Microbial and host biomarker development for detection and prognosis of early stage non-small cell lung cancer

项目概要（总体） 肺癌仍然是所有癌症死亡的主要原因。改进的成像技术使检测成为可能 早期肺癌的发病率很高，但大量患有非恶性肺结节的患者经常被诊断为肺癌。 接受侵入性诊断方法。即使手术切除早期非小细胞肺 癌症（NSCLC）是最有效的治疗方法，NSCLC 术后复发仍然是一个重大问题 作为生存。目前尚无临床上有用的生物标志物可以准确诊断不确定性 结节或识别那些在成功手术切除后注定会出现癌症复发的患者。 最近，我们和其他人使用独立于培养的技术来表征微生物组，这导致了 鉴定与肺癌诊断和预后相关的微生物特征 多种疾病阶段。与 Micronoma 合作获得的初步宏基因组数据 我们纽约大学队列的血液样本已经确定了与以下疾病相关的体循环中的微生物特征： 早期 NSCLC 诊断。此外，使用 NanoString 平台，我们已经鉴定了循环 RNA 预测早期 NSCLC 诊断的特征。此外，我们的初步数据显示，下呼吸道 特征可用于预测早期癌症手术切除后的预后。这些数据表明 可以利用微生物和宿主基因组特征在早期开发有用的生物标志物 非小细胞肺癌。添加代谢物测量可以进一步增强这种预测能力，因为这些 是微生物和宿主功能的最终产物。在此 BCC 应用程序下，我们将首先确定顶部 使用血液和下呼吸道预测早期 NSCLC 诊断和预后的微生物/宿主生物标志物 样本来自一组患有肺结节且推测为外科临床 I 期（<3cm）但具有 早期 NSCLC（TNM ≤ IIIA）或非 NSCLC 结节的最终组织学诊断。我们将实施切割 边缘生物信息学方法从这些公正的组学方法中识别出最有希望的目标 （宏基因组、代谢组和转录组），这将指导有针对性的方法的开发 在生物标志物参考实验室下进行验证。这些有针对性的方法将包括 开发靶向微生物 DNA 下一代测序、靶向代谢物测量和 定制的 NanoString 面板作为 CLIA 水平检测，经过内部和外部验证，将识别 完全手术切除后非小细胞肺癌诊断和复发风险最高的患者。

Project summary (OVERALL) Lung cancer remains the leading cause of all cancer mortality. Improved imaging techniques enable the detection of lung cancer at earlier stages, yet a large number of patients with non-malignant lung nodules are frequently subjected to invasive diagnostic approaches. Even though surgical removal of early stage non-small cell lung cancer (NSCLC) is the most effective therapy, post-surgical recurrence of NSCLC remains a significant problem as survival. Currently there are no clinically useful biomarkers that can accurately diagnose the indeterminate nodule or identify those patients destined to have recurrence of cancer after successful surgical removal. Recently, the use of culture-independent techniques to characterize the microbiome by us and others has led to identification of microbial signatures associated with lung cancer diagnosis and prognosis among a cohort with a wide range of disease stages. Preliminary metagenomic data obtained in collaboration with Micronoma using blood samples of our NYU cohort have identified microbial signatures in systemic circulation associated with early-stage NSCLC diagnosis. Further, using a NanoString platform we have identified circulating RNA signatures predictive of early-stage NSCLC diagnosis. In addition, our preliminary data shows that lower airway signatures can be used to predict prognosis post-surgical removal of early stage cancer. These data suggest that microbial and host genomic signatures could be leveraged to develop useful biomarkers in early-stage NSCLC. The addition of metabolite measurements could further contribute to this predictive power since those are end products of microbial and host functions. Under this BCC application we will first identify top microbial/host biomarkers that predict early-stage NSCLC diagnosis and prognosis using blood and lower airway samples from a cohort of patients with lung nodules and a presumed surgical clinical Stage I (<3cm) but with a final histological diagnosis of early-stage NSCLC (TNM  IIIA) or non-NSCLC nodules. We will implement cutting edge bioinformatic approaches to identify the most promising targets from these unbiased omic approach (metagenome, metabolome and transcriptome) which will guide the development of targeted approaches that to be validated under the Biomarker Reference Laboratory. These targeted approaches will include the development of targeted microbial DNA next generation sequencing, targeted metabolite measurement and custom-made NanoString panels as CLIA level assays, internally and externally validated, that will identify patients at highest risk for NSCLC diagnosis and recurrence after complete surgical resection.