Identifying insulin resistance biomarkers and metabolomic signature as predictors of precursors to pancreatic cancer

识别胰岛素抵抗生物标志物和代谢组学特征作为胰腺癌前兆的预测因子

• 批准号: 9357552
• 负责人: Christian Maximillian Schmidt
• 金额: $ 17.13万
• 依托单位: INDIANA UNIV-PURDUE UNIV AT INDIANAPOLIS
• 依托单位国家: 美国
• 财政年份: 2016
• 资助国家: 美国
• 起止时间: 2016-09-23 至 2020-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9357552
• 关键词: Adopted; Alkaline Phosphatase; Amylases; Benign; Biochemistry; Biological; Biological Markers; Branched-Chain Amino Acids; C-Peptide; CA-19-9 Antigen; Caliber; Cancer Etiology; Cancer Patient; Cessation of life; Characteristics; Clinical; Clinical/Radiologic; Consumption; Cyst; Cytopathology; Data; Diabetes Mellitus; Dose; Epigenetic Process; Etiology; Excision; Genomics; Glucose; Glutamine; Glycolysis; Glycosylated Hemoglobin; Glycosylated hemoglobin A; Goals; Guidelines; Incidence; Indiana; Insulin; Insulin Resistance; Leptin; Lesion; Life Style; Lipase; Main pancreatic duct; Malignant - descriptor; Malignant Neoplasms; Malignant neoplasm of pancreas; Measures; Mediation; Metabolic; Modeling; Molecular; Molecular Weight; Mucinous Neoplasm; Names; Obesity; Operative Surgical Procedures; Pancreas; Pancreatic Cyst; Papillary; Pathology; Pathway interactions; Patients; Pentosephosphate Pathway; Phenotype; Physicians; Plasma; Post-Translational Protein Processing; Proteomics; RBP4 gene; Research Design; Review Literature; Risk; Risk Factors; Running; Scanning; Screening for cancer; Serum; Side; Specificity; Surgical Pathology; Survival Rate; Time; Universities; Unresectable; addiction; adiponectin; base; biomarker selection; cancer biomarkers; cancer diagnosis; cancer invasiveness; carcinogenesis; cohort; disease diagnosis; evidence base; high risk; improved; innovation; metabolomics; molecular marker; mortality; neoplasm type; novel; pancreatic cancer cells; polypeptide C; predictive marker; prevent; public health relevance; resistance mechanism; response; sedentary lifestyle

Project Summary/Abstract Pancreatic cancer is a leading cause of cancer death. Most patients with this disease are diagnosed at a late, unresectable stage. An effective strategy to reduce the incidence and mortality of pancreatic cancer is the timely identification and optimal treatment of its precursor lesions. One such lesion is intraductal papillary mucinous neoplasm (IPMN) because some of these cyst lesions have potential to progress to invasive cancer. Based solely on clinical and radiologic features, current guidelines for predicting and treating malignant IPMN have a satisfactory sensitivity (>90%) but a dismal specificity (25-30%), compared with final surgical pathology. Such a low specificity has resulted in an unacceptable high false positive rate and hereby a number of unnecessary pancreatic resections associated with surgically-related mortality in benign IPMN. Therefore, there is an urgent unmet need to identify molecular biomarkers to improve malignant IPMN prediction. The long-term goal of the proposed study is to improve clinical IPMN management and prevent pancreatic cancer by identifying risk factors or predictors for malignant IPMN. The primary objective is to evaluate the associations of IR biomarkers and metabolites with malignant IPMN risk. Our central hypothesis that IR biomarkers and metabolite signature can predict malignant IPMN risk has been formulated on the basis of our preliminary data and extensive literature review. We propose to investigate this novel but biologically plausible hypothesis among 400 IPMN patients who have undergone surgery at the Indiana University Pancreatic Cyst and Cancer Early Detection Center. Of these, 118 were classified by final pathology as malignant and 282 as benign IPMNs. Our Specific Aims are: 1. Investigate the associations between selected plasma IR biomarkers and malignant IPMN risk. Selected IR biomarkers are C-peptide, branched-chain amino acids, leptin, high-molecular weight form of adiponectin, retinol binding protein-4, and glycated hemoglobin; 2. Identify plasma metabolites distinguishing malignant from benign IPMN using global metabolomics. More than 800 named metabolites will be measured. Mediation analysis will be run to evaluate whether and to what extent identified metabolites predict malignant IPMN through IR mechanism. 3. Determine the capacity of combining plasma IR biomarkers and metabolites (identified in Aims 1 and 2) with clinicopathologic characteristics for predicting malignant IPMN. The proposed study is expected to identify the IR biomarkers and metabolites that are associated with malignant IPMN risk and demonstrate that the model that integrates IR biomarkers and metabolites with clinicopathologic features is more predictive of malignant IPMN than the model that relies solely on clinicopathologic data. Our expected results are significant because they will inform physicians to make evidence-based clinical IPMN management and open new avenues for preventing this precursor lesion and ensuing pancreatic cancer. Our proposal is innovative because malignant IPMN predictors will be identified from both pathway-based and global-profiling approaches.

项目总结/摘要 胰腺癌是癌症死亡的主要原因。大多数患有这种疾病的患者都是在很晚的时候被诊断出来的， 不可切除期降低胰腺癌发病率和死亡率的有效策略是 及时识别和最佳治疗其前驱病变。其中一种病变是导管内乳头状病变 粘液性肿瘤（IPMN），因为这些囊肿病变中的一些有可能进展为浸润性癌症。 仅基于临床和放射学特征，预测和治疗恶性IPMN的现行指南 与最终手术病理相比，具有令人满意的敏感性（>90%），但特异性（25-30%）令人沮丧。 如此低的特异性导致了不可接受的高假阳性率，从而导致了许多不确定性。 不必要的胰腺切除与良性IPMN的胰腺相关死亡率相关。因此，我们认为， 迫切需要鉴定分子生物标志物以改善恶性IPMN的预测。的 该研究的长期目标是改善临床IPMN管理和预防胰腺癌 通过识别恶性IPMN的危险因素或预测因子。主要目的是评估 IR生物标志物和代谢物与恶性IPMN风险的相关性。我们的核心假设是， 生物标志物和代谢物签名可以预测恶性IPMN风险已经制定了我们的基础上， 初步数据和广泛的文献综述。我们建议研究这种新颖但在生物学上合理的 在印第安纳州大学胰腺囊肿医院接受手术的400名IPMN患者中， 癌症早期检测中心其中，118例经最终病理学分类为恶性，282例为恶性。 良性IPMN。我们的具体目标是：1。研究选定的血浆IR 生物标志物和恶性IPMN风险。选择的IR生物标志物是C-肽、支链氨基酸、 瘦素、脂联素的高分子量形式、视黄醇结合蛋白-4和糖化血红蛋白; 2. 使用全局代谢组学识别区分恶性和良性IPMN的血浆代谢物。 将测量800多种命名的代谢物。将进行调解分析，以评估是否 代谢产物在何种程度上通过IR机制预测恶性IPMN。3.确定容量 将血浆IR生物标志物和代谢物（在目的1和2中鉴定）与临床病理学 预测恶性IPMN的特征。这项拟议的研究预计将确定IR生物标志物 以及与恶性IPMN风险相关的代谢物，并证明整合了 IR生物标志物和代谢产物与临床病理特征相比更能预测恶性IPMN。 这是一个完全依赖于临床病理学数据的模型。我们的预期结果是重要的，因为它们将告知 医生进行循证临床IPMN管理，并为预防这种疾病开辟新途径 前驱病变和随后的胰腺癌。我们的建议是创新的，因为恶性IPMN 预测因素将从基于路径的方法和全球概况分析方法中确定。