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

基本信息

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
项目类别：
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

项目摘要

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.

项目概要/摘要尽管肝细胞癌 (HCC) 是美国第 14 位最常见癌症1，但它在最常见癌症中排名第 5 位。癌症死亡的常见原因，5 年生存率为 19.6%。美国肝癌的发病率正在上升每年以4.5%的速度增长，它是过去十年中唯一死亡率上升的癌症。目前，检测 HCC 的唯一方法是对肝硬化和甲型肝炎患者进行监测成像。甲胎蛋白（AFP）。然而，大约 13-20% 诊断为 HCC 的患者没有肝硬化，并且因此从未进行过这种疾病的筛查。此外，大约 30-40% 的 AFP 患者是正常的肝癌。因此，HCC 通常在晚期才被检测到，此时肝癌的数量有限。治疗选择。这项提议的诞生是基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可以准确识别实体瘤彼此以及来自邻近的对照组织。此外，当血液样本用来代替肿瘤。该初步数据对于 HCC 来说尤其可靠。基于这些初步数据，拟议的研究将推动早期生物标志物的开发 HCC 的检测有多种方式。首先，我们将使用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。该生物样本库的 HCC 数量几乎与样本作为整个 TCGA 网络。其次，拟议的研究将使用比现有研究更严格的控制已在 TCGA 网络中提供。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 肿块和其他原发灶 TCGA 中的肿瘤，目前尚不清楚它们是否能够将 HCC 与其他恶性和良性肿块区分开来肝脏（例如，转移性结直肠癌、肝癌），比 HCC 更常见，或来自患有 HCC 的患者与没有 HCC 的肝硬化患者。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 HCC 是否已发生治疗（例如化疗栓塞），因此有可能作为监测工具。总的来说，这些实验将有助于确定使用 TCGA 开发的算法对独立 HCC 群体的通用性。这些研究将为制定更有效的筛查和监测方案奠定基础这有望影响与 HCC 相关的显着发病率和死亡率。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，以及肿瘤内细菌在癌症生物学中的作用。