Bacterial DNA as a Diagnostic Biomarker of Hepatocellular Carcinoma

细菌 DNA 作为肝细胞癌的诊断生物标志物

• 批准号: 10557105
• 负责人: Amir Zarrinpar
• 金额: $ 18.1万
• 依托单位: UNIVERSITY OF CALIFORNIA, SAN DIEGO
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-02-01 至 2025-01-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10557105
• 关键词: Advanced Development; Aftercare; Algorithms; Bacteria; Bacterial DNA; Benign; Biological Markers; Blood; Blood specimen; Cancer Biology; Cancer Etiology; Cancerous; Cells; Cessation of life; Chemoembolization; Cirrhosis; Colon; DNA; Data; Detection; Development; Diagnosis; Disease; Florida; Foundations; Genome; Goals; HBV Liver Disease; Hepatic Mass; Incidence; Liver; Liver neoplasms; Machine Learning; Malignant - descriptor; Malignant Neoplasms; Malignant neoplasm of liver; Microbe; Mission; Morbidity - disease rate; Outcome; Patients; Population Control; Primary Neoplasm; Primary carcinoma of the liver cells; Prognosis; Protocols documentation; Public Health; Rectal Cancer; Research; Research Personnel; Role; Sampling; Solid; Solid Neoplasm; Testing; The Cancer Genome Atlas; Therapeutic; Tissues; Tumor Burden; Universities; Virus; alpha-Fetoproteins; biobank; blood treatment; blood-based biomarker; cancer biomarkers; cancer genomics; cancer type; cohort; detection method; diagnostic biomarker; early detection biomarkers; experimental study; fungus; genome sequencing; genomic data; improved; machine learning algorithm; metastatic colorectal; microbial; microbial community; mortality; non-alcoholic fatty liver disease; novel strategies; patient screening; screening; surveillance imaging; tool; translational impact; translational potential; tumor; tumor DNA; whole genome

PROJECT SUMMARY/ABSTRCT Though hepatocellular carcinoma (HCC) is the 14th most common cancer in the US,1 it is the fifth most common cause of cancer deaths with a 5-year survival of 19.6%. The incidence of HCC in the US is rising at the annual rate of 4.5% per year, and it is the only cancer with an increase in mortality over the last ten years. Currently, the only method for detection of HCC is with surveillance imaging in patients with cirrhosis and alpha fetoprotein (AFP). However, approximately 13-20% of patients diagnosed with HCC do not have cirrhosis, and hence were never screened for the disease. In addition, AFP is normal in approximately 30-40% of patients with HCC. Because of this, HCC is usually detected at an advanced stage, when there are a limited number of therapeutic options. This proposal is born out of convincing preliminary data that indicate that DNA from tumor- dwelling microbes can identify malignancies. Approximately 2.5% of reads in The Cancer Genome Atlas (TCGA) are microbial. Machine learning algorithms using these microbial reads accurately identified solid tumors from each other and from adjacent control tissue. Moreover, much of this accuracy is maintained when blood samples are used instead of the tumors. This preliminary data is particularly robust for HCC. Based on this preliminary data, the proposed studies will advance the development of a biomarker for early detection of HCC in several ways. First, we will use the machine learning algorithms developed from TCGA on a new cohort of samples from the University of Florida liver biobank. This biobank has nearly as many HCC samples as the entire TCGA network. Second, the proposed studies will use more rigorous controls than what was available in the TCGA network. This includes the use of non-HCC liver malignancies and benign tumors. Whereas the machine learning algorithms were adequate in distinguishing HCC masses from other primary tumors in TCGA, it's not clear whether they can distinguish HCCs from other malignant and benign masses in the liver (e.g., metastatic colorectal cancer, hepatomas), which are more common than HCC, or the blood from patients with HCC to those from patients with cirrhosis without HCC. Finally, the proposed studies will help determine whether the machine learning algorithms developed from TCGA can detect whether HCC has been treated (e.g., chemoembolization) and thus potentially serve as a tool for surveillance. Overall, these experiments will help determine how generalizable the algorithms developed with TCGA are to independent cohorts of HCC. These studies will lay the foundation for the development of more effective screening and surveillance protocols that will hopefully impact the significant morbidity and mortality associated with HCC. Finally, if these studies are successful, they would encourage the exploration of using microbial DNA as an early detection biomarker for other types of cancers, and the role of tumor-dwelling bacteria in cancer biology.

项目摘要/ABSTRCT 尽管肝细胞癌（HCC）是美国第14个最常见的癌症，但它是第五大的癌症 癌症死亡的常见原因为5年生存率为19.6％。美国HCC的发病率正在上升 每年4.5％的年率，它是唯一在过去十年中死亡率增加的癌症。 目前，肝硬化患者的唯一检测HCC的方法是监视成像 胎儿蛋白（AFP）。但是，大约13-20％的诊断为HCC的患者没有肝硬化，并且 因此，从未对这种疾病进行筛查。此外，大约30-40％的患者AFP正常 HCC。因此，通常在高级阶段检测到HCC，当 治疗选择。该提议是由令人信服的初步数据诞生的，这些数据表明来自肿瘤的DNA 居住微生物可以识别恶性肿瘤。癌症基因组图集（TCGA）中约2.5％的读数 是微生物。使用这些微生物读取的机器学习算法准确地从 彼此和来自相邻的控制组织。此外，当血液样本时，保持大部分精度 用代替肿瘤。此初步数据对于HCC特别强大。 基于此初步数据，拟议的研究将推动早期生物标志物的开发 以多种方式检测HCC。首先，我们将使用从TCGA开发的机器学习算法 来自佛罗里达大学肝脏生物库的新样本。这个生物库的HCC几乎 示例作为整个TCGA网络。其次，拟议的研究将使用比什么更严格的控制 在TCGA网络中可用。这包括使用非HCC肝脏恶性肿瘤和良性肿瘤。 而机器学习算法足以区分HCC质量和其他主要的质量 TCGA的肿瘤，尚不清楚它们是否可以将HCC与其他恶性和良性肿块区分开 肝脏（例如转移性结直肠癌，肝癌），比HCC更常见，或者是来自HCC的血液 患有肝硬化患者的HCC患者没有HCC。最后，拟议的研究将有助于 确定从TCGA开发的机器学习算法是否可以检测到HCC是否已经 经过处理（例如化学栓塞），因此有可能用作监视的工具。总体而言，这些实验 将有助于确定使用TCGA开发的算法对HCC独立组的概括。 这些研究将为开发更有效的筛查和监视方案奠定基础 这将有望影响与HCC相关的重大发病率和死亡率。最后，如果这些研究是 成功，他们将鼓励使用微生物DNA作为早期检测生物标志物的探索 其他类型的癌症以及肿瘤细菌在癌症生物学中的作用。