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 肿瘤细胞释放出小块DNA和RNA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与疾病相关的突变。这些液体 活检测试在具有明确的突变景观的肿瘤中最成功，例如肺和乳房 癌症。但是，寻找常见突变在结构复杂的肿瘤中不太成功 突变的发生率与许多肉瘤一样，例如骨肉瘤（OS）和Ewing的肉瘤。 最近的数据表明，与突变无关的液体活检技术，包括评估循环 DNA碎片大小模式和甲基化状态可以提高测定的灵敏度并识别组织 人类癌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充当替代 对治疗的反应和/或确定治疗抗药性的早期出现。因此，有可能 使用先进的液体活检工具来更有效地告知患者特异性疗法，尤其是在情况下 重复成像/肿瘤采样是挑战。因此，该提议的基本假设是 可以在RNA中鉴定基因表达和表观遗传转移性特征，并从从中分离出DNA 犬OS中的血浆并使用机器学习进行整合，以提高液体活检的敏感性。 进一步预测，这种改进的液体活检平台将能够识别治疗 特定的特定特征反映了对治疗的反应或抵抗力。我们将使用犬OS，其中有一个 结构混乱的肿瘤基因组，是人类肉瘤的大动物疾病模型。使用患者匹配 在整个处理过程中，我们将在多个时间点取的狗的血浆样品，我们将评估无细胞 DNA和RNA使用全面的非突变液体活检测定法。这将结合评估 多个参数，包括无细胞的DNA片段大小，甲基化和基因表达改变以及 使用机器学习来优化参数集成。液体活检工具将进一步验证 检测OS患者早期疾病进展。最后，我们将开始剖析药物暴露的变化 循环中的疾病特异性签名。最终，这项工作开发的工具和技术将具有 对犬和人类肉瘤的广泛适用性，支持提高癌症检测的准确性 临床决策。重要的是，该提案中概述的工作为 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 作为成功的独立临床科学家的发展。