Hybrid Plasma Markers that Complement CT Imaging for Early Lung Cancer Detection

混合血浆标记物可补充 CT 成像以进行早期肺癌检测

• 批准号: 9248994
• 负责人: A McGarry Houghton
• 金额: $ 51.64万
• 依托单位: FRED HUTCHINSON CANCER RESEARCH CENTER
• 依托单位国家: 美国
• 财政年份: 2015
• 资助国家: 美国
• 起止时间: 2015-04-01 至 2020-03-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9248994
• 关键词: Adherence; Age; Age-Years; Algorithms; Antibodies; Antigens; Apoptosis; Attention; Autoantibodies; B-Lymphocytes; Benign; Biological Markers; Biopsy; Blood; Breast; Cancer Etiology; Cancer Histology; Cancerous; Caring; Cells; Cessation of life; Characteristics; Chest; Chronic Obstructive Airway Disease; Clinic; Clinical; Clinical Data; Colon; Colon Carcinoma; Complement; Complex; Costs and Benefits; Data; Detection; Diagnosis; Diagnostic; Dimensions; Dose; Enzyme-Linked Immunosorbent Assay; Evaluation; FDA approved; Gender; Guidelines; Hybrids; Image; Immune; Immunologic Markers; Infiltration; Lung; Lung nodule; Malignant - descriptor; Malignant Neoplasms; Malignant neoplasm of lung; Malignant neoplasm of ovary; Medical; Metabolism; Methods; Modeling; Morbidity - disease rate; Multivariate Analysis; Nodule; Operative Surgical Procedures; Pancreas; Patient Care; Patients; Plasma; Practice Guidelines; Procedures; Property; Prostate; Protein Array; Proteomics; Race; Radiation exposure; Reporting; Reproducibility; Research Design; Rest; Risk stratification; Sampling; Scanning; Severities; Smoker; Smoking History; Specificity; Specimen; Survival Rate; Testing; Translations; Tumor-Derived; Tumor-Infiltrating Lymphocytes; United States; Unnecessary Procedures; Validation; Variant; X-Ray Computed Tomography; aged; angiogenesis; base; biomarker discovery; biomarker panel; blood-based biomarker; cancer cell; cancer diagnosis; candidate marker; cardiovascular health; case control; chest computed tomography; clinical care; cohort; cost; density; design; high risk; high risk population; improved; lung cancer screening; lung imaging; malignant breast neoplasm; mortality; new technology; outcome forecast; performance tests; predictive modeling; programs; prospective; public health relevance; screening; standard care; theories

 DESCRIPTION (provided by applicant): Lung cancer is the leading cause of cancer death in the United States and worldwide. In 2013, it is estimated that there will be at least 228,000 new cases of lung cancer diagnosed and more than 159,000 deaths in the United States - approximately equal to the next four most common causes of cancer-related mortality combined (colon, breast, prostate, pancreas). The NCI-sponsored, National Lung Screening Trial (NLST) found a 20% reduction in lung cancer specific mortality in high-risk subjects screened with low-dose chest computed tomography (CT). However, 26% of CT-scans reported noncalcified nodules >4 mm while only 5% of these positive findings would actually be expected to be cancer. Analysis of several screening cohorts indicates that 25-50% of smokers >50 years of age have CT identifiable pulmonary nodules but very few of them (~2.5%) are caused by lung cancer. Current practice guidelines for pulmonary nodule evaluation call for invasive biopsy procedures depending upon the size and characteristics of the nodule and key clinical parameters (e.g., age, smoking history), raising considerable cost/benefit and morbidity/mortality considerations even in this high-risk population. Clearly there is a need for additional risk stratification for subjects that have pulmonary nodules detected by CT imaging. Discovery of viable proteomic, glycomic and/or immunological biomarkers in blood to complement CT would be especially valuable to guide clinical care. However, no plasma markers have advanced sufficiently in validation trials to be viable FDA-approved candidates. We created a high density antibody array containing 3200 different antibodies that we use to interrogate pre-diagnostic sample sets from observational trials in a nested case-control design study to evaluate proteomic, glycomic and autoantibody differences. We have shown that this novel technology is highly sensitive and reproducible. Furthermore, we have confirmed known and found new viable proteomic biomarker candidates in ovarian, breast, colon and lung cancer. Using pre-diagnostic lung cancer samples from the Cardiovascular Health Study (CHS), we found 30 proteomic, glycomic or autoantibody biomarkers that were significantly increased (p<0.002) in people that are subsequently diagnosed with lung cancer. Here, we propose to use plasma samples from 297 lung nodule positive subjects that have been screened via CT and have known cancer/nodule status (147 were cancer) to test these 30 markers and potentially discover additional candidates. We will then combine these data with CT imaging parameters and clinical data to create a risk prediction model that we will test in a similar sized prospectivly collected cohort. Our specific aims are: (1) Test the ability of putative proteomic and glycomic biomarkers to identify malignant pulmonary nodules. (2) Determine if autoantibodies present in plasma are tumor-derived and assess their utility for the detection of cancerous nodules. (3) Perform multivariate analyses of hybrid plasma biomarkers to distinguish malignant from benign nodules identified on CT chest imaging.

 描述（由适用提供）：肺癌是美国和全球癌症死亡的主要原因。 2013年，据估计，在美国，至少有228,000例被诊断出的肺癌的新病例和159,000多人死亡 - 大约等于接下来的四个最常见的与癌症相关死亡率的最常见原因（结肠，乳腺，前列腺，前列腺，胰腺）。 NCI赞助的国家肺筛查试验（NLST）发现，在使用低剂量胸部计算机断层扫描（CT）筛查的高风险受试者中，肺癌特异性死亡率降低了20％。但是，有26％的CT扫描报告不钙化的淋巴结> 4 mm，而实际上只有5％的这些阳性结果预计将是癌症。对几个筛查队列的分析表明，> 50岁的吸烟者中有25-50％的吸烟者具有可识别的肺结核，但其中很少有肺癌引起的肺部结节（约2.5％）。当前的肺节点评估练习指南呼吁进行侵入性活检程序，具体取决于节点和关键临床参数的大小和特征（例如，年龄，吸烟史），提高了考虑成本/福利和发病率/死亡率的考虑因素，即使在此高危人群中。显然，对于通过CT成像检测到的肺部淋巴结的受试者需要额外的风险分层。在血液中发现可行的蛋白质组学，糖基因组和/或免疫生物标志物到完成CT对于指导临床护理特别有价值。但是，在验证试验中，没有足够的血浆标记是可行的FDA批准候选者。我们创建了一个高密度抗体阵列，其中包含3200种不同的抗体，用于在嵌套的病例对照设计研究中从观察性试验中询问诊断前样品集，以评估蛋白质组学，糖基因组和自身抗体差异。我们已经表明，这种新型技术是高度敏感和可再现的。此外，我们已经确认了已知的并发现了卵巢，乳腺癌，结肠和肺癌的新的可行蛋白质组学生物标志物候选者。使用心血管健康研究（CHS）的诊断前肺癌样本，我们发现30种蛋白质组学，糖基糖或自身抗体生物标志物显着增加。 （p <0.002）随后被诊断出患有肺癌的人。在这里，我们建议使用来自297个肺结核阳性受试者的血浆样本，这些受试者已通过CT进行筛选，并具有已知的癌症/结节状态（147个是癌症）来测试这30个标记，并可能发现其他候选者。然后，我们将将这些数据与CT成像参数和临床数据相结合，以创建一个风险预测模型，我们将在类似尺寸的前瞻性收集的队列中进行测试。我们的具体目的是：（1）测试假定的蛋白质组学和糖核生物标志物鉴定恶性肺结核的能力。 （2）确定血浆中存在的自身抗体是否是肿瘤来源的，并评估其检测取消结节的效用。 （3）对杂化等离子体生物标志物进行多元分析，以区分恶性肿瘤与CT胸部成像上鉴定的良性结节。