Detecting and locating cancer for patients with CT-detected lung nodules

项目摘要 低剂量计算机断层扫描（LDCT）已被证明可以将肺癌死亡率降低20%， 高风险的吸烟者和前吸烟者。然而，NLST中有25%的受试者表现出异常 并且这些病变的大部分被确定为假阳性。有一个未得到满足的需要， 准确和非侵入性地识别早期侵袭性肺癌， 威胁着那些没有。最近，人类血液中的无细胞DNA（cfDNA）已经成为一种理想的方法。 癌症检测的来源。在本提案中，我们开发了一个集成系统CancerRadar，包括（1） 一种用于cfDNA的具有成本效益的全基因组甲基化分析的实验测定cfMethyl-Seq， 在分析CpG岛中，超过全基因组亚硫酸氢盐测序（WGBS）10倍富集;和（2）a 从cfMethyl-Seq数据中提取各种信息的计算框架，包括cfDNA甲基化， cfDNA片段大小、拷贝数变异（CNV）和微生物组成，并进行多特征分析。 用于检测恶性肺结节并定位其原发肿瘤部位的集成学习。我们将验证 CancerRadar与几个临床队列。相比常用的小面板集中在一个类型 标记，CancerRadar概况和整合多个遗传/表观遗传特征的全基因组概况， 因此可以稳健地捕获非常小比例的肿瘤来源的cfDNA片段， 诊断具有异质性癌症发病机制的患者，并学习和利用新的重要特征， 训练样本量增长。

PROJECT SUMMARY Low-Dose Computed Tomography (LDCT) has been demonstrated to reduce lung cancer mortality by 20% for high-risk current and former smokers. However, 25% of the subjects in the NLST demonstrated abnormalities and a large fraction of those lesions were determined to be false-positives. There is an unmet need to accurately and non-invasively identify early-stage aggressive lung cancers and distinguish lesions that are life threatening from those that are not. Recently cell-free DNA (cfDNA) in human blood has emerged as an ideal source for cancer detection. In this proposal, we develop an integrated system, CancerRadar, consisting of (1) an experimental assay, cfMethyl-Seq, for cost-effective genome-wide methylation profiling of cfDNA, offering >10 fold enrichment over Whole Genome Bisulfite Sequencing (WGBS) in profiling CpG islands; and (2) a computational framework to extract various information from cfMethyl-Seq data, including cfDNA methylation, cfDNA fragment size, copy number variation (CNV), and microbial composition, and perform multi-feature ensemble learning for detecting malignant lung nodule and locating its primary tumor sites. We will validate CancerRadar with several clinical cohorts. Compared to the commonly used small panels focusing on one type of markers, CancerRadar profiles and integrates genome-wide profiles of multiple genetic/epigenetic features, therefore can robustly capture the very small proportion of tumor-derived cfDNA fragments, comprehensively diagnose patients with heterogeneous cancer pathogenesis, and learn and exploit newly significant features as training sample size grow.