Biomarker Development Laboratory

项目总结(BDL) 最近，使用独立于培养的技术来表征微生物组已经导致了对 体循环中的微生物特征与肺癌诊断和预后相关。 在这项提案中，顾问们描述了识别微生物DNA的鸟枪式元基因组学如何 被用来识别血浆中预测不同癌症的微生物特征，包括肺癌。我们的 来自纽约大学EDRN档案的初步数据显示，元基因组签名可以预测早期- 分期非小细胞肺癌(NSCLC)与非NSCLC的比较。在这个队列中，我们还发现了 下呼吸道的微生物和宿主转录特征与预后(复发)有关。 这些数据支持微生物和宿主基因组特征可用于开发新的生物标记物 这种疾病的早期阶段。OMIC方法可以以不偏不倚的方式探索这些签名，允许 确定最佳表现特征以预测非小细胞肺癌的诊断和预后。此外, 对代谢环境的评估可以进一步发现其他潜在的生物标志物，因为它与 微生物和宿主的新陈代谢。因此，这项建议的目标是利用我们的NSCLC档案来评估 微生物元基因组和宿主转录特征与使用血液的代谢组学方法配对 样本开发预测早期非小细胞肺癌疾病的新生物标记物特征(目标1)。到时候我们会的 评估患者下呼吸道样本的元基因组、代谢组和宿主转录组数据 早期非小细胞肺癌以确定预测肺癌复发的特征(目标2)。最后，使用一个 综合多组学方法，我们将优化目标3中表现最好的特征的选择。队列 被选中进行这些调查的人将分为发现和验证两部分。成功的生物标记物将 接受外部验证。这里生成的数据将作为不可知论方法的基础 确定具有高度预测性的生物标志物，这些标志物将为以下目标方法的开发和验证提供支持 生物标志物参考实验室(BRL)。

Project summary (BDL) Recently, the use of culture independent techniques to characterize the microbiome has led to identification of microbial signatures in the systemic circulation associated with lung cancer diagnosis and prognosis. Consultants in this proposal have described how shotgun metagenomics, which identifies microbial DNA, can be used to identify microbial signatures in plasma predictive of different cancers, including lung cancer. Our preliminary data from our NYU EDRN archives show that metagenomic signatures can be predictive of early- stage non-small cell lung cancer (NSCLC) compared to non-NSCLC. In this cohort, we have also identified microbial and host transcriptomic signatures present in the lower airways associated with prognosis (recurrence). These data support that microbial and host genomic signatures could be used to develop novel biomarkers in early stages of this disease. Omic approaches can explore these signatures in an unbiased fashion, allowing for identification of best performing features for predicting, in this case, NSCLC diagnosis and prognosis. In addition, evaluation of the metabolomic environment can further uncover other potential biomarkers as it relates to the metabolism of microbes and host. Therefore, the goal of this proposal is to utilize our NSCLC archives to evaluate microbial metagenomic and host transcriptomic features paired with metabolomic approaches using blood samples to develop novel biomarker signatures that predict early-stage NSCLC disease (Aim 1). We will then evaluate the metagenome, metabolome and host transcriptomic data from lower airway samples from patients with early-stage NSCLC to identify features predictive of lung cancer recurrence (Aim 2). Finally, using an integrated multi-omic approach, we will optimize the selection of best performing features in Aim 3. The cohort selected for these investigations will be divided in Discovery and Validation. Successful biomarkers will then undergo external validation. The data generated here will serve as the foundation of an agnostic approach to identify highly predictive biomarkers that will feed the development and validation for targeted approaches under the Biomarkers Reference Laboratory (BRL).