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/摘要 虽然肝细胞癌（HCC）是美国第14大最常见的癌症，但它是第五大最常见的癌症。 是癌症死亡的常见原因，5年生存率为19.6%。在美国，HCC的发病率正在上升， 年发病率为4.5%，是过去十年中死亡率上升的唯一癌症。 目前，检测HCC的唯一方法是在肝硬化和α-SMA患者中进行监视成像。 甲胎蛋白（AFP）。然而，大约13-20%的诊断为HCC的患者没有肝硬化， 因此从未接受过疾病筛查。此外，AFP在大约30-40%的患有急性白血病的患者中是正常的。 HCC。正因为如此，HCC通常在晚期被检测到，此时存在有限数量的 治疗选择这一提议源于令人信服的初步数据，这些数据表明来自肿瘤的DNA- 居住的微生物可以识别恶性肿瘤。癌症基因组图谱（TCGA）中约2.5%的读数 是微生物。使用这些微生物读数的机器学习算法准确地识别了实体瘤， 彼此和相邻对照组织。此外，当血液样本 而不是肿瘤。这一初步数据对于HCC尤其可靠。 基于这些初步数据，拟议的研究将推进早期生物标志物的开发 肝癌的检测有几种方法。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一组新样本。这个生物样本库中的肝癌数量 作为整个TCGA网络的样本。第二，拟议的研究将使用更严格的控制， 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。 然而，机器学习算法足以区分HCC肿块与其他原发性肿瘤。 尽管在TCGA中的肿瘤，但尚不清楚它们是否能区分HCC与其他恶性和良性肿块， 肝脏（例如，转移性结直肠癌，肝细胞瘤），比HCC更常见，或来自 肝癌患者与无肝癌的肝硬化患者。最后，拟议的研究将有所帮助 确定从TCGA开发的机器学习算法是否可以检测HCC是否已经被 处理过的（例如，化疗栓塞）并因此潜在地用作监视的工具。总的来说，这些实验 将有助于确定TCGA开发的算法对HCC独立队列的可推广性。 这些研究将为制定更有效的筛查和监测方案奠定基础 这将有望影响与HCC相关的显著发病率和死亡率。最后，如果这些研究 如果成功，他们将鼓励探索使用微生物DNA作为早期检测生物标志物， 其他类型的癌症，以及肿瘤驻留细菌在癌症生物学中的作用。