# Early detection of metastatic disease in US Veterans following surgery for early stage lung cancer

> **NIH VA I01** · VA GREATER LOS ANGELES HEALTHCARE SYSTEM · 2020 · —

## Abstract

Early detection of metastatic disease in US Veterans following surgery for early stage lung cancer
Surgery remains the most effective treatment for early stage non-small-cell lung cancer (NSCLC). This
therapeutic mainstay, however, is plagued by the development of post-surgical recurrence which most often
presents as metastatic disease. No significant improvements have been developed in the past 15 years and,
following surgery, the overall 5-year survival rate remains below 50%. New effective strategies that overcome
the major obstacles related to metastases will improve lung cancer cure rates and make a major impact in VA
patient care. Detection of NSCLC at an early stage results in favorable survival outcomes. In Aim 1 we will
identify molecular and cellular profiles that predict aggressive disease resulting in lung cancer metastasis
following surgery by conducting the following subaims: A) Identify and characterize somatic mutations that differ
between aggressive versus indolent lung cancers using whole exome sequencing, B) Develop tumor gene
expression signatures associated with important driver mutations and determine whether gene expression
signatures group tumors with biologically related mutations and thereby serve as markers for distinguishing
between aggressive and indolent lung cancers and C) Characterize the immune cell phenotypes found within
the tumor microenvironment that distinguish aggressive and indolent lung cancers. In Aim 2 we will develop a
novel highly sensitive approach for Minimum Residue Disease (MRD) and cancer recurrence detection by
conducting the follow subaims: A) Identify truncal mutations from pre-surgery plasma circulating tumor DNA
(cfDNA) samples, B) Detect MRD/recurrence using post-surgery plasma cfDNA samples and C) Assess the
statistical significance of the MRD predictive score in the determination of recurrence. In this VA Merit Review
Supplement proposal we will conduct a pilot study that begins to develop the requisite platforms that facilitate
our understanding, prediction, and detection of NSCLC metastases in US Veterans.

## Key facts

- **NIH application ID:** 9842307
- **Project number:** 1I01CX002005-01
- **Recipient organization:** VA GREATER LOS ANGELES HEALTHCARE SYSTEM
- **Principal Investigator:** Steven M. Dubinett
- **Activity code:** I01 (R01, R21, SBIR, etc.)
- **Funding institute:** VA
- **Fiscal year:** 2020
- **Award amount:** —
- **Award type:** 1
- **Project period:** 2020-04-01 → 2023-03-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/9842307

## Citation

> US National Institutes of Health, RePORTER application 9842307, Early detection of metastatic disease in US Veterans following surgery for early stage lung cancer (1I01CX002005-01). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/9842307. Licensed CC0.

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