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

 描述（由申请人提供）：肺癌是美国和全世界癌症死亡的主要原因。2013年，据估计，美国将有至少228，000例新的肺癌诊断病例和超过159，000例死亡-大约等于癌症相关死亡率的下四个最常见原因的总和（结肠癌，乳腺癌，前列腺癌，胰腺癌）。NCI赞助的国家肺筛查试验（NLST）发现，在接受低剂量胸部计算机断层扫描（CT）筛查的高危受试者中，肺癌特异性死亡率降低了20%。然而，26%的CT扫描报告了>4 mm的非钙化结节，而这些阳性结果中只有5%实际上被预期为癌症。对几个筛查队列的分析表明，25-50%的>50岁的吸烟者具有CT可识别的肺结节，但其中很少（约2.5%）是由肺癌引起的。肺结节评估的当前实践指南要求根据结节的大小和特征以及关键临床参数（例如，年龄、吸烟史），即使在这种高风险人群中也会引起相当大的成本/效益和发病率/死亡率考虑。显然，需要对CT成像检测到肺结节的受试者进行额外的风险分层。在血液中发现可行的蛋白质组学、糖组学和/或免疫学生物标志物以补充CT将对指导临床护理特别有价值。然而，没有血浆标记物在验证试验中得到足够的进展，成为FDA批准的可行候选物。我们创建了一个包含3200种不同抗体的高密度抗体阵列，用于在巢式病例对照设计研究中询问来自观察性试验的诊断前样本集，以评估蛋白质组学，糖组学和自身抗体差异。我们已经证明，这种新技术是高度敏感和可重复的。此外，我们已经证实了已知的和发现新的可行的蛋白质组生物标志物候选人在卵巢癌，乳腺癌，结肠癌和肺癌。使用来自心血管健康研究（CHS）的诊断前肺癌样本，我们发现了30种蛋白质组学，糖组学或自身抗体生物标志物，这些生物标志物在随后诊断为肺癌的人群中显著增加（p<0.002）。在这里，我们建议使用来自297名肺结节阳性受试者的血浆样本，这些受试者已经通过CT进行了筛查，并且具有已知的癌症/结节状态（147名是癌症），以测试这30种标志物，并可能发现其他候选者。然后，我们将联合收割机将这些数据与CT成像参数和临床数据相结合，以创建一个风险预测模型，我们将在类似规模的前瞻性收集的队列中进行测试。我们的具体目标是：（1）测试推定的蛋白质组学和糖组学生物标志物识别恶性肺结节的能力。(2)确定血浆中存在的自身抗体是否是肿瘤衍生的，并评估其用于检测癌性结节的效用。(3)对混合血浆生物标志物进行多变量分析，以区分CT胸部成像上识别的恶性和良性结节。

 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.