Hybrid Glycoproteomic and Autoantibody Biomarkers for Lung Cancer Early Detection

项目摘要 肺癌是全球癌症死亡的主要原因。通常情况下，肺癌诊断较晚。 由于没有廉价、非侵入性和准确的筛查方法，因此无法在疾病的阶段进行筛查。低剂量 高风险吸烟者的CT筛查通过识别小的肺结节将肺癌死亡率降低了约20%， 但假阳性率为95%。为了有效地进行肺癌筛查，敏感性和特异性对 必须提高肺癌的检测水平。我们已经创建了一个混合血液生物标记物平台，可以识别 蛋白质组、血糖和自身抗体在多种癌症类型中的变化具有高度的敏感性和特异性， 包括肺脏。在这里，我建议使用我们的新平台来识别和验证混合生物标记物来检测 将肺癌早期作为筛查前或CT后以指导后续治疗(例如，支气管镜检查， 继续CT监视或手术活检)。我已经鉴定并验证了蛋白质组标记 在两组诊断前血浆样本中展示了在肺癌早期检测中的实用性。这些蛋白质组标记 将进一步分析血糖和自身抗体的变化(目标1)。此外，混合血浆生物标记物 将在我们的弗雷德·哈钦森肺癌早期检测诊所的诊断血浆样本中进行检测 (目标2)。在一个多学科的合作中，我识别的生物标志物将与肺结节整合在一起。 用于创建风险预测模型的特征，该模型可区分CT上发现的恶性和良性结节 成像。我还将通过探索细胞内糖基化的机制来进一步研究生物标记物生物学。 我们最好的外周生物标志物的蛋白质出口(目标3)。

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).