Biomarker Development Laboratory

生物标志物开发实验室

• 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
• 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

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).

项目摘要（BDL） 最近，使用独立技术来表征微生物组，导致了鉴定 与肺癌诊断和预后相关的全身循环中的微生物特征。 该提案中的顾问描述了识别微生物DNA的shot弹枪宏基因组学如何可以 用于鉴定血浆中的微生物特征，可预测包括肺癌在内的不同癌症。我们的 我们的NYU EDRN档案中的初步数据表明，宏基因组特征可以预测早期 与非NSCLC相比，期非小细胞肺癌（NSCLC）。在这个队列中，我们还确定了 与预后相关的较低气道中存在的微生物和宿主转录组特征（复发）。 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签名，从而 识别最佳性能特征，以预测NSCLC诊断和预后。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 微生物和宿主的代谢。因此，该提案的目的是利用我们的NSCLC档案来评估 微生物元基因组和宿主转录组特征与代谢组方法使用血液配对 样品开发出可预测早期NSCLC疾病的新型生物标志物特征（AIM 1）。然后我们会 评估来自患者下气道样品的元基因组，代谢组和宿主转录组数据 使用早期NSCLC来识别预测肺癌复发的特征（AIM 2）。最后，使用一个 集成的多摩尼克方法，我们将优化AIM 3中最佳性能功能的选择。 为这些调查选择将在发现和验证中划分。那么成功的生物标志物将 接受外部验证。此处生成的数据将作为不可知论方法的基础 确定高度预测性的生物标志物，可以为有针对性方法提供的开发和验证 生物标志物参考实验室（BRL）。