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: 9179001
• 负责人: Christian Maximillian Schmidt
• 金额: $ 20.39万
• 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 2018-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9179001
• 关键词: Adopted; Alkaline Phosphatase; Amylases; Benign; Biochemistry; 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; Early Diagnosis; Epigenetic Process; Excision; Genomics; Glucose; Glutamine; Glycolysis; Glycosylated Hemoglobin; 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; Serum; Side; Specificity; Staging; Surgical Pathology; Survival Rate; Universities; Unresectable; Work; abstracting; addiction; adiponectin; base; biomarker selection; cancer biomarkers; cancer diagnosis; carcinogenesis; cohort; disease diagnosis; evidence base; high risk; improved; innovation; metabolomics; molecular marker; mortality; neoplasm type; novel; pancreatic cancer cells; 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.

项目摘要/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被诊断出来 无法切除的阶段。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体病变。这样的病变是导管内乳头状的 粘液性肿瘤（IPMN），因为其中一些囊肿病变具有进展到侵入性癌症的潜力。 仅基于临床和放射学特征，预测和治疗恶性IPMN的当前指南 与最终的手术病理相比，具有令人满意的灵敏度（> 90％），但具有沮丧的特异性（25-30％）。 如此低的特异性导致了无法接受的高误报率，从而 良性IPMN中与手术相关死亡率相关的不必要的胰腺切除术。所以， 紧急未满足的需要确定分子生物标志物以改善恶性IPMN预测。这 拟议的研究的长期目标是改善临床IPMN管理并预防胰腺癌 通过识别恶性IPMN的危险因素或预测因素。主要目的是评估 IR生物标志物和代谢产物与恶性IPMN风险的关联。我们的核心假设是 生物标志物和代谢物签名可以预测基于我们的恶性IPMN风险 初步数据和广泛的文献综述。我们建议研究这部小说，但在生物学上是合理的 在印第安纳大学胰腺囊肿的400名IPMN患者中的假设 和癌症早期检测中心。其中，有118条被最终病理分类为恶性肿瘤，282分为 良性IPMN。我们的具体目的是：1。研究选定的等离子IR之间的关联 生物标志物和恶性IPMN风险。选定的IR生物标志物是C肽，分支链氨基酸， 瘦素，脂联素，视黄醇结合蛋白4和糖化血红蛋白的高分子重量形式； 2。 使用全球代谢组学鉴定血浆代谢物与良性IPMN区分开。 将测量800多个命名代谢物。将进行调解分析以评估是否和 通过IR机制鉴定出代谢产物的代谢产物预测恶性IPMN。 3。确定容量 将血浆IR生物标志物和代谢物（在AIM 1和2中鉴定）与临床病理学结合 预测恶性IPMN的特征。拟议的研究预计将识别IR生物标志物 和与恶性IPMN风险相关的代谢产物，并证明了整合的模型 IR生物标志物和具有临床病理特征的代谢物比具有恶性IPMN比 仅依赖临床病理数据的模型。我们的预期结果很大，因为他们会告知 医师进行基于证据的临床IPMN管理和开放新途径以防止这种情况 前体病变和随后的胰腺癌。我们的建议是创新的，因为恶性IPMN 将从基于途径的和全球实践的方法中确定预测因子。