Proteomic, Glycomic and Autoantibody Lung Cancer Biomarker Validation

描述(申请人提供)：肺癌是美国和世界范围内癌症死亡的主要原因。据估计，2013年，美国将至少新增22.8万例肺癌确诊病例，超过15.9万人死亡--大约相当于接下来四种最常见的癌症相关死亡原因(结肠癌、乳腺癌、前列腺癌和胰腺)的总和。肺癌早期发现的潜在好处是显而易见的--如果在早期发现，肺癌的5年生存率超过50%，而转移性肺癌患者中只有3.7%的人能存活这么长时间。到目前为止，大多数筛查方法的随机对照试验，如胸部X光检查和痰细胞学检查，都没有证明对肺癌死亡率有任何影响。此外，还没有蛋白质组或基因组血浆标志物在验证试验中进展到足以成为FDA批准的广泛筛查的可行候选者。发现可存活的肺癌早期检测血液中的蛋白质组、血糖和/或免疫生物标记物单独或结合低剂量胸部计算机断层扫描(CT)效果良好将特别有价值，因为肺癌很常见，通常是致命的，而且治疗费用昂贵-因此它具有很高的社会成本。我们采取了一种独特的方法来发现生物标记物，旨在克服目前的障碍。我们创建了一个包含3200种不同抗体的高密度抗体阵列，用于在嵌套病例对照设计研究中询问来自多个观察性试验的诊断前样本集(即数百个病例和对照匹配的诊断前样本)，以评估蛋白质组、血糖和自身抗体的差异。由于我们的发现方法使用了高亲和力抗体，我们表现最好的标志物可以很容易地转移到类似于ELISA法的方法中，便于正式验证和进入临床。我们已经证明，这项技术是高度敏感的(低皮卡水平)和重复性(大多数变异系数&lt；10%)。此外，我们还确认了卵巢癌、乳腺癌、结肠癌和肺癌中新的可行的蛋白质组生物标记物候选。使用来自心血管健康研究的肺癌诊断前样本，我们发现在被诊断为肺癌的人中，有30个蛋白质组、血糖或自身抗体生物标志物显著增加(p&lt；0.002)。在这里，我们建议使用前列腺癌、肺癌、结直肠癌和卵巢癌(PLCO)筛查试验的样本来验证这些候选对象。我们的具体目标是：(1)使用PLCO样本初步验证30个潜在的肺癌早期检测蛋白质组、血糖或自身抗体生物标记物候选对象(例如，具有&gt；2倍增加的，p&lt；0.002，AUC&gt；0.67)。(2)开发和评估一种新的风险预测模型，将目标1中评估的生物标记物与多种临床和流行病学因素相结合，包括肺癌亚型、吸烟史、年龄、性别、体重指数、肺癌和其他癌症的家族史、辐射、石棉和其他暴露，这些因素可能会影响生物标记物的表现和风险预测。

DESCRIPTION (provided by applicant): Lung cancer is the leading cause of cancer death in the United States and world-wide. 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 potential benefits of lung cancer early detection are clear - when discovered at an early stage, lung cancer has 5 year survival rates over 50% while only 3.7% of metastatic lung cancer patients survive that long. To date, randomized controlled trials of most screening modalities such as chest x-ray and sputum cytology have not demonstrated any impact on lung cancer mortality. Also, no proteomic or genomic plasma markers have advanced sufficiently in validation trials to be viable FDA-approved candidates for widespread screening. Discovery of viable lung cancer early detection proteomic, glycomic and/or immunological biomarkers in blood that would work well on their own or in combination with low-dose chest computed tomography (CT) would be especially valuable since lung cancer is common, most often fatal and is expensive to treat - so it has high societal costs. We have taken a unique approach to biomarker discovery designed to overcome current obstacles. We created a high density antibody array containing 3200 different antibodies that we use to interrogate pre-diagnostic sample sets from multiple observational trials (i.e., hundreds of pre-diagnostic samples with well-matched cases and controls) in a nested case-control design study to evaluate proteomic, glycomic and autoantibody differences. Since our discovery method uses high affinity antibodies, our best performing markers can be readily transferred to ELISA-like methods facilitating formal validation and movement into the clinic. We have shown the technology is highly sensitive (low picogram levels) and reproducible (most coefficients of variation <10%). 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 diagnosed with lung cancer. Here, we propose to use samples from the Prostate, Lung, Colorectal, and Ovarian (PLCO) Cancer Screening Trial to validate these candidates. Our specific aims are: (1) To use PLCO samples to preliminarily validate 30 potential lung cancer early detection proteomic, glycomic or autoantibody biomarker candidates (e.g., those with >2 x increases, p<0.002, AUC > 0.67). (2) To develop and assess the performance of a novel risk prediction model integrating the biomarkers assessed in Aim 1 with a variety of clinical and epidemiologic factors, including lung cancer subtype, smoking history, age, gender, body mass index, family history of lung and other cancers, radiation, asbestos and other exposures, that should potentially impact biomarker performance and risk prediction.