘤中不太成功， 突变的发生率，如许多肉瘤的情况，如骨肉瘤（OS）和尤文氏肉瘤。 最近的数据表明，不依赖于突变的液体活检技术，包括评估循环中的 DNA片段大小模式和甲基化状态可以增加测定的灵敏度并鉴定组织 人类癌症的起源和组织学亚型。此外，现在的证据表明，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一种替代物， 对治疗的反应和/或识别治疗抗性的早期出现。因此，有可能 使用先进的液体活检工具更有效地告知患者特异性治疗，特别是在 其中重复成像/肿瘤取样是具有挑战性的。因此，这一提议的假设是， 基因表达和表观遗传转移标记可以在从肿瘤组织中分离的RNA和DNA中鉴定。 血浆中的犬OS和集成使用机器学习，以提高液体活检的灵敏度。 进一步预测，这种改进的液体活检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。我们将使用犬操作系统，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。使用患者匹配 在整个治疗过程中的多个时间点采集的OS犬血浆样本，我们将评估无细胞 使用全面的突变独立液体活检检测DNA和RNA。这将包括评价 多个参数，包括游离DNA片段大小、甲基化和基因表达改变， 使用机器学习来优化参数集成。将进一步确认液体活检工具， 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 疾病特异性信号最终，从这项工作中开发的工具和技术将具有 广泛适用于犬和人类肉瘤，有助于提高癌症检测的准确性， 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，从而进一步支持我的研究。 成为一名成功的独立临床科学家。