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: 10357369
• 负责人: Amir Zarrinpar
• 金额: $ 22.16万
• 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 2024-01-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10357369
• 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; Hepatic Mass; Hepatitis B Virus; 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; base; 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 虽然肝细胞癌在美国最常见的癌症中排名第14位，但它在最常见的癌症中排名第五 常见癌症死亡原因，5年生存率19.6%。在美国，肝细胞癌的发病率正在上升 它的年死亡率为4.5%，是过去十年中死亡率唯一上升的癌症。 目前，检测肝细胞癌的唯一方法是对肝硬变和甲型肝炎患者进行监测成像。 胎蛋白(AFP)。然而，大约13%-20%的被诊断为肝细胞癌的患者没有肝硬化，并且 因此，他们从未对这种疾病进行过筛查。此外，大约30%-40%的患者AFP正常。 肝细胞癌。正因为如此，肝细胞癌通常是在晚期才被发现，当时只有有限的数量 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- 栖息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 都是微生物。使用这些微生物的机器学习算法从 彼此之间以及来自相邻对照组织的。此外，这种准确性在血液样本中得到了很大程度的保持。 是用来代替肿瘤的。这一初步数据对肝细胞癌来说尤其强劲。 基于这些初步数据，拟议的研究将推进早期生物标记物的开发 以多种方式检测肝细胞癌。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队列。这个生物库有几乎同样多的肝癌 作为整个TCGA网络的样本。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。这包括使用非肝癌肝脏恶性肿瘤和良性肿瘤。 鉴于机器学习算法在区分肝细胞癌肿块和其他原发肿瘤方面是足够的 TCGA中的肿瘤，尚不清楚它们是否能将肝癌与其他恶性和良性肿块区分开来 比肝细胞癌更常见的肝脏(例如转移性结直肠癌、肝癌)，或来自 肝细胞癌患者与无肝细胞癌的肝硬变患者相比，差异有统计学意义。最后，建议的研究将会有所帮助。 确定从TCGA开发的机器学习算法是否可以检测出肝癌是否已经 治疗(例如，化疗栓塞术)，因此有可能成为监测的工具。总体而言，这些实验 将有助于确定用TCGA开发的算法在多大程度上适用于肝癌的独立队列。 这些研究将为开发更有效的筛查和监测方案奠定基础 这有望对与肝细胞癌相关的显著发病率和死亡率产生影响。最后，如果这些研究是 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，以及肿瘤细菌在癌症生物学中的作用。