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

项目摘要（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 肺癌的早期阶段，但大量的患者与非恶性肺结节，往往是 进行侵入性诊断。即使手术切除早期非小细胞肺 非小细胞肺癌（NSCLC）是最有效的治疗方法，但术后复发仍然是一个重要问题 作为生存目前还没有临床上有用的生物标志物可以准确地诊断不确定的 结节或识别那些在成功手术切除后注定癌症复发的患者。 最近，我们和其他人使用非培养技术来表征微生物组， 在一个患有肺癌的队列中鉴定与肺癌诊断和预后相关的微生物特征 各种疾病阶段。初步的宏基因组数据是与Escherichoma合作使用 我们的纽约大学队列的血液样本已经确定了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，下气道 标记可用于预测早期癌症手术切除后的预后。这些数据表明 微生物和宿主的基因组特征可以用来在早期阶段开发有用的生物标志物， NSCLC。添加代谢物测量可以进一步有助于这种预测能力，因为这些 是微生物和宿主功能的最终产物。在此BCC应用程序中，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 来自患有肺结节和假定的外科临床I期（<3cm）但患有肺结节的患者队列的样本， 最终组织学诊断为早期NSCLC（TNM分期）或非NSCLC结节。我们将实施削减 边缘生物信息学方法，以确定最有前途的目标，从这些公正的组学方法 （宏基因组，代谢组和转录组），这将指导靶向方法的发展， 在生物标志物参考实验室进行验证。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水平测定，内部和外部验证， NSCLC诊断和完全手术切除后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.