# Biomarker Development Laboratory

> **NIH NIH U2C** · NEW YORK UNIVERSITY SCHOOL OF MEDICINE · 2024 · $495,245

## Abstract

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).

## Key facts

- **NIH application ID:** 10837150
- **Project number:** 5U2CCA271890-02
- **Recipient organization:** NEW YORK UNIVERSITY SCHOOL OF MEDICINE
- **Principal Investigator:** Leopoldo Nicolas Segal
- **Activity code:** U2C (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $495,245
- **Award type:** 5
- **Project period:** 2023-05-04 → 2028-04-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10837150

## Citation

> US National Institutes of Health, RePORTER application 10837150, Biomarker Development Laboratory (5U2CCA271890-02). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10837150. Licensed CC0.

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