Biomarker Development Laboratory

生物标志物开发实验室

基本信息

批准号：
10701256
负责人：
Leopoldo Nicolas Segal
金额：
$ 50.52万
依托单位：
NEW YORK UNIVERSITY SCHOOL OF MEDICINE
依托单位国家：
美国
项目类别：
财政年份：
2023
资助国家：
美国
起止时间：
2023-05-04 至 2028-04-30
项目状态：
未结题

来源：
https://reporter.nih.gov/project-details/10701256
关键词：
Archives Biological Markers Blood Tests Blood specimen Cancer Prognosis Cells Circulation Clinical Collaborations Complex Credentialing Culture-independent methods DNA DNA Markers Data Detection Development Diagnosis Diagnostic Procedure Disease Early Detection Research Network Early Diagnosis Environment Evaluation Excision Foundations Genomics Goals Grant Histologic Imaging Techniques In Situ Individual Investigation Laboratories Lesion Lung Adenocarcinoma Lung Neoplasms Lung diseases Lung nodule Malignant - descriptor Malignant Neoplasms Malignant neoplasm of lung Metabolic Metabolism Metagenomics Microbe Modeling Multiomic Data Nodule Non-Malignant Non-Small-Cell Lung Carcinoma Operative Surgical Procedures Oral Pathway interactions Patients Performance Perioperative Plasma Process Prognosis Publishing Recurrence Recurrent Malignant Neoplasm Recurrent disease Risk Sampling Sensitivity and Specificity Shotguns Statistical Models Systems Biology TNM Testing Thoracotomy Time Tissue Sample Tissues Transcript Validation artificial intelligence method biobank biomarker development biomarker signature cancer diagnosis cancer recurrence cancer type cohort diagnostic biomarker diagnostic strategy early detection biomarkers follow-up genomic signature high risk host-microbe interactions immunoregulation improved laboratory development lung cancer screening metabolome metabolomics metagenome microbial microbial signature microbiome minimally invasive multiple omics nano-string novel novel marker potential biomarker predictive marker predictive signature prevent specific biomarkers transcriptome transcriptome sequencing transcriptomics

项目摘要

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).

项目概要（BDL）最近，使用独立于培养的技术来表征微生物组已导致鉴定与肺癌诊断和预后相关的体循环中的微生物特征。该提案中的顾问描述了如何识别微生物 DNA 的鸟枪法宏基因组学可用于识别血浆中预测不同癌症（包括肺癌）的微生物特征。我们的来自纽约大学 EDRN 档案的初步数据表明，宏基因组特征可以预测早期- 与非 NSCLC 相比，非小细胞肺癌 (NSCLC) 分期。在这个队列中，我们还发现下呼吸道中存在的微生物和宿主转录组特征与预后（复发）相关。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志物这种疾病的早期阶段。 Omic 方法可以以公正的方式探索这些特征，从而允许识别用于预测（在本例中）NSCLC 诊断和预后的最佳表现特征。此外，代谢组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，该提案的目标是利用我们的 NSCLC 档案来评估微生物宏基因组和宿主转录组特征与使用血液的代谢组学方法相结合样本来开发预测早期 NSCLC 疾病的新型生物标志物特征（目标 1）。我们随后将评估患者下呼吸道样本的宏基因组、代谢组和宿主转录组数据与早期 NSCLC 一起识别肺癌复发的预测特征（目标 2）。最后，使用一个综合多组学方法，我们将优化目标 3 中表现最佳特征的选择。选择用于这些调查的将分为发现和验证。成功的生物标志物将接受外部验证。这里生成的数据将作为不可知论方法的基础确定高度预测的生物标志物，这些生物标志物将有助于开发和验证目标方法生物标志物参考实验室 (BRL)。