Hybrid Glycoproteomic and Autoantibody Biomarkers for Lung Cancer Early Detection

用于肺癌早期检测的混合糖蛋白组学和自身抗体生物标志物

• 批准号: 9406033
• 负责人: Kristin J Lastwika
• 金额: $ 0.26万
• 依托单位: FRED HUTCHINSON CANCER RESEARCH CENTER
• 依托单位国家: 美国
• 财政年份: 2017
• 资助国家: 美国
• 起止时间: 2017-01-01 至 2019-12-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9406033
• 关键词: Age; Age-Years; Antibodies; Antigens; Autoantibodies; Benign; Biological Assay; Biological Markers; Biology; Biopsy; Blood; Bronchoscopy; Cancer Etiology; Cancer Histology; Cancer Patient; Cessation of life; Chronic Obstructive Airway Disease; Clinic; Clinical; Clinical Data; Cohort Studies; Colon; Complement; Complex; Cytoplasm; Cytoplasmic Protein; Data; Development; Diagnosis; Diagnostic; Dimensions; Disease; Dose; Early Diagnosis; Emotional; Evaluation; Funding; Gender; Glycoproteins; Hybrids; Image; Incidence; Interdisciplinary Study; Laboratories; Lung; Lung nodule; Malignant - descriptor; Malignant Neoplasms; Malignant neoplasm of lung; Methods; Morbidity - disease rate; Nested Case-Control Study; Nodule; Operative Surgical Procedures; Ovarian; Pathway interactions; Patients; Performance; Peripheral; Plasma; Populations at Risk; Procedures; Production; Prostate; Proteins; Proteomics; Race; Radiation exposure; Rest; Risk; Risk Factors; Sampling; Sensitivity and Specificity; Severities; Signal Transduction; Smoker; Smoking History; Survival Rate; Testing; Tobacco use; Translations; Unnecessary Procedures; X-Ray Computed Tomography; aged; biomarker discovery; biomarker panel; blood-based biomarker; cancer biomarkers; cancer subtypes; cancer type; candidate marker; chest computed tomography; cigarette smoking; cohort; cost; exosome; follow-up; glycoproteomics; glycosylation; high risk; high risk population; improved; interest; low-dose spiral CT; lung cancer screening; lung imaging; mortality; novel; predictive modeling; programs; screening; sialyl Lewis a; standard care

Project Summary Lung cancer is the leading cause of cancer deaths worldwide. Typically, lung cancer is diagnosed at a late stage of disease since inexpensive, non-invasive and accurate screening methods are not available. Low-dose CT screening in high risk smokers reduces lung cancer mortality by ~20% by identifying small lung nodules, but at a 95% false positive rate. For lung cancer screening to be effective, the sensitivity and specificity to detect lung cancer must be improved. We have created a hybrid blood biomarker platform that identifies proteomic, glycomic and autoantibody changes with high sensitivity and specificity in multiple cancer types, including lung. Here, I propose to use our novel platform to identify and validate hybrid biomarkers to detect lung cancer early either as a pre-screen or after CT to guide follow-up treatment (e.g., bronchoscopy, continued CT surveillance or surgical biopsy). I have already identified and validated proteomic markers that show utility in lung cancer early detection in two pre-diagnostic plasma sample sets. These proteomic markers will be further analyzed for glycomic and autoantibody changes (Aim 1). Additionally, hybrid plasma biomarkers will be examined in diagnostic plasma samples from our Fred Hutchinson Lung Cancer Early Detection Clinic (Aim 2). In a multi-disciplinary collaboration, my identified biomarkers will be integrated with pulmonary nodule features to create a risk prediction model that distinguishes malignant from benign nodules identified on CT imaging. I will also delve further into biomarker biology by exploring the mechanism of intracellular glyco- protein export for our best peripheral biomarkers (Aim 3).

项目摘要 肺癌是全球癌症死亡的主要原因。通常，肺癌在晚期被诊断 疾病的阶段由于廉价，非侵入性和准确的筛查方法不可用。低剂量 高风险吸烟者中的CT筛查可通过识别小肺结节，降低肺癌死亡率约20％， 但是以95％的假阳性率。为了使肺癌筛查有效，对 必须改善检测肺癌。我们创建了一个识别的混合血液生物标志物平台 在多种癌症类型中，蛋白质组学，糖基酸和自身抗体的变化具有高灵敏度和特异性， 包括肺。在这里，我建议使用我们的新型平台识别和验证混合生物标志物来检测 早期作为屏幕前或CT之后的肺癌指导随访治疗（例如，支气管镜检查， 继续进行CT监视或手术活检）。我已经确定并验证了蛋白质组学标记 在两个诊断前血浆样品集中显示了肺癌早期检测的效用。这些蛋白质组学标记 将进一步分析糖和自身抗体变化（AIM 1）。此外，混合等离子体生物标志物 将在我们的Fred Hutchinson肺癌早期检测诊所的诊断等离子体样品中检查 （目标2）。在多学科的合作中，我确定的生物标志物将与肺结结合 创建风险预测模型的功能，该模型将恶性与CT上鉴定的良性结节区分开 成像。我还将通过探索细胞内糖的机制进一步研究生物标志物生物学 蛋白质出口以我们最好的外围生物标志物（AIM 3）。