# The Pulmonary Pre-malignancy Atlas in the Lung Adenocarcinoma Spectrum

> **NIH VA I01** · VA GREATER LOS ANGELES HEALTHCARE SYSTEM · 2023 · —

## Abstract

Lung cancer is the leading cause of cancer death among US Veterans despite recent therapeutic advances. The
National Lung Screening Trial (NLST) has provided compelling evidence of the efficacy lung cancer screening,
using low-dose computed tomography (LDCT), to reduce lung cancer mortality. The benefits of screening,
however, must be reconciled with potential harms, including high false-positive rates and the possibility of
overdiagnosis. Recent studies demonstrate that lung cancer often exhibits significant molecular heterogeneity
because of genomic mutations, facilitating the initiation and expansion of diverse cell populations within the
tumor, promoting resistance to targeted and immune therapies, and impacting patient survival. It is critical to
characterize and predict tumor behavior in order to develop appropriate clinical management of early-stage lung
cancer. We have profiled lung adenocarcinoma (LUAD) in the NLST by whole-exome sequencing (WES) and
multiplex immunofluorescence (MIF). Available data includes clinical and nodule information, LDCT images
acquired at three time points, and long-term clinical follow-up, which allows us to assess aggressive versus
indolent behavior. We hypothesize that somatic mutations impact immune responses, shaping the tumor
microenvironment (TME) and morphology, can be described by quantitative features extracted from LDCTs. Our
goal is to develop a systematic approach integrating molecular signature and clinical imaging profiles to
distinguish between aggressive and indolent early-stage lung cancer by utilizing data collected in the NLST study
and validating findings through the analysis of publicly available data sets. We plan the following specific aims:
1) To identify the somatic mutation profiles associated with aggressive tumor behavior. In addition to analyzing
the NLST data, we will extend our analysis to publicly available data sets, including The Cancer Genome Atlas
(TCGA) and the Stanford Non-Small Cell Lung Cancer Radiogenomic dataset. From these two data sets, we will
select subjects with equivalent clinical features to the NLST cohort. 2) To identify tumor microenvironment
alteration characteristics associated with aggressive behavior in the NLST cohort. We will cross-validate our
TME findings with the signatures derived from transcriptome data of publicly available data sets, as noted in Aim
1. We will deconvolute the transcriptome data to estimate the composition of cell types in TME by utilizing the
cell lineage markers, which we have identified from single-cell transcriptome data of lung cancer. 3) To integrate
the LDCT features and molecular profiles that differentiated aggressive from indolent behaviors in the NLST
cohort. We will leverage machine learning techniques to extract and combine features from LDCT with genetic
and TME signatures, as identified in Aim 1 and 2. Predicting disease aggressiveness will improve personalized
patient therapy for early-stage lung cancer.

## Key facts

- **NIH application ID:** 10480632
- **Project number:** 1I01BX005721-01A1
- **Recipient organization:** VA GREATER LOS ANGELES HEALTHCARE SYSTEM
- **Principal Investigator:** Linh M Tran
- **Activity code:** I01 (R01, R21, SBIR, etc.)
- **Funding institute:** VA
- **Fiscal year:** 2023
- **Award amount:** —
- **Award type:** 1
- **Project period:** 2022-10-01 → 2026-09-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10480632

## Citation

> US National Institutes of Health, RePORTER application 10480632, The Pulmonary Pre-malignancy Atlas in the Lung Adenocarcinoma Spectrum (1I01BX005721-01A1). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10480632. Licensed CC0.

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