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

基本信息

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
项目类别：
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

项目摘要

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.

项目概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。在几种人类癌症中，肿瘤细胞将小片段的主要 DNA 和 RNA 释放到循环中， FDA 批准的液体活检测试旨在寻找常见的疾病相关突变。活检测试在具有明确突变情况的肿瘤（例如肺癌和乳腺癌）中最为成功然而，在结构复杂的肿瘤中寻找常见突变的成功率较低。突变的发生率，就像许多肉瘤的情况一样，例如骨肉瘤 (OS) 和尤文氏肉瘤。最近的数据表明，不依赖突变的液体活检技术，包括循环评估 DNA 片段大小模式和甲基化状态，可以提高测定的灵敏度并识别组织此外，人类癌症的起源和组织学亚型的证据现在表明存在独特的基因。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/或识别早期出现的治疗耐药性因此，有可能。使用先进的液体活检工具更有效地告知患者特定的治疗方法，特别是在某些情况下重复成像/肿瘤采样具有挑战性，因此，该提议的假设是：基因表达和表观遗传转移特征可以在分离的 RNA 和 DNA 中鉴定犬操作系统中的血浆并使用机器学习进行集成，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方法反映对治疗的反应或抵抗的特定特征我们将使用犬 OS，它具有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。在整个治疗过程中的多个时间点采集患有 OS 的狗的血浆样本，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检测定，这将纳入评估。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，液体活检工具将得到进一步验证。最后，我们将开始剖析药物暴露如何改变。最终，这项工作开发的工具和技术将具有流通中的疾病特异性特征。广泛适用于犬和人类肉瘤，有助于提高癌症检测和治疗的准确性重要的是，本提案中概述的工作为临床决策提供了独特的机会。在转化医学背景下扩展基因组技能，从而进一步支持我的研究发展成为一名成功的独立临床科学家。